Digital Signal Processing 22 (2012) 314–323

Contents lists available at SciVerse ScienceDirect

Digital Signal Processing

www.elsevier.com/locate/dsp

Robust and secured image-adaptive data hiding

Suresh N. Mali ∗, Pradeep M. Patil, Rajesh M. Jalnekar

Vishwakarma Institute of Technology, Pune, (MS), India

a r t i c l e i n f o a b s t r a c t

Article history:Available online 29 September 2011

Keywords:SteganographyData hidingRobustnessSecurityEnergyAETQuantizationCDCSPSNRIQMs

Rapid growth in the demand and consumption of digital information in past decade has led to validconcerns over issues such as content security, authenticity and digital right management. Imperceptibledata hiding in digital images is an excellent example of demonstration of handling these issues. ClassicalCryptography is related with concealing the content of messages, whereas, Steganography is related withconcealing the existence of communication by hiding the messages in cover. This paper presents a robustand secured method of embedding high volume of text information in digital Cover-images withoutincurring any perceptual distortion. It is robust against intentional or unintentional attacks such as imagecompression, tampering, resizing, filtering and Additive White Gaussian Noise (AWGN). The schemesavailable in the literature can deal with these attacks individually, whereas the proposed work is a singlemethodology that can survive all these attacks. Image Adaptive Energy Thresholding (AET) is used whileselecting the embedding locations in frequency domain. Coding framework with Class Dependent CodingScheme (CDCS) along with redundancy and interleaving of embedded information gives enhancement indata hiding capacity. Perceptual quality of images after data hiding has been tested using Peak Signal toNoise Ratio (PSNR) whereas statistical variations in selected Image Quality Measures (IQMs) are observedwith respect to Steganalysis. The results have been compared with existing algorithms like STOOL inspatial domain, COX in DCT domain and CDMA in DWT domain.

© 2011 Elsevier Inc. All rights reserved.

1. Introduction

Due to availability of Internet throughout the world, in un-derdeveloped as well as developed countries, content security isplaying a major role in multimedia communication. Since the sameInternet channels are used for commercial activities, coding the in-formation before transmitting has become a common practice toovercome hacking problems. The techniques available to achievethe goal of content security are Cryptography, Encryption andSteganography. Cryptography scrambles the message so that it can-not be understood, while Steganography hides the very existenceof the message by carefully embedding it into a cover. An eaves-dropper can intercept a Cryptographic message but one may noteven know the existence of Steganographic communication. En-cryption and Steganography achieves the same goal via differentmeans. Encryption encodes the data so that an unintended re-cipient cannot determine its intended meaning. Steganography, incontrast attempts to prevent an unintended recipient from sus-pecting about the hidden information [1,2]. Combining Encryptionwith Steganography allows better private communication.

* Corresponding author.E-mail addresses: snmali@rediffmail.com (S.N. Mali), patil_pm@rediffmail.com

(P.M. Patil), rajesh.jalnekar@vit.edu (R.M. Jalnekar).

1051-2004/$ – see front matter © 2011 Elsevier Inc. All rights reserved.doi:10.1016/j.dsp.2011.09.003

Image Steganography is the art and science of hiding important(secret) information in a Cover-image. The word Steganography hasbeen derived from the Greek words “stegos” meaning “cover” and“grafia” meaning “writing” [2] referred as “covered writing”. Se-curity is a major consideration while embedding messages of largevolume. There are several directions to alleviate this security issue:some involves adding uncertainty to the embedding mechanism,some generates features with randomness such as projecting a setof media components onto proprietary directions [3], and some fo-cuses on making the embedded message to be tamper-proof andforge-proof. In this work, main focus is given on adding security tothe core embedding mechanism to make it difficult for an attackerto detect the existence of evidence of embedding. The work pre-sented in this paper concentrates on embedding the text messagesinto images, however, the proposed approach and analysis can beeasily applied for embedding the message into audio or video sig-nals as a cover.

An early work on the image Steganography is Least SignificantBit (LSB) technique [4–9] that attempts to minimize the detectabil-ity of hidden data by introducing as little distortion as possibleduring embedding. However, as pointed out by Fridrich and Goljan[9,10], recent advances in Steganalysis have shown that this ap-proach does not guarantee detectability, evident by the fact thatthey can be successfully attacked using statistical or even visualattacks [11].

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Steganography Tool (STOOL) for windows [11,12] is one of themost popular technique available as an open source. Version 3.0includes programs that process GIF and BMP images. A useful fea-ture of STOOL is a status line that displays the largest messagesize that can be embedded in the given cover file. EzStego [11], aJAVA based tool is limited to 8-bit GIF files. This is an applicationof Steganography, on line stego tool which runs on different plat-forms and on internet. Hide and Seek 4.1 by Colin Maroney [4,13,14]is another basic Steganography program that works on either 8-bitcolor or 8-bit black-and-white GIF files. If the cover image is larger,the stego-image will be cropped to fit. Hide4PGP is a freewareprogram distributed as source code in ANSI C and precompiled exe-cutables for DOS, OS/2 and the Win32 console [15]. The informationto be hidden is scrambled, to prevent perceptible patterns withrepetitive data in BMP image files. White Noise Storm (WNS) [16] is aset of software for DOS which applies LSB technique to embed theencrypted message. It also utilizes the randomization and spreadspectrum principle which scatters the hidden message throughoutthe image. Steganos is one of the most impressive Steganographytool developed over the past few years [17]. This tool has changedfrom being a program to embed the message in LSBs of images toa sophisticated commercial Steganography suit with capability ofemploying adaptive Steganography. Westfeld proposed a LSB basedJPEG Steganography scheme, named F5 [18]. In this technique, in-stead of replacing the LSB, the DCT coefficients are either increasedor decreased by one. COX is a secured (tamper resistant) algo-rithm for watermarking images in DCT domain [6] which advocatea watermark embedded imperceptibly using spread-spectrum-likefashion into the perceptually most significant spectral componentsof the Cover-image. Data hiding capacity is increased using CodeDivision Multiple Access (CDMA) DWT based technique [19–21],however the perceptual quality of the image degrades after em-bedding the information. Solanki et al. [22] proposed a methodcalled Selective Embedding Coefficient (SEC) for the transmitter whichemploys local criteria to select coefficients for embedding. Use oflocal criteria for deciding where to embed is found to be crucialfor maintaining image quality under high volume embedding. Theblocks whose energy is greater than a predefined threshold are se-lected for information embedding.

There is a trade off between capacity, robustness (against at-tacks) and embedding induced distortion. In this work, along withthese three parameters a fourth parameter is considered which isthe security of a hidden information. Specifically, a mechanism ofCDCS to increase data hiding capacity and AET along with dataredundancy, interleaving and randomization to increase the robust-ness and security of the hidden information. The effect AET andQuality Factor (QF) on PSNR has also been tested. Robustness ofthe AET method is tested under various intentional and uninten-tional attacks such as image compression, limited amount of localand global image tampering, image resizing, low pass filtering andAWGN.

The rest of the paper is organized as follows. Section 2 dealswith the design issues while embedding large volume of text inCover-images. Section 3 describes the proposed robust and securedimage-adaptive data hiding system in detail. Results and discus-sions are compiled in Section 4 at length followed by conclusionsin Section 5.

2. Design issues

The design issues while hiding the text information in imagesusing sealed Steganography are:

1. The Stego image should not have any distortion artifacts thatmay cause any visual inspection to trigger the detection of hid-den information.

Fig. 1. Steganographic design issues.

Fig. 2. Trade-off between capacity, imperceptibility and robustness.

2. The algorithm should be statistically undetectable and it mustprovide Robustness against a variety of image manipulation at-tacks.

3. The information embedded should be highly secured.4. The most important is the algorithm should not scarify the

embedding Capacity in order to achieve the said requirements.

All these design issues are briefly summarized in Fig. 1. Thereare four major requirements of information hiding depending uponthe purpose of the application. There is always a trade-off betweenthese three main parameters i.e. capacity, imperceptibility and ro-bustness as shown in Fig. 2. If any one of these parameters ischanged then the other two gets affected. Though the capacity, ro-bustness, and security relation issues are driven by the applicationneed and its priorities, one has to optimize all the parameters toget the best results.

3. Proposed system

The general Steganographic data hiding system is as shown inFig. 3. To break the security of the communication the attackeris going to continuously monitor the public channel. A passive at-tacker is interested in finding whether a Stego-image sent by thetransmitter to the receiver contains any secret information or not.Therefore, the main role of proposed embedding algorithm (Fig. 4)is to reduce the perceptual degradation of a Stego-image so asnot to arouse an attacker’s suspicion. In other words, a Stegano-graphic system is considered to be insecure, if anyone is able todifferentiate between Cover-image and Stego-image. Embedding oftext message in Cover-image leaves unique artifacts in Stego-image,which can be detected using either PSNR or IQMs.

The proposed Steganographic data hiding system as shown inFig. 4, consists of text processing and image processing phases. In“Text Processing Phase” the text message is processed and in “ImageProcessing Phase” the Cover-image is processed. After embeddingthe information bits into the Cover-image, it is reconstructed to get

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Fig. 3. General Steganographic data hiding system.

a Stego-image as an output. The inputs to the proposed system areCover-image file (C ), Text message file (m), Number of Redundantbits for each encrypted bits (r), Number of bits for interleaving thebit stream (n), Energy Threshold Factor (w), Seed for the genera-tion of random number (seed) and image QF.

3.1. Text Processing Phase

Text message file along with r and n are the inputs to thisphase. The steps carried out during this phase are:

Step 1: Read the text message file.Step 2: Encrypt the characters of the text message file.Step 3: Add redundancy to each of the encrypted bit.Step 4: Interleave the bits and read them column wise to make the

bit stream ready for embedding.

The encryption methodology adopted for encrypting text charac-ters plays a vital role in deciding the embedding capacity and thelevel of robustness and security of the entire Steganographic sys-tem. In most of the Steganographic systems in the literature ASCII

character codes are used which needs 7-bit to represent each char-acter of the text message. However, in order to accommodate moreand more characters and increase the data hiding capacity we pro-pose a new CDCS encryption.

After studying various text one can assigned fixed decimalcodes using proposed CDCS technique to each character by con-sidering their relative frequency of occurrences [74]. One can cat-egorize the characters in three different non-overlapping classesas Class A (most frequently appearing characters), Class B (aver-age frequently appearing characters) and Class C (less frequentlyappearing characters). Further, assuming only capital letters, al-phanumeric and few special characters the number of bits neededto represent each character can be reduced in each class.

If N1, N2 and N3 are the total number of characters belongingto Class A, Class B and Class C respectively in CDCS, Total number ofbits m to be embedded is given by,

m = (N1 + 2 × N2 + 2 × N3) + 4 × h (1)

where h = N1 + N2 + N3, i.e. total number of characters in a textfile. Percentage Bit Saving (PBS) is given by,

PBS =[

1 −(

m

7 × h

)]× 100% (2)

where m = Total number of bits to be embedded Saving numberof encoding bits is nothing but increase in data hiding capacity.

The set (Tt ) of text characters representing the text of the mes-sage to be embedded is given by,

Tt ∈ {t1, t2, t3, t4, . . . , th} (3)

where h = Total number of characters in the given text.The set (Te) of encrypted bits using CDCS is given by,

Te ∈ {e1, e2, e3, e4, . . . , em} (4)

where m = Total number of bits to be embedded.Along with CDCS we are also using redundancy and interleav-

ing of embedded bits which will increase robustness of the systemfor attacks like image tampering and AWGN. With the help of re-dundancy one can make the copies of embedded bits and using

Fig. 4. Proposed Steganographic embedding algorithm.

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interleaving these bits scatters all over the Stego-image. Therefore,although some part of the Stego-image gets tampered or changeddue to AWGN, another copy of tampered information bits will al-ways be available in rest part of the Stego-image.

If r is the number of bits that are to be repeated for eachembedded bit, then the set (Ter ) of encrypted bits after addingredundancy is given by,

Ter ∈ {e11, e12, ..e1r, e21, e22, ..e2r,

e31, e32, ..e3r, . . . ., em1, em2, ..emr} (5)

For the sake of simplicity consider a set (M ′ = Ter ) having one-to-one correspondence as,

M ′ ∈ {b1,b2,b3,b4, . . . ,bp} (6)

where b1 = e11 and bp = emr .Before embedding the bits the set (M ′) is arranged on the basis

of interleaving factor (n) as,

M ′ ∈ {b1,b2,b3, . . . ,bn,bn+1,bn+2,bn+3, . . . ,b2n, . . . ,bp} (7)

While embedding the bit stream M ′ is referred as M and isgiven by,

M ∈ {b1,bn+1,b2n+1, . . . ,b2,bn+2,b2n+2, . . . ,b3,

bn+3,b2n+3, . . . ,bp} (8)

Bits taken in this specified sequence are called as Final BitStream (FBS) which eventually gets embedded into the quantizedDCT coefficients of the selected image blocks in image process-ing phase. More the value of redundancy, less will be the error inrecovering the information at the receiving end. However, it willreduce the number of characters to be embedded. More the valueof redundancy, more will the redundant bits gets spread all overthe image. Both redundancy and interleaving are responsible forthe robust data recovery at the receiver end.

3.2. Image processing phase

This phase take the Cover-image file, Energy Threshold Fac-tor (w), Seed for the generation of random number (seed) andrequired QF as an input and select the DCT coefficients for em-bedding. The steps carried out during this phase are:

Step 1: Read the Cover-image given by the user.Step 2: Divide the image into 8 × 8 non-overlapping blocks.Step 3: Apply two dimensional DCT to each block. If the intensity

values of the 8 × 8 blocks are aij , then the correspondingDCT coefficients ci j are given by,

ci j = DCT2(aij) (9)

where DCT2 denotes a two-dimensional DCT and i, j ={0,1, . . . ,7}.

Step 4: Calculate Energy of each block. Energy of a block is com-puted using,

E =7∑

i=1

7∑j=1

‖Cij‖2, ∀i, j = {0,1, . . . ,7}, (i, j) �= 0

(10)

The DC coefficient ((i, j) = 0) is not used for calculation ofenergy or embedding, because any variation in DC coeffi-cient of a block degrades the quality of the image heavily.

Step 5: Accept w from the user and calculate Mean Value of En-ergy (MVE) using,

MVE = 1

B

B∑1

Eb (11)

where B = Total number of blocks abd b = block number.Step 6: Identify the Valid Blocks VBs which satisfies the Energy

Threshold Criteria E � Et , where Et = w × MVE.Step 7: Randomly Selects blocks from VBs using,

Xn+1 = (A × Xn + C) mod (M), n � 0 (12)

where M is the modulus (M > 0), A is the multiplier(0 � A < M), C is the increment (0 � C < M), X0 is thestarting value 0 � X0 < M . Pseudo Random Sequence forspecific choices of M , A, C and X0 is given by (12).

Step 8: Accept QF from the user and quantize the coefficientsof all VBs by dividing them with respective elements ofquantization matrix as,

Ci j = Cij

M Q Fi j

∀i, j = {0,1, . . . ,7} (13)

where, Ci j is the quantized coefficient matrix, MQFij is

the i jth element of quantization matrix for a given valueof QF.

Step 9: Identify the Valid DCT Coefficients (VCs) which satisfiesthe non-zero criteria (Cij �= 0) and falls into lower andmiddle frequency band.

Step 10: Check suitability of the given Cover-image (number ofbits in FBS � number of VCs) and prompt the messageon console “Given Cover-image is not suitable for embed-ding”.

Step 11: Embed FBS given by the text processing phase into all se-lected VCs. The coefficients are scanned in zig-zag fashion,as in JPEG, to get one dimensional vector Ck . The embed-ding makes the quantized non-zero DCT coefficient ‘Odd’for ‘bit = 0’ or ‘Even’ for ‘bit = 1’. The coefficients withhidden bits dk are given by,

dk ={

Odd Ck, if bit = 0,

Even Ck, if bit = 1(14)

Step 12: The hidden coefficients dk are reverse scanned to forman 8 × 8 matrix and multiplied by the JPEG Quantizationmatrix to obtain unquantified coefficients (Cij).

Step 13: Apply inverse DCT to each block.Step 14: Reconstruct the image as Stego-image.

Low and middle frequency DCT coefficients are used to embedin VBs. Hiding the message in these coefficients induces minimaldistortion due to JPEG’s finer quantization in this range. Actually,compression reduces the energy of all blocks. Energy threshold-ing gives VBs with higher energy, which can handle variationsin their VCs without giving any perceptual degradation of Cover-image. Therefore VBs having more energy will protect the messageeven after compression attack.

4. Results and discussions

The proposed system has been tested with MATLAB 7.0 plat-form on Pentium-IV processor with 2.4 GHz and 4 GB RAM. A fullfetch graphical user interface has been developed using JAVA todemonstrate embedding of text information in 512 × 512 grayscale images. More than 3500 different types of 512 × 512 grayscale images have been tested by changing the parameters r, n,

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Table 1Number of bits saved using proposed CDCS.

Number ofEPR characters

Number of bitsin ASCII

Number of bitsin CDCS

Number of bitssaved

078 0546 0428 118253 1771 1407 364506 3542 2814 728584 4088 3244 844

Fig. 5. Number of bits saved with proposed CDCS mechanism.

w , QF and seed. The performance of the proposed system hasbeen compared with various other commercially available Stegano-graphic softwares such as COX, STOOL, DWT and SEC for the sameimage data set. Original Cover-images and the Stego-images ob-tained have been compared with respect to data hiding capacity,PSNR and IQMs. Robustness of the proposed system is tested un-der various intentional and unintentional attacks such as imagecompression, limited amounts of local and global image tamper-ing, image resizing, low pass filtering and AWGN.

4.1. Data hiding capacity

Initially all the images from image data set have been appliedas the input to the embedding algorithm to embed 1000 ASCIIcharacters of a text file ‘A-message.txt’. Out of 3545 images fromthe image data set 3025 images were eligible with VCs more thanthe encrypted number of bits. The proposed CDCS scheme saveslot of coding bits that are needed to be embedded in Cover-imagesas shown in Table 1. This saving can further be increased with in-crease in message length as well as increase in r as shown in Fig. 5.It can be observed that increase in data hiding capacity is the re-sult of saving the number of bits while coding the characters withproposed CDCS mechanism.

4.2. PSNR variations

The PSNR is most commonly used as a measure of percep-tual quality of Stego-image. The signal in this case is the originalCover-image (C ), and the noise is the error introduced due to em-bedding in Stego-image (S). PSNR is used as an approximation tohuman perception of reconstruction quality. A higher PSNR wouldnormally indicate that the reconstruction is of better quality. PSNRis computed in terms of Mean Squared Error (MSE) of two m × nmonochrome images C and S as,

PSNR = 10 log10MAX2

= 20 log10MAX

(15)

MSE MSE

Fig. 6. PSNR of Stego-images from the dataset.

Fig. 7. Effect of QF on PSNR for ‘Baboon’ image.

where, MAX is the maximum possible pixel value of the image and

MSE = 1

mn

i=0∑m−1

j=0∑n−1

∥∥C(i, j) − S(i, j)∥∥2

Higher value of PSNR is possible by proper selection of valuesof w , QF. Fig. 6 shows the plot of PSNR for all eligible Cover-images after embedding 6,190 bits of information with w = 1.0and ‘QF’ = 50. The PSNR of 2871 Stego-images (out of 3025) ismore than 40 dB for embedding of 6190 bits information. Suchhigher values of PSNR will certainly not create any doubt in theminds of the attacker.

The effect of w on PSNR for various values of QF have beentested for all images in image data set. Fig. 7 shows the result ofvariation in PSNR for ‘Baboon’ image. The value of PSNR increaseswith increase in w . Also the value of PSNR increases with QF forgiven value of w . This is because of trade off between the imagequality and the volume of embedding at a given robustness (deter-mined by selected QF). One can reduce QF to get maximum JPEGcompression for which the Stego-image is to survive.

4.3. JPEG compression attack

Digital images with hidden content may be compressed as itgoes over a low bandwidth link of a wireless network. Since theembedding methodology used in our schemes is tuned to JPEG, the

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Table 2Performance under JPEG Compression attack for ‘Lena’ image.

QF r JPEG attack[bpp]

Number bitsembedded

r JPEG attack[bpp]

Number bitsembedded

25 01 0.50 9120 03 0.42 323050 01 0.72 14,360 03 0.61 463075 01 1.30 18,790 03 1.15 6310

Table 3Data hiding capacity and PSNR for proposed and SEC algorithm.

Name of image file Bits embedded byproposed algorithm

PSNR[dB]

Bits embedded bySEC [22] algorithm

PSNR[dB]

Lena 14,357 41.72 11,044 34.58Peppers 14,160 41.77 10,447 35.89Baboon 40,075 39.67 25,331 32.27Bridge 35,868 40.98 24,633 32.34Couple 23,700 38.40 15,545 34.05Boat 21,063 41.08 15,234 34.21

Table 4Performance of the Algorithms for ‘Lena’ image under resizing attack.

Interpolationmethod

Percentageresizing attack

Bits embedded(proposed system)

Redundancy ‘r’(proposed system)

Bits embedded(SEC method [22])

Redundancy ‘r’(SEC method [22])

10% 4768 3 7447 11Bicubic 15% 4768 3 6826 12

20% 4768 3 6301 13Nearest 2% 4425 5 6301 13Neighbor 5% 3375 7 4096 20

10% 2535 9 2275 362% 4425 5 2275 36

Bilinear 5% 3375 7 2155 3810% 2535 9 1241 66

decoding of embedded data is perfect for all the attacks less thanor equal to the given QF. Table 2 shows the number of bits em-bedded (with 100% recovery) at various values of QFs, under JPEGattacks for ‘Lena’ image. Quality factor plays an important role indeciding the robustness of the hidden data. More the QF at thetime of embedding, more will be the non-zero DCT coefficients(that increases the embedding capacity) available for embedding.Addition of redundancy in text information bits before embeddinginto the images is another way to increase robustness. With re-dundancy the embedded information will survive for some morecompression. However, increase in redundancy beyond 3 will notonly reduce the embedding capacity but also gives only marginalimprovement in robustness.

Table 3 shows number of bits embedded (with 100% recov-ery) by proposed algorithm and Selectively Embedding in Coefficients(SEC) scheme [22] for QF = 25, under JPEG attacks for various Cover-images. It is observed that the data hiding capacity of the proposedalgorithm is more compared to SEC algorithm. Also the value ofPSNR has been improved.

4.4. Image resizing attack

Image resizing is also a popular attack. In image resizing, im-age is shrunk to a smaller size and scaled back to its originalsize. During this process there is possibility of losing the infor-mation. Various interpolation methods are used for image resizing.The most popular ones are Bilinear, Bicubic and Nearest Neighborinterpolations. Table 4 shows the results of resizing attack usingthe said interpolation methods at QF = 25 for proposed methodand SEC method [22]. It is observed that increase in resizing needsmore value of r for faithful reproduction at the cost of embeddingcapacity. Also, survival for more resizing is possible in Bicubic in-terpolation as that of Nearest Neighbor and Bilinear interpolation. Itis also observed that the proposed algorithm gives better results

Table 5Performance of proposed algorithm at QF = 25 for ‘Lena’ image under local imagetampering attack.

Image tamperingattack (%)

Bitsembedded

Redundancy‘r’

10 4135 320 2560 530 2560 550 1475 9

with less number of redundancy as compared to SEC algorithm.Robustness in the proposed system is achieved using inter leavingand redundancy, number of characters to be embedded will get re-duced while designing the robust data embedding system for givenpercentage of image resizing attacks.

4.5. Image tampering attack

The embedding scheme presented using proposed system is re-silient to image tampered in various ways. In spite of malicioustampering of the image all the embedded bits were recoveredsuccessfully after the attack. Fig. 8(a) shows 20% tampered ‘Lena’image. It can be observed from Table 5 that as the tampering in-creases, r increases for faithful reproduction of embedded informa-tion. However, this affects the overall embedding capacity. Globalimage tampering is as shown in Fig. 8(b). When ‘Lena’ image wasembedded with redundancy, r = 7 at QF = 25, all the 1900 bitswere recovered successfully from globally tampered Stego-image.

4.6. AWGN attack

The method considered in this work is sustainable to mildAWGN attack. Fig. 9(a) shows ‘Lena’ image having AWGN attackwith variance = 0.001 and mean = 0. With QF = 25, information

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Fig. 8. Image tampering attack: (a) ‘Lena’ image (512 × 512) tampered locally by 20%, (b) ‘Lena’ image (512 × 512) tampered globally.

Fig. 9. ‘Lena’ image with: (a) AWGN attack (variance = 0.001), (b) Gaussian low pass filter attack (a = 0.6).

of 1576 bits has successfully been embedded with said AWGN at-tack. Redundancy of 9 bits is necessary for faithful reproduction ofembedded information.

4.7. Filter attack

Low pass filter attack is very common attack in which theimage is filtered using a low pass filter without noticeable differ-ence. Fig. 9(b) shows the ‘Lena’ image with Gaussian Low Pass filter(σ = 0.6). With QF = 25, information of 2040 bits has successfullybeen embedded with said Gaussian low pass filter attack. Redun-dancy of 7 bits is necessary for faithful reproduction of embeddedinformation.

4.8. IQM analysis

The approach of attacker is based on the fact that hiding in-formation in digital images gives rise to alterations of the imageproperties that introduce some form of degradation, no matterhow small. These degradations can act as signatures that could beused to reveal the existence of a hidden message. For example, theStego-image is perceptually identical but statistically different fromthe Cover-image. The receiver uses these statistical differences inorder to decode the message. However, the very same statisticaldifference that is created could potentially be exploited by the at-tacker to determine if a given image is embedded or not. Avcibaset al. [23,24] showed in their paper that addition of a watermarkor message leaves unique artifacts, which can be detected usingIQMs. There are 26 different measures that are categorized into sixgroups as Pixel difference, Correlation, Edge, Spectral, Context andHuman visual system. Avcibas et al. [23] developed a discrimina-

Fig. 10. IQM calculation.

tor for Cover images and Stego images, using an appropriate set ofIQMs. To select quality metrics (features) to be used for Steganaly-sis, they used Analysis using Variance (ANOVA) techniques. Based onthese analysis, the IQMs that are useful for Steganalysis purposeare Minkowsky Measures M1 and M2, angular correlation M4, imagefidelity M5, normalized cross correlation M6, spectral magnitude dis-tortion M7, median of block spectral phase distortion M8 and medianof weighted block spectral distortion M9. The calculated IQMs for dif-ferent embedding domain plays an important role in the processof classification of images.

It has been observed that filtering an image without embeddedmessage causes changes in the IQMs differently than the changesbrought about on embedded Stego-images. The IQM scores arecomputed from images and their Gaussian filtered versions for se-lected IQMs [23] as shown in Fig. 10.

Fig. 11 shows variations in IQMs for proposed, STOOL, COX andCDMA techniques. Fig. 11(a) shows the Minkowsky Measure, M1 fordifferent embedding domains with embedding of 5% informationas that of total size of the Cover-image. It gives measure of dis-similarity. It can be observed that M1 gives significant difference

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Fig. 11. Variations in IQMs for proposed, STOOL, COX and CDMA techniques.

for STOOL, COX and CDMA with respect to original image. Whereas,this difference is very negligible for the proposed system. It is wellknown that the major distortion in spatial domain is because of di-

rect manipulation of LSB of pixel information byte. One can easilydifferentiate the spatial domain images by just observing the graphof 25 different images (from Cover-image data set) as shown in

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Fig. 12. IQMs Ml, M4 and M9 at different embedding ratios.

Fig. 11(a). Similar observations can be drawn from remaining IQMs(Fig. 11(b) to Fig. 11(h)). Therefore, it is difficult for the attacker todistinguish the Stego-image using proposed algorithm and originalCover-image as compared to other Steganographic tools.

The IQMs are calculated for varying percentage (1% to 15%)of embedded information. The effect of varying embedding ratiosover IQMs are as shown in Fig. 12(a) to Fig. 12(c) for M1, M4 andM9 respectively for ‘Baboon’ image. It can be observed that thevariations in IQMs caused due to embedding with proposed al-gorithm are very less as compared to variations in IQMs causeddue to embedding with other Steganographic tools. The measureM4 gives the similarity between two images. The CDMA embed-ding has more effect of variations with respect to embedding ratio.As percentage of information increases in CDMA the M4 tends tobe less. This indicates that increasing the embedding ratio causesmore dissimilarity between two images. Variation in M4 is lessin case of STOOL and proposed DCT domain embedding as shownin Fig. 12(b). Most of the available modern Staganalysis tools usesIQMs prescribed in this paper. Because of these minimum IQMsvariations the proposed system is more secured than other sys-tems.

Fig. 12(c) shows the effect of variation of embedding ratioon Spectral Measure M9. This measure captures the statistical dis-tortions caused in the frequency domain. The variations in M9for proposed DCT domain embedding is negligible as compare toSTOOL and CDMA embedding. This is due to the minimum alter-ation of statistics of Cover-image in frequency domain in the pro-posed system as compared to spatial variations in STOOL and DWTdomain variations in CDMA mechanisms.

5. Conclusions

The key factors to achieve robustness of the proposed schemeare a powerful coding framework that allows dynamic choice ofhiding locations and embedding in robust DCT coefficients. Re-dundancy, interleaving, energy thresholding and randomizationspreads the embedded information all over the Cover-image. Thistakes care of the attacks like image tampering, resizing, filtering

and AWGN. JPEG quantization reduces the possibility of corruptingthe information for selected QF in compression attack. However, asthe level of compression increases, the number of VCs gets reducedwhich in tern reduces the pay load. The embedding parameters w ,QF , r and seed for the given Cover-image decides the embeddinglocations adaptively and becomes the integral part of the AGEK.Due to this even the transmitter does not have the explicit knowl-edge of locations where the information has been embedded. Thisincreases the security level of the system. The additional advantageof the system is the feedback prompt about the suitability of theCover-image for embedding the required number of characters. Theproposed system gives better perceptual quality of Stego-imagethan the STOOL, COX, CDMA and SEC techniques. Increasing redun-dancy increases robustness but reduces payload. The embeddingcapacity achieved is among the best reported in the literature withadded advantage of enhanced robustness and security. Proposedsystem gives minimum IQMs variations. Therefore the Stego-imagesobtained using this system are secured from the modern Steganal-ysis tools. Thus the proposed system can be leveraged in severalexciting applications, such as image annotation, electronic patient’sreport data hiding and monitoring of criminal information throughtheir fingerprint images.

Acknowledgments

Authors are thankful to anonymous reviewers for their valuablesuggestions that have raised eminence of the paper.

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Suresh N. Mali received the B.E. degree in Electronics Engineering in1987 and the M.E. degree in Electronics Engineering – Computer Appli-cations in 1992 from Shivaji University, Maharashtra (India). He receivedhis Ph.D. degree from Bharati Vidyapith (India). He is working as a teachersince 1987 and currently working as Principal, Imperial College of Engi-neering, JSPM Wagholi, Pune (India). His research interests are informationsecurity, data hiding, signal processing, digital multimedia communica-tions and Steganography.

Pradeep M. Patil received the B.E. degree in Electronics Engineeringin 1988 from Amravati University, Amravati (India) and M.E. Electron-ics in 1992 from Marathwada University, Aurangabad (India). He receivedhis Ph.D. degree in Electronics and Computer Engineering in 2004 atSwami Ramanand Teerth Marathwada University (India). He is working asa teacher since 1988 and presently he is working as Principal, SinghgadCollege of Engineering, Warje, Pune (India). He is a member of variousprofessional bodies like IE, ISTE, IEEE and Fellow of IETE. He has been rec-ognized as a Ph.D. guide by University of Pune and North MaharashtraUniversity, Jalgaon (India). His research areas include pattern recognition,neural networks, fuzzy neural networks and power electronics. His workhas been published in various national and international journals and con-ferences including IEEE and Elsevier.

Rajesh M. Jalnekar received the B.E. degree in Electronics and Telecom-munication in 1988, the M.E. degree in Electronics and Telecommunicationin 1993 and Ph.D. degree in Electronics and Telecommunication from PuneUniversity, Maharashtra (India). He has 2 years of industrial experienceand 17 years of teaching experience. He is currently working as a Director,at Vishwakarma Institute of Technology, Pune (India). Research interestsinclude information security, data hiding, digital signal processing, multi-media communications and Steganography.