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Citation: Alkanhel, R.; Abdallah,

H.A. Securing Color Video When

Transmitting through

Communication Channels Using

DT-CWT-Based Watermarking.

Electronics 2022, 11, 1849. https://

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electronics

Article

Securing Color Video When Transmitting through CommunicationChannels Using DT-CWT-Based WatermarkingReem Alkanhel and Hanaa A. Abdallah *

Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bintAbdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia; [email protected]* Correspondence: [email protected]

Abstract: In this paper, a color video watermarking system based on SVD in the complex waveletdomain is proposed. The process of inserting copyright information in video bit streams is known asvideo watermarking. It has been advocated in recent years as a solution to the problem of unlawfuldigital video alteration and dissemination. An effective, robust, and invisible video watermarkingalgorithm is proposed in this paper. The two-level dual tree complex wavelet transform (DT-CWT)and singular value decomposition are used to create this approach, which was built on a cascade oftwo powerful mathematical transforms. This hybrid technique demonstrates a high level of securityas well as various levels of attack robustness. The proposed algorithm was used to the test forimperceptibility and robustness, and this resulted in excellent grades. We compared our suggestedmethod to a DWT-SVD-based technique and found it to be far more reliable and effective.

Keywords: color video; watermarking; protection; dual tree complex wavelet transform; imperceptible;singular value decomposition; robustness

1. Introduction

Information sharing is now commonplace among institutions and individuals allaround the world. The introduction of 3G and 4G networks for high-speed communication,as well as the advent of social media, such as Facebook, Twitter, and the WhatsAppapplication on mobile phones, have enabled people to share information 24 h a day, sevendays a week. The problem of data altering and subsequently republishing it under adifferent name has arisen as a result of sharing information with others. These issuesprompted the researchers to create an algorithm that would put an end to the practice byembedding security into digital content. This effort resulted in digital watermarking [1].

A digital watermark is essentially a piece of ownership information inserted in digitaldata to protect it from unauthorized access. Watermarking for digital video is simply a moreadvanced variant of watermarking for digital images. Frames are a series of still imagesthat make up digital video. The payload is the quantity of information that is embedded.

Video watermarking, unlike image watermarking, also addresses the issue of bigvolume. The watermark-embedding algorithm tucks the watermark into the host media(image/video) or the altered version of the host media. The frames of the movie areconverted to frequency domain utilizing the frequency domain conversion methodologyin the transform domain watermarking approach, and the watermark is then placed intransfer domain media.

Illegal digital movie distribution is a regular and serious danger to the film business.A pirated duplicate of a digital video can now be easily transmitted to a global audiencedue to the arrival of high-speed broadband internet access. Digital video watermarking, inwhich additional information, known as a watermark, is inserted in the host video, maybeone way to limit this type of digital theft. This watermark can be retrieved from the videocontent at the decoder and used to determine whether it is watermarked. [2]

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Electronics 2022, 11, 1849 2 of 25

SVD is popular due to its ease of implementation and appealing mathematical prop-erties. The use of SVD for watermarking has gained favor. In image processing, patternrecognition, and information security, the SVD approach has received a great deal of atten-tion. One method of watermarking with SVD is to apply it to the entire cover image andalter all of the SVs to incorporate the watermark data. For most sorts of attacks, the singularvalues (SVs) fluctuate relatively little, which is a key feature of SVD watermarking.

The piracy of a digital movie is a serious problem for movie studios and producersbut can be addressed by digital video watermarking. Robustness to several attacks onthe watermark has increased with existing watermarking methods. However, none ofthe available solutions is resistant to a combination of video compression and histogramequalization. The watermark is contained in the singular values of the dual-tree complexwavelet transform coefficients of the luminance channel in this study, which is a blind videowatermarking approach.

The original video quality is preserved since distortion in the luminance channel is lesssensitive to the human eye. Due to the strong stability of its singular values, the singularvalue decomposition is applied, and the approximate shift invariance characteristic of thedual-tree complex wavelet transform ensures robustness against attacks. Noise addition,compression and histogram equalization are all supported by the proposed approach. Ourproposed technique using DT-CWT and SVD is compared with the existence DWT-SVDwatermarking method.

The following is the paper’s structure. Section 2 introduces a background of SingularValue Decomposition (SVD) and the CWT Transform with Dual Trees (DT-CWT), which isused in watermarking. Section 3 reviews the related literature. Section 4 proposes the novelrobust CWT-based SVD watermarking approach. The experimental results are presented inSection 5. Finally, Section 6 introduces our conclusions.

2. Background Review2.1. Singular Value Decomposition (SVD)

An image is a matrix with non-negative elements, as defined in algebra. If A is asquare image, indicated by A ∈ R n × n, and R is the real number domain, then A’s SVD isdefined as [2]:

As found in algebra, an image is a matrix of non-negative entries. If A is a squareimage, denoted as A ∈ R n × n, where R represents the real number domain, then the SVDof A is defined as [2]:

A = USVT (1)

where U and V are orthogonal matrices such that UT U = I, VT V = I, I is an identity matrix.S = diag

(σ1, . . . . . . , σp

), where p = min (m, n), σ1 ≥ σ2 ≥ . . . . . . ≥ σp ≥ 0, are the singular

values of A. This decomposition is known as the singular value decomposition (SVD) of Aand can be written as follows [2]:

SVD(A) = [USV] (2)

orA = USVT (3)

The stability of SVs states that when there is a minor disruption with A, the deviationof SVs is not greater than the matrix’s greatest SV [3]. SVD has advantages when applied todigital images [3–5].

- When a small perturbation occurs in an image, the SVs of the image have good stability.- The SVs denote the image’s algebraic qualities, which are not visible.

2.2. The CWT Transform with Dual Trees

In 1998, the dual-tree CWT was originally introduced using Kingsbury [6]. The notionbehind the twin-tree technique is relatively straightforward, similar to the idea of fine/poor

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publish-filtering of real subband indicators. Real DWTs are used in the twin tree CWT;the first DWT provides the actual component of the renovation, while the second DWTprovides the imaginary part. Figures 1 and 2 show the evaluation and synthesis Feedbacks(FBs) used to implement the dual-tree CWT and its inverse (2). Each of the two real waveletstransforms uses a different set of filters to satisfy the PR criteria. The two sets of filters arecombined in such a way that the overall rework is nearly analytic. The two sets of filtersare collectively designed in order that the overall rework is approximately analytic.

Figure 1. Dual-tree discrete CWT decomposition.

Figure 2. Synthesis for dual-tree CWT.

Permit h0(n) and h1(n) to denote the upper fb’s low-bypass/high-pass filter out pair,and permit g0(n) and g1(n) to denote the lower fb’s low-skip/excessive-bypass filter outpair as ψh(t) and ψg(t) are the two real wavelets associated with each of the two real wavelettransforms (t). The filters are built so that the complex wavelet ψ(t) = ψh(t) + jψg(t) is nearlyanalytic, in addition to pleasing PR scenarios. They are built in such a way that ψg(t) isabout the Hilbert remodel of of ψh(t) denoted as ψg(t) ≈ Hψh(t).

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It is worth noting that the filters are real; there is no need for complicated arithmetic tocreate the twin-tree CWT. The real and imaginary elements are both inverted to invert therework. To obtain genuine signals, the inverse of each of the two actual DWTs is employed.The final output is calculated by averaging the real alerts. Although the original signal x(n)may be retrieved by me from both the actual and imaginary parts, such inverse DT-CWTsdo not capture all of the advantages of an analytic wavelet transform. If the square matricesFh and Fg are used to represent the two actual DWTs, then the square matrix can also beused to represent the twin-tree CWT.

The DT-CWT is also simple to set up. As there is unlikely to be any data drift be-tween the two actual DWTs, they might be used with current DWT software or hardware.Furthermore, for green hardware implementation, the rework is naturally parallelized.Furthermore, because the DT-CWT is implemented using two real wavelet transforms,the application of the DT-CWT can be guided by the current concept and practice of realwavelet transforms. Wavelet-based sign processing methods, such as wavelet coefficientthresholding established for actual wavelet transforms can also be used to the DT-CWT [6].

The DT-CWT, on the other hand, necessitates the configuration of modern filters. Itusually necessitates a pair of filter out units chosen in such a way that the correspondingwavelets form a Hilbert rework pair. To enforce each tree of the DT-CWT, current wavelettransform filters are no longer required. Pairs of Daubechies’ wavelet filters, for example,do not satisfy the requirement that ψg(t) ≈ H ψh(t). If the DT-CWT is used with filters thatdo not meet this condition, the rework will no longer provide the overall benefits of thepreviously established analytic wavelets.

Wavelets cannot be used in other areas of image processing because of these issues.The lack of shift invariance problem can be avoided by not down sampling the filter outputsfrom each degree; however, this will significantly increase the computational costs, and theresulting decimated wavelet transform will still be unable to distinguish between opposingdiagonals because the rework remains separable. To distinguish opposing diagonals withseparable filters, the clear out frequency responses for beneficial and dreadful frequenciesmust be unequal.

Complex wavelet filters, which can be made to reduce negative frequency additions,are a good way to achieve this. As can be seen, the CWT outperforms the separable DWTin terms of shift-invariance and directional selectivity. As in the basic DWT, two treesare applied to the rows and then the columns of the picture to compute the 2-D CWT ofimages (Figure 3).

Figure 3. Complex filter responses.

The CWT decomposes an image into six directed subimages, each level resulting fromuniformly spaced directional filtering and subsampling.

There are four main flaws in the wavelet transform. The first problem is oscillations,which indicates that the wavelet coefficients tend to fluctuate positive and negative aroundsingularities. The solution is given in this section. Wavelet-based processing is morecomplicated as a result, making singularity extraction and signal modeling, in particular,extremely difficult [6]. A wavelet overlapping a singularity could have a small or even zerowavelet coefficient. The second issue is shift variance, which causes the wavelet coefficientoscillation pattern around singularities to be significantly perturbed by a tiny shift insign. Wavelet-area processing is also complicated by shift variance. Recall a piecewise

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smooth signal x (t − t0) similar to the step function to better understand wavelet coefficientoscillations and shift variance [6],

u(t) =

{0 t < 01 t ≥ 0

(4)

The data was analyzed using a wavelet basis with a sufficient number of vanishingmoments. Its wavelet coefficients include samples from the wavelet’s step reaction [6].

d(j, n) ≈ 2−3j

2 ∆

2jt0−n∫−∞

ψ(t)dt (5)

where ∆ is the jump’s height. As ψ(t) is a zero-oscillating bandpass function, its stepresponse d (j, n) is a function of n. Furthermore, the factor 2 j in the upper limit (j ≥ 0)increases the sensitivity of d (j, n) to the time shift t0, resulting in high shift variance. Thethird issue is aliasing, which is caused by the wavelet coefficients being computed usingiterated discrete-time down-sampling processes intermingled with non-ideal low-pass andhigh-pass filters. Of course, the IDWT eliminates aliasing, however, only if the wavelet andscaling coefficients remain unchanged. Lack of directionality is the fourth issue due to thislack of directional selectivity, modeling and processing of geometric visual elements, suchas ridges and edges.

As a Solution, Complex Wavelets Are Used

There is a straightforward solution to these four DWT flaws. The key is to noticethat these issues do not affect the Fourier rework. To begin with, the Fourier transform’ssignificance no longer oscillates unmistakably and adversely but instead provides a clearpositive envelope within the Fourier domain. Second, the Fourier remodel is perfectlytransfer-invariant, encoding the shift with a simple linear section offset. Third, the Fouriercoefficients are not aliased, and the signal reconstruction does not rely on sophisticatedaliasing cancellation properties.

The sinusoids of the multi-Dimensional Fourier basis become directional plane wavesin the end. We provide a CWT-based completely SVD watermarking approach for greyand color photos in this research. The excessive-frequency band SVs are used to achievethe watermark embedding approach. The LSB and additive approaches are used to insertwatermarks in this paper.

3. Related Work

Several publications on the usage of the DWT with SVD for information hiding indigital photographs have been published in the literature. In [7], DWT is used to split apicture into frequency subbands, and one of the bands is adjusted for data embeddingby dividing it into 44-block blocks. Every block has SVD applied to it, and a watermarkis embedded in the diagonal of each block. When a watermarked image is attacked, thenormalized correlation coefficient NC is utilized to determine the degree of similaritybetween the original watermark and the extracted watermark. When DWT and SVD arecoupled, the watermarking method exceeds the traditional DWT method in terms of attackresistance. In [8], the method presented works well for image processing processes, usingthe DWT and SVD methods to hide data in the image’s high frequency range.

The authors in [9] provided a lossless video watermarking strategy based on optimalkey frame selection employing a linear wavelet transform and an intelligent gravitationalsearch algorithm. The histogram difference approach is used to extract color motionand motionless frames from the cover video. On the chrominance channel of motionframes, a one-level linear wavelet transform is applied, and a low-frequency sub-bandLL is used for watermark embedding. In terms of imperceptibility and robustness, thesuggested approach was tested against 12 video processing assaults. Experiments show

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that the suggested method outperformed five state-of-the-art techniques on the assaultsunder consideration.

In terms of intricacy, embedding the watermark in all frames of the cover video is nota smart approach. A study [10] offered a solution for not having the watermark appearin all frames of the cover video. Due to the widespread illicit copying of digital media,such as images, videos, and audio, for the aim of copyright protection, the watermarkingmethod is an important strategy. This work proposed a SCD approach for embedding awatermark in selected cover frames. The approach is resistant against numerous attacksdue to the use of the wavelet transform technology and singular value decomposition. Thealgorithm’s performance measurement demonstrates its resistance to both intentional andunintended attacks.

An efficient compression-based safe digital watermarking was proposed in [11]. Thevideo is first transformed into a number of frames in this document. The frame is thendecomposed using a dual-tree complex wavelet transform. The embedding places arethen optimally picked using the adaptive cuttlefish optimization technique to boost thesystem’s security. Then, using the elliptic curve cryptography method, the secret photosare encrypted. The encrypted images are then transformed to binary bits. The binarybits are then embedded in the video frame’s designated place. The H.265 video encodingstrategy is used after the encryption procedure to reduce the size of the encrypted document.This method efficiently reduces the image size without compromising the image’s quality.Finally, the image is compressed.

An article [12] explains the principles of the suggested method and provides a strategyto cope with the most common sorts of assaults in video watermarking by thoroughlystudying the types of attacks. As a result, by offering a three-dimensional discrete cosinetransform technique, it offers a secure solution against collusion attempts. According to thetests, the proposed approach is immune to collusion attacks, which are the most commontype of digital watermarking attack. The suggested technique also has a larger capabilityfor watermarking than other methods. The suggested algorithm is capable of covering alltypes of video, both dynamic and static, due to its unique blocking mechanism.

A paper [13] explained the principles of the suggested method and provided a strategyto cope with the most common sorts of assaults in video watermarking by thoroughlystudying the types of attacks. As a result, by offering a three-dimensional discrete cosinetransform technique, it offers a secure solution against collusion attempts. According to thetests, the proposed approach is immune to collusion attacks, which are the most commontype of digital watermarking attack. The suggested technique also has a larger capabilityfor watermarking than other methods. The suggested algorithm is capable of covering alltypes of video, both dynamic and static, due to its unique blocking mechanism.

Based on 2-D DFT, this work [14] provided a geometrically robust multi-bit videowatermarking technique (two-dimensional discrete Fourier transform). While most knownvideo watermarking systems require synchronization to extract the watermark from rotatedor scaled videos, which takes time and reduces accuracy, the suggested method can extractthe watermark directly from rotated, scaled, or cropped videos without synchronization.Circular templates in the DFT domain are translated into spatial masks and appended tothe video frames in the spatial domain to insert the watermark. To maintain the accuracyof the watermarked video, a perceptual model based on local contrast is used. We alsopresent an accurate and efficient extraction approach based on the Wiener-filtered DFTmagnitude cross-correlation with the stretched DFT magnitude.

Based on the integer wavelet transform and the generalized chaotic sine map, the workin [15] presents a strong blind and secure video watermarking method. Each main frameof the standard video is subjected to an integer wavelet transform in this method. Thethree principles of content quality, data resilience, and data capacity are used to evaluatewatermarking strategies. As a result, the watermark is placed in low-frequency coefficientsto ensure the quality of the watermarked video. By using a chaotic map as a watermark,an adequate security level is added to improve the efficiency and usefulness. In addition,

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the key criterion of resistance measurement is the normalized correlation coefficient (NCC)between the main and extraction watermarks. The results reveal that the proposed strategyis effective in a variety of situations.

A color video watermarking algorithm based on the hyperchaotic Lorentz system waspresented in [16]. To begin, the color watermark images are scrambled using a hyperchaoticLorentz system to increase their privacy. Second, we employ shot boundary detection toextract the video’s non-motion frames. The chaotic sequence is then utilized to determinewhich non-motion frames are specific. The discrete wavelet transform is then applied toparticular frames to obtain the required subbands. Finally, the encrypted watermarks areplaced into the subbands that have been chosen. The peak signal-to-noise ratio, normalizedcorrelation, and structural similarity index measure are used to evaluate the suggestedmethod’s performance. Experiments reveal that the average PSNR and SSIM of water-marked frames are 57.00 dB and 0.99, respectively; indicating that the suggested approachhas a good level of effectiveness.

The authors in [17] used a two-level discrete wavelet transform (DWT) and a skilledsingular value decomposition technique (SVD). For non-blind and blind watermarkingtechniques, performance characteristics, such as processing time, peak signal-to-noise ratio(PSNR), and normalized correlation (NC) are compared. When subjected to tough noiseattacks, such as geometrical, filtering, salt and pepper noises, the PSNR and NC valuesare achieved as an average value of 40 dB and 0.99, respectively. For difficult attacks,such as salt and pepper, Gaussian, and Gamma sounds, the range conversion methodenhances PSNR.

Based on the undecimated discrete wavelet transform, this paper [18] provides animproved video watermarking approach (UDWT). The cover video’s frames are brokeninto 808 chunks. To insert the watermark bit, two AC coefficients are chosen in each 8-bitblock. The technique is applied to four UDWT bands, and the redundancy in this transformenabled for a large-capacity video watermarking. The watermarking approach is renderedignorant due to the masking qualities of the UDWT human visual system. The experimentalresults show that the proposed video watermarking system meets all four watermarkingrequirements: security, obliviousness, robustness, and capacity.

4. Discussion

The suggested DT-CWT-based SVD video watermarking methods include two pro-cedures: one embeds the watermark frame per frame, while the other extracts it fromthe watermarked version of the video clip. For each frame of the video, the embeddingtechnique is shown in the block diagram shown in Figure 4. Figure 5 shows the two water-marks. Figure 6a–d shows the different videos that we used in this paper, and c and d showthe two scene images of one video, whereas whereas Figure 5 shows the two watermarks.

4.1. CWT-Based SVD Watermarking Is a Suggested Watermarking Method

The proposed strategy is implemented by modifying the SVs of frames’ high frequencysubbands using either the LSB or the addition methods.

4.1.1. CWT Using the LSB MethodEmbedding a Watermark

(1) The watermark is subjected to a one-level complex wavelet transform.(2) All high-pass bands are then subjected to the SVD transform.(3) Use the DT-CWT and SVD to process the original frame.(4) For the entire frame, the one-level CWT is computed. Six high-frequency sub-bands

are created using this procedure.(5) Application of SVD to the high-frequency subband

Aj = Uj × Sj ×VTj , j = 1, 2, 3, . . . , 6 (6)

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Figure 4. DT-CWT watermark embedding procedure in video frames.

Figure 5. The two different watermarks embedded in the scenes of the video clip.

Electronics 2022, 11, x FOR PEER REVIEW 9 of 26

(a) (b)

(c) (d)

Figure 6. (a) Akiyo. (b) Foreman. (c,d) Mobile. (c,d) Snapshots of the two scenes of the video clip.

4.1. CWT-Based SVD Watermarking Is a Suggested Watermarking Method The proposed strategy is implemented by modifying the SVs of frames’ high fre-

quency subbands using either the LSB or the addition methods.

4.1.1. CWT Using the LSB Method

Embedding a Watermark (1) The watermark is subjected to a one-level complex wavelet transform. (2) All high-pass bands are then subjected to the SVD transform. (3) Use the DT-CWT and SVD to process the original frame. (4) For the entire frame, the one-level CWT is computed. Six high-frequency sub-bands

are created using this procedure. (5) Application of SVD to the high-frequency subband 𝐴 = 𝑈 × 𝑆 × 𝑉 , j =1,2,3,….,6 (6)

The high-pass subbands of the one-level decomposition are denoted by j, and the associated SVs matrix is denoted by Sj. (6) Using the LSB technique, replace the SVs of the high-pass sub band with the SVs of

the watermark. (7) After that, embed the values of the SVs of the CWT coefficients of the watermark into

the LSB of the SVs of the original frame. As the size of subbands of cover frame is greater than the subbands of watermark to

hide all S values of watermark in lsb of singular values of cover frame. (8) Obtain the new DT-CWT coefficients of six subbands: 𝐴 = 𝑈 × 𝑆 × 𝑉 , j =1,2,3,….,6 (7)

(9) Finally, using the modified CWT coefficients, apply the reverse CWT. The last water-marked frame is created during this activity.

(10) Then, we generate the watermarked video as shown in Figure 7.

Figure 6. (a) Akiyo. (b) Foreman. (c,d) Mobile. (c,d) Snapshots of the two scenes of the video clip.

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The high-pass subbands of the one-level decomposition are denoted by j, and theassociated SVs matrix is denoted by Sj.

(6) Using the LSB technique, replace the SVs of the high-pass sub band with the SVs ofthe watermark.

(7) After that, embed the values of the SVs of the CWT coefficients of the watermark intothe LSB of the SVs of the original frame.

As the size of subbands of cover frame is greater than the subbands of watermark tohide all S values of watermark in lsb of singular values of cover frame.

(8) Obtain the new DT-CWT coefficients of six subbands:

Aj = Uj × Sj ×VTj , j = 1, 2, 3, . . . , 6 (7)

(9) Finally, using the modified CWT coefficients, apply the reverse CWT. The last water-marked frame is created during this activity.

(10) Then, we generate the watermarked video as shown in Figure 7.

Figure 7. DT-CWT-based SVD watermark embedding procedure using the LSB method.

The Watermark Extraction

Without using the cover frame, the watermark is retrieved. The following are the stepsof the extraction procedure:

(1) Calculate the frame’s one-level DT-CWT. This results in six high-frequency subbands.(2) Every high-pass subband is subjected to SVD.

Aj = Uj × Sj ×VTj , j = 1, 2, 3, . . . , 6 (8)

(3) Every high frequency band has SVs taken from it. Take the LSB of each of the SVs ofthe watermarked image’s coefficients.

(4) Using the matrix of the watermarked image and the vectors obtained during the embed-ding process; construct the DT-CWT coefficients of the six high-frequency subbands.

Aw,j = Uw,j × Swj ×VT

j , j = 1, 2, 3, . . . , 6 (9)

(5) Finally, the watermarks are developed using reverse DT-CWT.

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4.1.2. Proposed Additive MethodThe Watermark’s Embedding

(1) The watermark is decomposed using one level of CWT.(2) Every high-pass band is subjected to SVD.(3) Use the DT-CWT and SVD to process the original image.(4) The two-level CWT is calculated. Six high-frequency sub-bands are created using

this procedure.(5) Decompose each high-frequency subband using the singular value decomposition:

Aj = Uj × Sj ×VTj , j = 1, 2, 3, . . . , 6 (10)

Sj signifies the SVs matrix, while as j denotes the high-pass subbands of the two-level decomposition.

As the size of subbands of cover frame is equal to the subband of the watermark toadd the singular values of watermark’s subband and cover‘s subbands.

(6) Using the additive method, combine the SVs of each high-frequency subband withthe SVs of the watermark.

Sj = S + k× Sw (11)

(7) Obtain the following six subbands:

Aj = Uj × Sj ×VTj , j = 1, 2, 3, . . . , 6 (12)

(8) Finally, using the altered DT-CWT coefficients, apply the inverse of DT-CWT; thisoperation provides the final watermarked frame.

(9) Then, we generate the watermarked video.

These steps are shown in Figure 8.

Figure 8. CWT-based SVD embedding method using additive method.

Extraction of the Watermark

The following is a description of the watermark extraction method:

(1) Calculate the frame’s two-level DT-CWT. This results in the creation of six high-frequency sub-bands.

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(2) For each high-frequency subband, use SVD.

Aj = Uj × Sj ×VTj , j = 1, 2, 3, . . . , 6 (13)

(3) Each high-frequency subband’s SVs should be extracted.

Swj =

(Sj − S

)/k (14)

(4) Using the SV framework of the watermarked frame and the vectors calculatedduring the insertion operation, calculate the DT-CWT coefficients of the six high-frequency subbands.

Aw,j = Uw,j × Swj ×VT

j , j = 1, 2, 3, . . . , 6 (15)

(5) Finally, use the inverse DT-CWT to create the visual watermarks.

5. Performance Evaluation

The performance of a digital watermark technique is primarily measured in terms ofimperceptibility and resilience. This includes both subjective and objective evaluations.The subjective rating is based on human perception.

Subjective evaluation has some practical utility for assessing the quality of watermark-ing systems, although subjective evaluation findings of watermarking schemes might varygreatly amongst people with different experiences. As a result, we use objective assess-ment criteria, such as the peak signal-to-noise ratio (PSNR), structural similarity indexes(SSIM), and normalized cross-correlation (NC) to assess the watermarking algorithm’simperceptibility and robustness.

5.1. Imperceptibility Assessment

The capacity of the host to hide the watermark information is known as imperceptibility.

5.1.1. Peak Signal-to-Noise Ratio

When comparing the mean square error between the original image and the water-marked image, the PSNR measure is utilized. This is related to imperceptibility. The higherthe PSNR, the better the watermarked image’s quality [19]. The two error-measuring meth-ods used to compare picture-watermarking quality are the mean square error (MSE) andthe peak signal-to-noise ratio (PSNR). The PSNR and the MSE have an inverse relationship.This measurement is denoted by Equation (16).

PSNR = 10 log10

(L2

1MN ∑M,N (Aw(i,j)−A(i,j))2

)= 10 log10

(L2

MSE

)(16)

where L is the maximum value possible in each pixel, A(i,j) is the original image, Aw(i,j)is the watermarked image, and M and N are the number of rows and columns in theoriginal images.

5.1.2. Correlation Coefficient

Between the original and extracted watermarks, this metric is employed. The valueof Cr ranges from −1 to 1, with the closer value to 1 indicating that the images are morecomparable. Mathematically, this measure is given as Equation (17)

Cr =∑M

∑N

A(i, j)AW(i, j)

∑M

∑N(A(i, j))2 (17)

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where A(i,j) and AW(i,j) are the original and extracted watermarks, respectively. The valuesof Cr of the system decide the robustness of the system is to the image processing operations.Higher Cr s that the system is highly robust to attacks.

5.1.3. Structural Similarity Index Measure

The structural similarity index measure (SSIM) is a method for comparing the originalimage to the watermarked image. The SSIM index can be thought of as a full referencemetric, which means it measures image quality using an uncompressed or distortion-freeimage as a baseline. SSIM is intended to improve on older technologies, such as (PSNR)and (MSE), which have been shown to be incompatible with human vision. The numericalvalue between −1 and 1 is taken by SSIM.

The instance of two identical sets of data is represented by value 1. Unlike the PSNRor MSE, the SSIM index evaluates perceived errors and analyzes picture deterioration asa perceived change in structural information. The notion behind structural informationis that pixels have substantial interdependencies when they are structurally close. Thesedependencies contain crucial information about the structure of the visual scene’s elements.The mean, variance, and covariance of values of the original and watermarked imagesinclude structural information. The following equation yields SSIM. (18):

SSIM =

[2µxµy + S1

][2σxy + S2

][µ2

x + µ2y + S1

][σ2

x + σ2y + S2

] (18)

where µx is the average of x, µy is the average of y, σxy is the covariance of x and y, σ2x is

the variance of x, σ2y is the variance of y, s1 = (k1D)2 and s2 = (k2D)2 are two variables to

stabillize the division with a weak denominator. D is the dynamic range of pixel values(typically this is 2#bits per pixel − 1). k1 = 0.01 and k2 = 0.03 by default.

5.2. Robustness

The capacity of a watermark technology to withstand diverse attacks is referred to asrobustness. The NC is used as the robustness evaluation approach in this case. This is howit is defined in (19):

NC =∑((Pi − Pa)(Qi −Qa))√

∑(Pi − Pa)2√

∑(Qi −Qa)2

(19)

where Pi represents the intensity of ith pixel in the image m and Pa is the mean intensity ofimage m. Qi represents the intensity of ith pixel in the image n, and Qa is the mean intensityof image n.

6. Simulation Results6.1. Results of Proposed DT-CWT-Based SVD Video Watermarking Using Additive Method

A total of 30 video frames were used to evaluate the proposed approach on videosequences. 30 frames have been chosen for watermarking in order to minimize memoryutilization and to reduce embedding delay. A watermark is embedded via the suggestedapproach in the first 15 frames, and another watermark is embedded in the other 15 frames.Figures 9 and 10 show how the correlation coefficient varies with the frame index. Theperformance of the suggested method is clearly superior to that of DWT-based SVD water-marking, as shown in Figure 9. We use a 30-frame colorful video clip from a mobile video,and each frame is 576 × 704 pixels in size. The video clip was divided into two scenes; wechose 15 frames from each scene to hide a watermark. Figures 9 and 10 show snapshotsof each scene. Using an additive approach, the watermark was then implanted in the Ycomponent of each frame of the two scenes. In each of the two scenes, there was a differentwatermark. The two watermark pictures are 288 × 352 pixels in size.

Electronics 2022, 11, 1849 13 of 25


a perceived change in structural information. The notion behind structural information is that pixels have substantial interdependencies when they are structurally close. These de-pendencies contain crucial information about the structure of the visual scene’s elements. The mean, variance, and covariance of values of the original and watermarked images include structural information .The following equation yields SSIM. (18): SSIM = 2μ μ + S 2σ + Sμ + μ + S σ + σ + S (18)

where μ is the average of x, μ is the average of y, σ is the covariance of x and y, σ is the variance of x, σ is the variance of y, s = (k D) and s = (k D) are two varia-bles to stabillize the division with a weak denominator. D is the dynamic range of pixel values (typically this is 2# −1). k = 0.01 and k = 0.03 by default.

5.2. Robustness The capacity of a watermark technology to withstand diverse attacks is referred to as

robustness. The NC is used as the robustness evaluation approach in this case. This is how it is defined in (19): 𝑁𝐶 = ∑((𝑃 − 𝑃 )(𝑄 − 𝑄 ))∑(𝑃 − 𝑃 ) ∑(𝑄 − 𝑄 ) (19)

where Pi represents the intensity of ith pixel in the image m and Pa is the mean intensity of image m. Qi represents the intensity of ith pixel in the image n, and Qa is the mean intensity of image n.

6. Simulation Results 6.1. Results of Proposed DT-CWT-Based SVD Video Watermarking Using Additive Method

A total of 30 video frames were used to evaluate the proposed approach on video sequences. 30 frames have been chosen for watermarking in order to minimize memory utilization and to reduce embedding delay. A watermark is embedded via the suggested approach in the first 15 frames, and another watermark is embedded in the other 15 frames. Figures 9 and 10 show how the correlation coefficient varies with the frame index. The performance of the suggested method is clearly superior to that of DWT-based SVD watermarking, as shown in Figure 9. We use a 30-frame colorful video clip from a mobile video, and each frame is 576 × 704 pixels in size. The video clip was divided into two scenes; we chose 15 frames from each scene to hide a watermark. Figures 9 and 10 show snapshots of each scene. Using an additive approach, the watermark was then implanted in the Y component of each frame of the two scenes. In each of the two scenes, there was a different watermark. The two watermark pictures are 288 × 352 pixels in size.

(a) (b) (c)


(d)

Figure 9. (a) The watermarked frame using the proposed method. (b) Extracted watermark from frame 10 of the first video scene in the proposed method. (c) Extracted watermark from frame 10 of the first video scene in the DWT-based SVD method. (d) Correlation values for the extracted water-marks for frames 1 to 15 in the first video scene without attacks.

(a) (b) (c)

(d)

Figure 10. (a) The watermarked frame using the proposed method. (b) Extracted watermark from frame 10 of the second video in the proposed method. (c) Extracted watermark from frame 10 of the second video scene in the DWT-based SVD method. (d) Correlation values due to detected water-marks from each frame in the second video scene without attack.

The two watermarked sceneries without attacks are shown in Figures 9 and 10. The suggested method’s resilience to conventional attacks, such as JPEG compression, Gauss-ian noise, and histogram equalization, is shown in Figures 11–16. The Gaussian noise at-tack is applied to the watermarked video frames with variance = 0.01. The correlation val-ues in Figures 11 and 12 show that the DWT-based SVD watermarking approach is more resilient to the insertion of Gaussian noise at some frames than the DT-CWT-based SVD watermarking method.

Figure 9. (a) The watermarked frame using the proposed method. (b) Extracted watermark fromframe 10 of the first video scene in the proposed method. (c) Extracted watermark from frame 10of the first video scene in the DWT-based SVD method. (d) Correlation values for the extractedwatermarks for frames 1 to 15 in the first video scene without attacks.


(d)

Figure 9. (a) The watermarked frame using the proposed method. (b) Extracted watermark from frame 10 of the first video scene in the proposed method. (c) Extracted watermark from frame 10 of the first video scene in the DWT-based SVD method. (d) Correlation values for the extracted water-marks for frames 1 to 15 in the first video scene without attacks.

(a) (b) (c)

(d)

Figure 10. (a) The watermarked frame using the proposed method. (b) Extracted watermark from frame 10 of the second video in the proposed method. (c) Extracted watermark from frame 10 of the second video scene in the DWT-based SVD method. (d) Correlation values due to detected water-marks from each frame in the second video scene without attack.

The two watermarked sceneries without attacks are shown in Figures 9 and 10. The suggested method’s resilience to conventional attacks, such as JPEG compression, Gauss-ian noise, and histogram equalization, is shown in Figures 11–16. The Gaussian noise at-tack is applied to the watermarked video frames with variance = 0.01. The correlation val-ues in Figures 11 and 12 show that the DWT-based SVD watermarking approach is more resilient to the insertion of Gaussian noise at some frames than the DT-CWT-based SVD watermarking method.

Figure 10. (a) The watermarked frame using the proposed method. (b) Extracted watermark fromframe 10 of the second video in the proposed method. (c) Extracted watermark from frame 10 ofthe second video scene in the DWT-based SVD method. (d) Correlation values due to detectedwatermarks from each frame in the second video scene without attack.

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The two watermarked sceneries without attacks are shown in Figures 9 and 10. Thesuggested method’s resilience to conventional attacks, such as JPEG compression, Gaussiannoise, and histogram equalization, is shown in Figures 11–16. The Gaussian noise attackis applied to the watermarked video frames with variance = 0.01. The correlation valuesin Figures 11 and 12 show that the DWT-based SVD watermarking approach is moreresilient to the insertion of Gaussian noise at some frames than the DT-CWT-based SVDwatermarking method.


(a) (b) (c)

(d)

Figure 11. (a) The watermarked frame under Gaussian noise attack using the proposed method. (b) Extracted watermark from frame 10 of the first video scene in the proposed method. (c) Extracted watermark from frame 10 of the first video scene in the DWT-based SVD method. (d) Correlation values for the extracted watermarks for frames 1 to 15 in the first video scene under Gaussian attack.

(a) (b) (c)

(d)

Figure 12. (a) The watermarked frame under Gaussian noise attack using the proposed method. (b) Extracted watermark from frame 10 of second video in the proposed method (c) Extracted water-mark from frame 10 of the second video scene in the DWT-based SVD method. (d) Correlation val-ues due to detected watermarks from each frame in the second video scene under Gaussian attack.

Figure 11. (a) The watermarked frame under Gaussian noise attack using the proposed method.(b) Extracted watermark from frame 10 of the first video scene in the proposed method. (c) Extractedwatermark from frame 10 of the first video scene in the DWT-based SVD method. (d) Correlationvalues for the extracted watermarks for frames 1 to 15 in the first video scene under Gaussian attack.

The watermarked video frames are compressed with a quality of 50% for videocompression. The correlation coefficients reveal that the proposed watermarking is morerobust to video compression for most frames than the DWT-based SVD watermarking, asillustrated in Figures 13 and 14.

Histogram Equalization is an image processing technique that uses a histogram toalter the contrast of an image. It spreads out the most frequent pixel intensity values orexpands out the image’s intensity range to improve contrast. Histogram equalizationachieves this by allowing the image’s low-contrast sections to obtain more contrast. Imagesthat appear washed out due to a lack of contrast, equalization can be applied. Histogramswill improve the contrast in an image, especially if the image has many low intensities.As illustrated in Figures 15 and 16, the correlation coefficients values clearly indicate thesuggested approach’s robustness when compared to the DWT-based SVD method.

6.2. The Effect of the Depth of the Watermark

Table 1 shows the effect of the depth of the watermark added to the video on the qualityof video the relation between the depth of the added watermark and the correlation betweenthe original watermark and the extracted watermark. We find that, as the depth increases,the quality of the recovered watermark is increases even under the effect of attacks.

Electronics 2022, 11, 1849 15 of 25


(a) (b) (c)

(d)

Figure 11. (a) The watermarked frame under Gaussian noise attack using the proposed method. (b) Extracted watermark from frame 10 of the first video scene in the proposed method. (c) Extracted watermark from frame 10 of the first video scene in the DWT-based SVD method. (d) Correlation values for the extracted watermarks for frames 1 to 15 in the first video scene under Gaussian attack.

(a) (b) (c)

(d)

Figure 12. (a) The watermarked frame under Gaussian noise attack using the proposed method. (b) Extracted watermark from frame 10 of second video in the proposed method (c) Extracted water-mark from frame 10 of the second video scene in the DWT-based SVD method. (d) Correlation val-ues due to detected watermarks from each frame in the second video scene under Gaussian attack.

Figure 12. (a) The watermarked frame under Gaussian noise attack using the proposed method.(b) Extracted watermark from frame 10 of second video in the proposed method (c) Extractedwatermark from frame 10 of the second video scene in the DWT-based SVD method. (d) Correlationvalues due to detected watermarks from each frame in the second video scene under Gaussian attack.


The watermarked video frames are compressed with a quality of 50% for video com-pression. The correlation coefficients reveal that the proposed watermarking is more ro-bust to video compression for most frames than the DWT-based SVD watermarking, as illustrated in Figures 13 and 14.

(a) (b) (c)

(d)

Figure 13. (a) The watermarked frame under compression attack using the proposed method. (b) Extracted watermark from frame 10 of the first video scene in the proposed method. (c) Extracted watermark from frame 10 of the first video scene for the DWT-based SVD method. (d) Correlation values for the extracted watermarks for frames 1 to 15 in the first video scene under compression attack.

(a) (b) (c)

(d)

Figure 13. (a) The watermarked frame under compression attack using the proposed method.(b) Extracted watermark from frame 10 of the first video scene in the proposed method. (c) Extractedwatermark from frame 10 of the first video scene for the DWT-based SVD method. (d) Correlation valuesfor the extracted watermarks for frames 1 to 15 in the first video scene under compression attack.

Electronics 2022, 11, 1849 16 of 25


The watermarked video frames are compressed with a quality of 50% for video com-pression. The correlation coefficients reveal that the proposed watermarking is more ro-bust to video compression for most frames than the DWT-based SVD watermarking, as illustrated in Figures 13 and 14.

(a) (b) (c)

(d)

Figure 13. (a) The watermarked frame under compression attack using the proposed method. (b) Extracted watermark from frame 10 of the first video scene in the proposed method. (c) Extracted watermark from frame 10 of the first video scene for the DWT-based SVD method. (d) Correlation values for the extracted watermarks for frames 1 to 15 in the first video scene under compression attack.

(a) (b) (c)

(d)

Figure 14. (a) The watermarked frame under compression attack using the proposed method.(b) Extracted watermark from frame 10 of second video in the proposed method. (c) Extracted water-mark from frame 10 of the second video scene in the DWT-based SVD method. (d) Correlation valuesfor the extracted watermarks from each frame in the second video scene under compression attack.


Figure 14. (a) The watermarked frame under compression attack using the proposed method. (b) Extracted watermark from frame 10 of second video in the proposed method. (c) Extracted water-mark from frame 10 of the second video scene in the DWT-based SVD method. (d) Correlation val-ues for the extracted watermarks from each frame in the second video scene under compression attack.

Histogram Equalization is an image processing technique that uses a histogram to alter the contrast of an image. It spreads out the most frequent pixel intensity values or expands out the image’s intensity range to improve contrast. Histogram equalization achieves this by allowing the image’s low-contrast sections to obtain more contrast. Im-ages that appear washed out due to a lack of contrast, equalization can be applied. Histo-grams will improve the contrast in an image, especially if the image has many low inten-sities. As illustrated in Figures 15 and 16, the correlation coefficients values clearly indi-cate the suggested approach’s robustness when compared to the DWT-based SVD method.

(a) (b) (c)

(d)

Figure 15. (a) The watermarked frame under the histeq attack using the proposed method. (b) Ex-tracted watermark from frame 10 of the first video scene in the proposed method. (c) Extracted watermark from frame 10 of the first video scene for the DWT-based SVD method. (d) Correlation values for the extracted watermarks for frames 1 to 15 in the first video scene under histeq attacks.

(a) (b) (c)

Figure 15. (a) The watermarked frame under the histeq attack using the proposed method.(b) Extracted watermark from frame 10 of the first video scene in the proposed method. (c) Extractedwatermark from frame 10 of the first video scene for the DWT-based SVD method. (d) Correlationvalues for the extracted watermarks for frames 1 to 15 in the first video scene under histeq attacks.

Electronics 2022, 11, 1849 17 of 25


Figure 14. (a) The watermarked frame under compression attack using the proposed method. (b) Extracted watermark from frame 10 of second video in the proposed method. (c) Extracted water-mark from frame 10 of the second video scene in the DWT-based SVD method. (d) Correlation val-ues for the extracted watermarks from each frame in the second video scene under compression attack.

Histogram Equalization is an image processing technique that uses a histogram to alter the contrast of an image. It spreads out the most frequent pixel intensity values or expands out the image’s intensity range to improve contrast. Histogram equalization achieves this by allowing the image’s low-contrast sections to obtain more contrast. Im-ages that appear washed out due to a lack of contrast, equalization can be applied. Histo-grams will improve the contrast in an image, especially if the image has many low inten-sities. As illustrated in Figures 15 and 16, the correlation coefficients values clearly indi-cate the suggested approach’s robustness when compared to the DWT-based SVD method.

(a) (b) (c)

(d)

Figure 15. (a) The watermarked frame under the histeq attack using the proposed method. (b) Ex-tracted watermark from frame 10 of the first video scene in the proposed method. (c) Extracted watermark from frame 10 of the first video scene for the DWT-based SVD method. (d) Correlation values for the extracted watermarks for frames 1 to 15 in the first video scene under histeq attacks.

(a) (b) (c)


(d)

Figure 16. (a) The watermarked frame under the histeq attack using the proposed method. (b) Ex-tracted watermark from frame 10 of second video in the proposed method (c) Extracted watermark from frame 10 of the second video scene in the DWT-based SVD method. (d) Correlation values for the extracted watermarks from each frame in the second video scene under histeq attack.

6.2. The Effect of the Depth of the Watermark Table 1 shows the effect of the depth of the watermark added to the video on the

quality of video the relation between the depth of the added watermark and the correla-tion between the original watermark and the extracted watermark. We find that, as the depth increases, the quality of the recovered watermark is increases even under the effect of attacks.

The additive technique of adding watermark to the image is performed using the following equations: 𝑆𝑖𝑛𝑔𝑢𝑙𝑎𝑟 𝑣𝑎𝑙𝑢𝑒𝑠 1 = 𝑆𝑖𝑛𝑔𝑢𝑙𝑎𝑟 𝑣𝑎𝑙𝑢𝑒𝑠 + 𝑘1 ∗ 𝑤𝑎𝑡𝑒𝑟𝑚𝑎𝑟𝑘

We studied the effect of k1 on the quality of recovered image and the recovered wa-termark.

Table 1. The effect of the depth of the watermark.

Mobile Video K1 (depth) 0.01 0.05 0.1 0.5 1

Proposed system

PSNR (average) watermarked frames)

115.13 dB 99.7 dB 91.48 dB 75.58 dB 68.34 dB

Correlation of watermark 0.99 0.99 0.99 0.99 0.99

SSIM 0.999 0.9989 0.956 0.91 0.6765

The quality of watermarked videos decreased with increasing the depth; however, the correlation between the watermarked video and the recovered one did not change. A comparison of PSNR was made for the effect of the depth of the watermark. The best quality was found when the depth was equal to 0.01. Therefore, we used this in our em-bedding technique.

Figure 16. (a) The watermarked frame under the histeq attack using the proposed method.(b) Extracted watermark from frame 10 of second video in the proposed method (c) Extractedwatermark from frame 10 of the second video scene in the DWT-based SVD method. (d) Correlationvalues for the extracted watermarks from each frame in the second video scene under histeq attack.

Table 1. The effect of the depth of the watermark.

Mobile Video

K1 (depth) 0.01 0.05 0.1 0.5 1

Proposed systemPSNR (average) watermarked frames) 115.13 dB 99.7 dB 91.48 dB 75.58 dB 68.34 dB

Correlation of watermark 0.99 0.99 0.99 0.99 0.99

SSIM 0.999 0.9989 0.956 0.91 0.6765

The additive technique of adding watermark to the image is performed using thefollowing equations:

Singular values 1 = Singular values + k1 ∗ watermark

We studied the effect of k1 on the quality of recovered image and the recovered watermark.The quality of watermarked videos decreased with increasing the depth; however,

the correlation between the watermarked video and the recovered one did not change.A comparison of PSNR was made for the effect of the depth of the watermark. The bestquality was found when the depth was equal to 0.01. Therefore, we used this in ourembedding technique.

6.3. Results of Proposed DT-CWT-Based SVD Video Watermarking Using LSB Method

Using a colorful video clip of 30 frames, we analyze the performance of the suggestedDT-CWT-based SVD video watermarking approach. Figures 17 and 18 show how the corre-lation coefficient varies with the frame index. The performance of the suggested method is

Electronics 2022, 11, 1849 18 of 25

clearly superior to that of DWT-based SVD watermarking, as shown in Figures 17 and 18.Figures 17 and 18 show snapshots of each scene. Using LSB approach, the watermarkwas then implanted in the Y component of each frame of the two scenes. In each of thetwo scenes, there was a different watermark. The two watermark pictures are 288 × 352pixels in size and each frame is 576 × 704 pixels in size.


6.3. Results of Proposed DT-CWT-Based SVD Video Watermarking Using LSB Method Using a colorful video clip of 30 frames, we analyze the performance of the suggested

DT-CWT-based SVD video watermarking approach. Figures 17 and 18 show how the cor-relation coefficient varies with the frame index. The performance of the suggested method is clearly superior to that of DWT-based SVD watermarking, as shown in Figure 17 and 18. Figures 17 and 18 show snapshots of each scene. Using LSB approach, the watermark was then implanted in the Y component of each frame of the two scenes. In each of the two scenes, there was a different watermark. The two watermark pictures are 288 × 352 pixels in size and each frame is 576 × 704 pixels in size.

(a) (b) (c)

(d)

Figure 17. (a) The watermarked frame using the proposed method. (b) Extracted watermark from frame 10 of the first video scene in the proposed method. (c) Extracted watermark from frame 10 of the first video scene for the DWT-based SVD method. (d) Correlation values for the extracted wa-termarks from each six frames in the first video scene without attacks.

(a) (b) (c)

Figure 17. (a) The watermarked frame using the proposed method. (b) Extracted watermark fromframe 10 of the first video scene in the proposed method. (c) Extracted watermark from frame 10of the first video scene for the DWT-based SVD method. (d) Correlation values for the extractedwatermarks from each six frames in the first video scene without attacks.

Figures 19 and 20 show that the DT-CWT-based SVD watermarking method is moreresilient than the DWT-based SVD watermarking method to the insertion of Gaussian noisewith variance = 0.01.

As illustrated in Figures 21 and 22, the correlation coefficients values clearly indicatethe suggested approach’s robustness when compared to the DWT-based SVD method.

Then the proposed technique is also applied to two videos with 300 frames, whichare Akiyo, and Foreman.mp4. As shown in Tables 2 and 3, the PSNR and SSIM of thewatermarked video frames, as well as the NC of the extract watermark, are described inTables 2 and 3. The PSNR value of the watermarked video frames reached 74 db, andthe SSIM values were all around 0.999, showing that the watermark has good invisibility,as shown in Tables 2 and 3. In addition, the extracted watermark’s NC values were all1.0000, demonstrating that the watermark can be extracted reliably when the host is notunder attack.

Electronics 2022, 11, 1849 19 of 25


6.3. Results of Proposed DT-CWT-Based SVD Video Watermarking Using LSB Method Using a colorful video clip of 30 frames, we analyze the performance of the suggested

DT-CWT-based SVD video watermarking approach. Figures 17 and 18 show how the cor-relation coefficient varies with the frame index. The performance of the suggested method is clearly superior to that of DWT-based SVD watermarking, as shown in Figure 17 and 18. Figures 17 and 18 show snapshots of each scene. Using LSB approach, the watermark was then implanted in the Y component of each frame of the two scenes. In each of the two scenes, there was a different watermark. The two watermark pictures are 288 × 352 pixels in size and each frame is 576 × 704 pixels in size.

(a) (b) (c)

(d)

Figure 17. (a) The watermarked frame using the proposed method. (b) Extracted watermark from frame 10 of the first video scene in the proposed method. (c) Extracted watermark from frame 10 of the first video scene for the DWT-based SVD method. (d) Correlation values for the extracted wa-termarks from each six frames in the first video scene without attacks.

(a) (b) (c)


(d)

Figure 18. (a) The watermarked frame using the proposed method. (b) Extracted watermark from frame 10 of second video in the proposed method. (c) Extracted watermark from frame 10 of the second video scene for the DWT-based SVD method. (d) Correlation values for the extracted water-marks from each frame in the second video scene without attack.

Figures 19 and 20 show that the DT-CWT-based SVD watermarking method is more resilient than the DWT-based SVD watermarking method to the insertion of Gaussian noise with variance = 0.01.

(a) (b) (c)

(d)

Figure 19. (a) The watermarked frame under Gaussian noise attack using the proposed method. (b) Extracted watermark from frame 10 of the first video scene in the proposed method. (c) Extracted watermark from frame 10 of the first video scene for the DWT-based SVD method. (d) Correlation values for the extracted watermarks from each six frames in the first video scene under Gaussian attack with variance 0.01.

Figure 18. (a) The watermarked frame using the proposed method. (b) Extracted watermark fromframe 10 of second video in the proposed method. (c) Extracted watermark from frame 10 ofthe second video scene for the DWT-based SVD method. (d) Correlation values for the extractedwatermarks from each frame in the second video scene without attack.


(d)

Figure 18. (a) The watermarked frame using the proposed method. (b) Extracted watermark from frame 10 of second video in the proposed method. (c) Extracted watermark from frame 10 of the second video scene for the DWT-based SVD method. (d) Correlation values for the extracted water-marks from each frame in the second video scene without attack.

Figures 19 and 20 show that the DT-CWT-based SVD watermarking method is more resilient than the DWT-based SVD watermarking method to the insertion of Gaussian noise with variance = 0.01.

(a) (b) (c)

(d)

Figure 19. (a) The watermarked frame under Gaussian noise attack using the proposed method. (b) Extracted watermark from frame 10 of the first video scene in the proposed method. (c) Extracted watermark from frame 10 of the first video scene for the DWT-based SVD method. (d) Correlation values for the extracted watermarks from each six frames in the first video scene under Gaussian attack with variance 0.01.

Figure 19. (a) The watermarked frame under Gaussian noise attack using the proposed method.(b) Extracted watermark from frame 10 of the first video scene in the proposed method.(c) Extracted watermark from frame 10 of the first video scene for the DWT-based SVD method.(d) Correlation values for the extracted watermarks from each six frames in the first video sceneunder Gaussian attack with variance 0.01.

Electronics 2022, 11, 1849 20 of 25Electronics 2022, 11, x FOR PEER REVIEW 21 of 26

(a) (b) (c)

(d)

Figure 20. (a) The watermarked frame under Gaussian noise attack using the proposed method. (b) Extracted watermark from frame 10 of second video in the proposed method. (c) Extracted water-mark from frame 10 of the second video scene for the DWT-based SVD method. (d) Correlation values for the extracted watermarks from each frame in the second video scene under Gaussian attack with variance 0.01.

As illustrated in Figures 21 and 22, the correlation coefficients values clearly indicate the suggested approach’s robustness when compared to the DWT-based SVD method.

(a) (b) (c)

(d)

Figure 21. (a) The watermarked frame under the histeq attack using the proposed method. (b) Ex-tracted watermark from frame 10 of the first video scene in the proposed method. (c) Extracted watermark from frame 10 of the first video scene for the DWT-based SVD method. (d) Correlation values for the extracted watermarks from each six frames in the first video scene under histeq.

Figure 20. (a) The watermarked frame under Gaussian noise attack using the proposed method.(b) Extracted watermark from frame 10 of second video in the proposed method. (c) Extracted wa-termark from frame 10 of the second video scene for the DWT-based SVD method. (d) Correlation valuesfor the extracted watermarks from each frame in the second video scene under Gaussian attack withvariance 0.01.


(a) (b) (c)

(d)

Figure 20. (a) The watermarked frame under Gaussian noise attack using the proposed method. (b) Extracted watermark from frame 10 of second video in the proposed method. (c) Extracted water-mark from frame 10 of the second video scene for the DWT-based SVD method. (d) Correlation values for the extracted watermarks from each frame in the second video scene under Gaussian attack with variance 0.01.

As illustrated in Figures 21 and 22, the correlation coefficients values clearly indicate the suggested approach’s robustness when compared to the DWT-based SVD method.

(a) (b) (c)

(d)

Figure 21. (a) The watermarked frame under the histeq attack using the proposed method. (b) Ex-tracted watermark from frame 10 of the first video scene in the proposed method. (c) Extracted watermark from frame 10 of the first video scene for the DWT-based SVD method. (d) Correlation values for the extracted watermarks from each six frames in the first video scene under histeq.

Figure 21. (a) The watermarked frame under the histeq attack using the proposed method.(b) Extracted watermark from frame 10 of the first video scene in the proposed method.(c) Extracted watermark from frame 10 of the first video scene for the DWT-based SVD method.(d) Correlation values for the extracted watermarks from each six frames in the first video sceneunder histeq.

Electronics 2022, 11, 1849 21 of 25

Figure 22. (a) The watermarked frame under the histeq attack using the proposed method.(b) Extracted watermark from frame 15 in the proposed method (c) Extracted watermark fromframe 15 of the DWT-based SVD method. (d) Correlation values for the extracted watermarks fromeach frame in the second video scene under the histeq attack.

Table 2. PSNR, SSIM, and NC of watermarked Akiyo.mp4.

watermark Frame 2 Frame 5 Frame 100


(a) (b) (c)

(d)

Figure 22. (a) The watermarked frame under the histeq attack using the proposed method. (b) Ex-tracted watermark from frame 15 in the proposed method (c) Extracted watermark from frame 15 of the DWT-based SVD method. (d) Correlation values for the extracted watermarks from each frame in the second video scene under the histeq attack.

Then the proposed technique is also applied to two videos with 300 frames, which are Akiyo, and Foreman.mp4. As shown in Tables 2 and 3, the PSNR and SSIM of the watermarked video frames, as well as the NC of the extract watermark, are described in Tables 2 and 3. The PSNR value of the watermarked video frames reached 74 db, and the SSIM values were all around 0.999, showing that the watermark has good invisibility, as shown in Tables 2 and 3. In addition, the extracted watermark’s NC values were all 1.0000, demonstrating that the watermark can be extracted reliably when the host is not under attack.



PSNR = 74.6 SSIM = −0.998 NC = 1

PSNR = 74.65 SSIM = 0.99 NC = 1

PSNR = 74.67 SSIM = 0.99 NC = 1



(a) (b) (c)

(d)





PSNR = 74.6 SSIM = −0.998 NC = 1

PSNR = 74.65 SSIM = 0.99 NC = 1

PSNR = 74.67 SSIM = 0.99 NC = 1


PSNR = 74.6SSIM = −0.998NC = 1


(a) (b) (c)

(d)





PSNR = 74.6 SSIM = −0.998 NC = 1

PSNR = 74.65 SSIM = 0.99 NC = 1

PSNR = 74.67 SSIM = 0.99 NC = 1


PSNR = 74.65SSIM = 0.99NC = 1


(a) (b) (c)

(d)





PSNR = 74.6 SSIM = −0.998 NC = 1

PSNR = 74.65 SSIM = 0.99 NC = 1

PSNR = 74.67 SSIM = 0.99 NC = 1


PSNR = 74.67SSIM = 0.99NC = 1



PSNR = 74.66 SSIM = 0.99 NC = 1

PSNR = 74.56 SSIM = 0.99 NC = 1

PSNR = 74.7 SSIM = 0.99 NC = 1

Table 3. PSNR, SSIM, and NC of watermarked “Foreman.mp4”.


PSNR = 74.7, SSIM = −0.998 NC = 1

PSNR = 74.9, SSIM = 0.99 NC = 1

PSNR = 74.6, SSIM = 0.99 NC = 1


PSNR = 74.8, SSIM = 0.99 NC = 1

PSNR = 74.7, SSIM = 0.99 NC = 1

PSNR = 74.8, SSIM = 0.99 NC = 1

6.4. Image Processing Attacks Noise and filter attacks are two types of image processing attacks. During the trans-

mission process, noise may affect the host in varying degrees. Table 4 shows how the ap-proach performs when the host is subjected to various types of noise, filtering, rotation, histogram equalization, sharpening, gamma correction, compression, cropping, and blur-ring attacks. The average NC value of the extracted watermark is above 0.9. This demon-strates that the technique has good robustness and can meet the requirements for safe and high-quality watermark transfer when a specific component of watermarked video frames is subjected to various attacks.

Table 4. Extracted watermark and NC value under various attacks.

Extracted Watermarks (NC)

Type of attack With-out at-tack

Median filter

Histogram equalization

blurring sharpening Gamma correction

Rotation 30

cropping Additive gaussian noise

Compression 50%

Mobile.mp4 (30 frames) Average of first 15 frames 1 0.938 0.924 0.932 0.965 0.983 0.996 0.996 0.956 0.933

Average of second 15 frames

1 0.975 0.932 0.943 0.964 0.985 0.995 0.997 0.967 0.932

Foreman.mp4 (301 frames)


PSNR = 74.66 SSIM = 0.99 NC = 1

PSNR = 74.56 SSIM = 0.99 NC = 1

PSNR = 74.7 SSIM = 0.99 NC = 1



PSNR = 74.7, SSIM = −0.998 NC = 1

PSNR = 74.9, SSIM = 0.99 NC = 1

PSNR = 74.6, SSIM = 0.99 NC = 1


PSNR = 74.8, SSIM = 0.99 NC = 1

PSNR = 74.7, SSIM = 0.99 NC = 1

PSNR = 74.8, SSIM = 0.99 NC = 1






Median filter



Rotation 30


Compression 50%



1 0.975 0.932 0.943 0.964 0.985 0.995 0.997 0.967 0.932


PSNR = 74.66SSIM = 0.99NC = 1


PSNR = 74.66 SSIM = 0.99 NC = 1

PSNR = 74.56 SSIM = 0.99 NC = 1

PSNR = 74.7 SSIM = 0.99 NC = 1



PSNR = 74.7, SSIM = −0.998 NC = 1

PSNR = 74.9, SSIM = 0.99 NC = 1

PSNR = 74.6, SSIM = 0.99 NC = 1


PSNR = 74.8, SSIM = 0.99 NC = 1

PSNR = 74.7, SSIM = 0.99 NC = 1

PSNR = 74.8, SSIM = 0.99 NC = 1






Median filter



Rotation 30


Compression 50%



1 0.975 0.932 0.943 0.964 0.985 0.995 0.997 0.967 0.932


PSNR = 74.56SSIM = 0.99NC = 1


PSNR = 74.66 SSIM = 0.99 NC = 1

PSNR = 74.56 SSIM = 0.99 NC = 1

PSNR = 74.7 SSIM = 0.99 NC = 1



PSNR = 74.7, SSIM = −0.998 NC = 1

PSNR = 74.9, SSIM = 0.99 NC = 1

PSNR = 74.6, SSIM = 0.99 NC = 1


PSNR = 74.8, SSIM = 0.99 NC = 1

PSNR = 74.7, SSIM = 0.99 NC = 1

PSNR = 74.8, SSIM = 0.99 NC = 1






Median filter



Rotation 30


Compression 50%



1 0.975 0.932 0.943 0.964 0.985 0.995 0.997 0.967 0.932


PSNR = 74.7SSIM = 0.99NC = 1

Electronics 2022, 11, 1849 22 of 25




PSNR = 74.66 SSIM = 0.99 NC = 1

PSNR = 74.56 SSIM = 0.99 NC = 1

PSNR = 74.7 SSIM = 0.99 NC = 1



PSNR = 74.7, SSIM = −0.998 NC = 1

PSNR = 74.9, SSIM = 0.99 NC = 1

PSNR = 74.6, SSIM = 0.99 NC = 1


PSNR = 74.8, SSIM = 0.99 NC = 1

PSNR = 74.7, SSIM = 0.99 NC = 1

PSNR = 74.8, SSIM = 0.99 NC = 1






Median filter



Rotation 30


Compression 50%



1 0.975 0.932 0.943 0.964 0.985 0.995 0.997 0.967 0.932



PSNR = 74.66 SSIM = 0.99 NC = 1

PSNR = 74.56 SSIM = 0.99 NC = 1

PSNR = 74.7 SSIM = 0.99 NC = 1



PSNR = 74.7, SSIM = −0.998 NC = 1

PSNR = 74.9, SSIM = 0.99 NC = 1

PSNR = 74.6, SSIM = 0.99 NC = 1


PSNR = 74.8, SSIM = 0.99 NC = 1

PSNR = 74.7, SSIM = 0.99 NC = 1

PSNR = 74.8, SSIM = 0.99 NC = 1






Median filter



Rotation 30


Compression 50%



1 0.975 0.932 0.943 0.964 0.985 0.995 0.997 0.967 0.932


PSNR = 74.7, SSIM =−0.998NC = 1


PSNR = 74.66 SSIM = 0.99 NC = 1

PSNR = 74.56 SSIM = 0.99 NC = 1

PSNR = 74.7 SSIM = 0.99 NC = 1



PSNR = 74.7, SSIM = −0.998 NC = 1

PSNR = 74.9, SSIM = 0.99 NC = 1

PSNR = 74.6, SSIM = 0.99 NC = 1


PSNR = 74.8, SSIM = 0.99 NC = 1

PSNR = 74.7, SSIM = 0.99 NC = 1

PSNR = 74.8, SSIM = 0.99 NC = 1






Median filter



Rotation 30


Compression 50%



1 0.975 0.932 0.943 0.964 0.985 0.995 0.997 0.967 0.932


PSNR = 74.9, SSIM =0.99NC = 1


PSNR = 74.66 SSIM = 0.99 NC = 1

PSNR = 74.56 SSIM = 0.99 NC = 1

PSNR = 74.7 SSIM = 0.99 NC = 1



PSNR = 74.7, SSIM = −0.998 NC = 1

PSNR = 74.9, SSIM = 0.99 NC = 1

PSNR = 74.6, SSIM = 0.99 NC = 1


PSNR = 74.8, SSIM = 0.99 NC = 1

PSNR = 74.7, SSIM = 0.99 NC = 1

PSNR = 74.8, SSIM = 0.99 NC = 1






Median filter



Rotation 30


Compression 50%



1 0.975 0.932 0.943 0.964 0.985 0.995 0.997 0.967 0.932


PSNR = 74.6, SSIM =0.99NC = 1



PSNR = 74.66 SSIM = 0.99 NC = 1

PSNR = 74.56 SSIM = 0.99 NC = 1

PSNR = 74.7 SSIM = 0.99 NC = 1



PSNR = 74.7, SSIM = −0.998 NC = 1

PSNR = 74.9, SSIM = 0.99 NC = 1

PSNR = 74.6, SSIM = 0.99 NC = 1


PSNR = 74.8, SSIM = 0.99 NC = 1

PSNR = 74.7, SSIM = 0.99 NC = 1

PSNR = 74.8, SSIM = 0.99 NC = 1






Median filter



Rotation 30


Compression 50%



1 0.975 0.932 0.943 0.964 0.985 0.995 0.997 0.967 0.932



PSNR = 74.66 SSIM = 0.99 NC = 1

PSNR = 74.56 SSIM = 0.99 NC = 1

PSNR = 74.7 SSIM = 0.99 NC = 1



PSNR = 74.7, SSIM = −0.998 NC = 1

PSNR = 74.9, SSIM = 0.99 NC = 1

PSNR = 74.6, SSIM = 0.99 NC = 1


PSNR = 74.8, SSIM = 0.99 NC = 1

PSNR = 74.7, SSIM = 0.99 NC = 1

PSNR = 74.8, SSIM = 0.99 NC = 1






Median filter



Rotation 30


Compression 50%



1 0.975 0.932 0.943 0.964 0.985 0.995 0.997 0.967 0.932


PSNR = 74.8, SSIM =0.99NC = 1


PSNR = 74.66 SSIM = 0.99 NC = 1

PSNR = 74.56 SSIM = 0.99 NC = 1

PSNR = 74.7 SSIM = 0.99 NC = 1



PSNR = 74.7, SSIM = −0.998 NC = 1

PSNR = 74.9, SSIM = 0.99 NC = 1

PSNR = 74.6, SSIM = 0.99 NC = 1


PSNR = 74.8, SSIM = 0.99 NC = 1

PSNR = 74.7, SSIM = 0.99 NC = 1

PSNR = 74.8, SSIM = 0.99 NC = 1






Median filter



Rotation 30


Compression 50%



1 0.975 0.932 0.943 0.964 0.985 0.995 0.997 0.967 0.932


PSNR = 74.7, SSIM =0.99NC = 1


PSNR = 74.66 SSIM = 0.99 NC = 1

PSNR = 74.56 SSIM = 0.99 NC = 1

PSNR = 74.7 SSIM = 0.99 NC = 1



PSNR = 74.7, SSIM = −0.998 NC = 1

PSNR = 74.9, SSIM = 0.99 NC = 1

PSNR = 74.6, SSIM = 0.99 NC = 1


PSNR = 74.8, SSIM = 0.99 NC = 1

PSNR = 74.7, SSIM = 0.99 NC = 1

PSNR = 74.8, SSIM = 0.99 NC = 1






Median filter



Rotation 30


Compression 50%



1 0.975 0.932 0.943 0.964 0.985 0.995 0.997 0.967 0.932


PSNR = 74.8, SSIM =0.99NC = 1



Type of attack Withoutattack

Medianfilter

Histogramequalization blurring sharpening Gamma

correctionRotation30 cropping Additive

gaussian noiseCompression50%

Mobile.mp4 (30 frames)

Averageof first 15 frames 1 0.938 0.924 0.932 0.965 0.983 0.996 0.996 0.956 0.933

Average ofsecond 15 frames 1 0.975 0.932 0.943 0.964 0.985 0.995 0.997 0.967 0.932


Averageof first 15 frames 1 0.945 0.914 0.933 0.974 0.987 0.995 0.997 0.978 0.942


Akiyo.mp4 (300 frames)

Average offirst 15 frames 1 0.976 0.928 0.931 0.967 0.979 0.997 0.996 0.976 0.955


6.4. Capacity

We can easily determine capacity using the method we proposed. The capacity isdetermined by the host as well as the watermark. Table 5 compares the capability of severalwatermarking technologies currently in use. In addition, we suppose the host’s size is576 × 704. When the watermark is a grayscale image, Table 5 displays the number of bitsthat can be embedded. It is simple to see how the proposed method can be used to imbedmore watermark information into the host than methods in papers [16,19,20].

Electronics 2022, 11, 1849 23 of 25

Table 5. Capacity comparison with existing methods.

Paper [19] Paper [20] Paper [16] Proposed

Grey scale image 4096 bits 8192 bits 40,960 bit 101,376 bit

6.5. Comparison and Discussion

The proposed method’s findings are compared to those of similar methods in thissection. It was not possible to find works with full similarity or very close matching due tothe small number of works conducted in the field of video watermarking or the lack of astandardized dataset; therefore, steps were taken to make the methods chosen have themost similarity to the proposed method and, as far as possible, have similar conditions.As a result, the suggested method’s results are compared to the outcomes of each of theseapproaches in Tables 6 and 7. We compare the quality of watermarked video and thetime required for implementation. In Table 8, the proposed technique is compared toexistence methods [16,21–23] under attacks and show the NC of recovered watermark. Asshown in the tables, the proposed strategy produces superior outcomes in the majority ofcircumstances than similar methods.

Table 6. Comparison between the proposed watermarking method and the traditional methodswithout attacks.

Method PSNR (dB) Run Time (sec)

DWT 65.96 15.7

SVD 33.37 18.8

DWT-based SVD 65.96 25.2

Proposed DT-CWT-based SVD 74.6 26.6

Table 7. Comparison between the proposed watermarking method and the traditional methodswithout attacks using LSB method for mobile video.

Method PSNR (dB) Run Time (sec)

DWT 35.90 16.3

DWT-based SVD watermarking 45.77 28.916

Proposed DT-CWT-based SVD watermarking 60.65 29.69

Table 8. NC comparison against various attacks of proposed method with existing method.

Type of Attack Paper [21] Paper [16] Paper [22] Paper [23] Proposed Method

No attack 1 1 1 1 1

Gaussian noise 0.965 0.707 0.880 0.81 0.956

rotation 0.998 0.787 - - 0.996

compression - - - - 0.945

Histogram equalization 0.981 0.694 0.990 0.886 0.924

Median filter 0.996 0.839 - 1 0.953

blurring - - - - 0.961

Gamma correction - - 0.935 1 0.979

Sharpening - - - 1 0.967

Cropping 0.991 - 0.070 0.900 0.996

Electronics 2022, 11, 1849 24 of 25

The experimental results reveal that the suggested LSB watermarking approach isrobust for most frames with and without attacks; however, when we measure the fidelityof the watermarked video, we discover that the proposed method is more robust than theDWT-based SVD watermarking in terms of fidelity.

7. Conclusions

In this paper, we offer a new approach for watermarking digital videos. The methodemploys the LSB and additive methods of two powerful mathematical transforms: DT-CWTand SVD. The DT-spatio-frequency CWT’s localization and the SVD’s substantial compo-nents’ compact capturing of semi-global features and the geometric information of imageswere integrated to utilize their appealing qualities. The proposed method’s resilience wasshown by the fact that it successfully recovered the watermark from each frame withoutusing the original video in the experiments. The retrieved watermark was extremely similarto the original watermark. The DWT-based SVD approach was compared to our proposedtechnique. The correlation coefficients revealed that the proposed watermarking was morerobust to video compression for most frames than the DWT-based SVD watermarking.

Author Contributions: Conceptualization, R.A. and H.A.A.; Data curation, R.A. and H.A.A.; Formalanalysis, R.A. and H.A.A.; Funding acquisition, R.A. and H.A.A.; Investigation, R.A. and H.A.A.;Methodology, R.A. and H.A.A.; Project administration, R.A. and H.A.A.; Resources, R.A. and H.A.A.;Software, R.A. and H.A.A.; Supervision, R.A. and H.A.A.; Validation, R.A. and H.A.A.; Visualization,R.A. and H.A.A.; Writing—original draft, R.A. and H.A.A.; Writing—review & editing, R.A. andH.A.A. All authors have read and agreed to the published version of the manuscript.

Funding: Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2022R323), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.

Acknowledgments: Princess Nourah bint Abdulrahman University Researchers Supporting Projectnumber (PNURSP2022R323), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.

Conflicts of Interest: The authors declare no conflict of interest.

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