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On Parameterization of Block based Copy-Move Forgery Detection Techniques
Jatin Wadhwa National Institute Of Technology Rourkela 769008,
Odisha [email protected]
Ruchira Naskar National Institute Of Technology Rourkela 769008,
Odisha [email protected]
Talib Ahemad National Institute Of Technology Rourkela 769008,
Odisha [email protected]
Rahul Dixit National Institute Of Technology Rourkela 769008,
Odisha [email protected]
ABSTRACTWith high increase in cyber-crime along with continuous development in multimedia processing and editing technologies, the credibility of digital images is highly at stake in the present day. Digital images act as the major source of legal evidence in various domains such as media, broadcast and legal industries. Hence any form of illegal modifications to them is intolerable. Recently a lot of researchers have focused on detection and control of digital image forgeries. However the literature lacks a standard way of evaluating and comparing the efficiencies of diverse forgery detection techniques. In this paper we propose a standard platform for estimating the efficiencies of state-of-the-art digital image forgery detection techniques. In this work we have dealt with a specific class of digital image forgery: the Copy-Move forgery, which is one of most prevalent forms of attack on digital images. We have compared and analyzed different copy-move forgery detection techniques using the proposed parameters. Our results prove the efficiency of the proposed parameterization and help to select the most suitable scheme according to the user’s requirements.
CCS Concepts• Applied computing ➝ Computer forensics; • Appliedcomputing ➝ Investigation techniques;
KeywordsCopy-move forgery; Cyber-crime; Digital image forgery; False negative rate; False positive rate; Parameterization
1. INTRODUCTIONDigital Forensics is a branch of forensic science encompassing the recovery and investigation of material found in digital devices. Digital images have always been the primary evidence to support or refute a hypothesis before criminal or civil court investigations or intrusion investigations, as well as in media and broadcast
industries. With the availability of powerful image editing and image processing software, credibility of the evidences contained in digital images is at stake. Traditional techniques for multimedia security and protection, including digital watermarking and steganography involve data pre-processing in some form or the other, which in turn requires image capturing devices to be equipped with special software and hardware utilities. However successful such techniques are in authentication and protection of digital multimedia data, they increase the device manufacturing cost manifolds [4]. To overcome this problem, in the recent years, a lot of research has been dedicated towards development of digital forensic tools and techniques for image forgery detection [16-18]. Such techniques are primarily aimed to be blind, i.e., they would not require any a-priori information or data pre-processing for multimedia data forgery detection, rather their operation is entirely based on post-processing of multimedia data.
During the past decade, forensic researchers have widely investigated different types of digital image forgeries, such as region duplication [1][2][19][16][18], compositing multiple images [17][10][12], forgeries involving specific image formats [7][5][14] etc. However, the current literature mainly focuses on the invention and development of forgery detection techniques, but lacks standard parameters to evaluate, compare and contrast the efficiencies of those.
In this paper we aim to provide a platform for performance estimation and fair comparison of state-of-the-art digital image forgery detection techniques. Our major contribution here is the development of standard parameters to analyse and compare diverse techniques, on the basis of their forgery detection efficiencies. In this paper we have dealt with, and demonstrated our results on region-duplication or copy-move form of image forgery [4-8], which is one of the most widely researched and most prevalent forms of attacks on the integrity of digital images. (However the evaluation parameters developed may be equally efficiently used to assess the efficiencies of other classes of image forgery detection schemes.) In copy-move forgery, a part of the image is copied and pasted somewhere else in the same image with the intent to obscure an important image object. In this class of forgery, the duplicated part(s) come from the same image. Hence its noise component, colour palette, dynamic range, and most other important properties will be compatible with the rest of the image, making the forgery difficult to be detected using methods that look for incompatibilities in statistical measures in different parts of the image [15-20]. Hence this class of forgery is of major concern to forensic researchers. In Figure 1, we have shown an example of copy-move forgery on an image, where a
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]. RACS’15, October 9–12, 2015, Prague, Czech Republic. © 2015 ACM. ISBN 978-1-4503-3738-0/15/10 …$15.00. DOI: http://dx.doi.org/10.1145/2811411.2811482
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car object has been copied from the original image and pasted on to itself to generate the tampered image. Vast majority of the copy-move forgery detection techniques operate on images, block-wise. Hence we focus on such block-based copy-move forgery detection techniques in this paper. (In Section 3, we discuss the block-based approach for copy-move forgery detection in details.)
Figure 1(a): Original Image
Figure 1(b): Tampered Image
Figure 1: Example of copy-move forgery done by standard image editing software. (Highlighted part of image is copied and pasted.)
In Section 2, we present an overview of state-of-the-art digital
forensic techniques. In Section 3, we discuss the operation of block-based copy-move forgery detection schemes in details. In Section 4, we present the proposed parameterized platform for evaluation and comparison of image forensic techniques. In Section 5 we present our experimental results. Finally we conclude with future research directions, in Section 6.
2. RELATED WORK In the recent years there has been considerable research towards development of copy-move forgery detection techniques. One of the most pioneer works has been done by Fridrich et. al. [1]. This paper [1] proposes three different block-based approaches for efficient and reliable copy-move forgery detection. Another very important contribution in this direction has been made by Popescu and Farid [2], where the authors have proposed detection techniques utilizing Principal Component Analysis (PCA) of images, for yielding reduced dimension representation, thus
making the proposed technique very less sensitive to additive noise or lossy compression. In [19], Cao et. al. have proposed different copy-move forgery detection techniques concentrating on low computational complexity and robustness to multiple copy-move forgery using DCT. In [16], Luo et. al. have proposed detection techniques that can successfully detect this type of tampering for images that have been subjected to various forms of post region duplication image processing, including blurring, noise contamination, severe lossy compression, and a mixture of these processing operations. The authors in [18] have used Scale-Invariant Feature Transform (SIFT) to handle situations where a duplicated region is geometrically transformed or re-scaled before being pasted to a new location. In the next section we present the technical details of state-of-the-art block-based region-duplication detection techniques. 3. BLOCK-BASED COPY-MOVE FORGERY DETECTION TECHNIQUES In this section we discuss in detail, the operating principles of four different copy-move forgery detection techniques, on which we carry out our experiments subsequently, so as to demonstrate the efficiency of the proposed parameterization of image forgery detection techniques. The proposed evaluation platform has been presented in Section 4, followed by our experimental results in Section 5. 3.1 Exhaustive Search In exhaustive search technique [4], is one of the most primitive techniques of copy-move forgery detection, where the copied-moved regions are tried to be detected by circular shifting and overlaying the image with itself. Here, the image is first divided into uniform blocks of fixed size (say B pixels), so that the blocks serve as the units of forgery detection. Initially, each pixel of both the circularly shifted image and the original image, are matched with one another and if their absolute difference is greater than or equal to a predefined threshold t, the entire block of size B pixels (containing the test pixel) is checked for duplication.
Let us assume that xi,j is a pixel of an M×N grayscale image, at position i,j (i.e. i-th row and j-th column). In exhaustive search, following is examined: �𝑥𝑖,𝑗 − 𝑥𝑖+𝑘(𝑚𝑚𝑚𝑚),𝑗+𝑙(𝑚𝑚𝑚𝑁)�, 0 ≤ 𝑘 < 𝑚; 0 ≤ 𝑙 < 𝑁,∀ 𝑖, 𝑗
........……….(1) The implementation of this scheme is considerably simple and
it is effective in finding major part of copy-moved segment in lossless image formats. However, in this scheme, for each shift, every pixel pair must be compared. This comparison requires M×N searches for each shift, the total number of possible shifts being (M/2)×(N/2). Due to this exhaustive search operation, the computational complexity of this algorithm is as high as order of (M×N)2, and hence makes it impractical to be applied even to moderate sized images. 3.2 Autocorrelation based Copy-move Forgery Detection Autocorrelation of a signal is defined as the degree of similarity between the signal and a lagged version of itself, over successive time intervals. The autocorrelation of image A of size M×N is defined as: 𝑟𝑘,𝑙 = ∑ ∑ 𝑥𝑖,𝑗𝑁−1
0 𝑥𝑖+𝑘,𝑗+𝑙𝑚−1𝑘=0 , 0 ≤ 𝑖 < 𝑚, 0 ≤ 𝑗 < 𝑁,∀ 𝑖, 𝑗
......….…….(2) The operating principle of autocorrelation based copy-move
forgery detection proposed in [2], is as follows. The original and
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the duplicated segments will introduce peaks in the autocorrelation for the shifts that corresponds to the copy-moved segments. If the autocorrelation is a spike, it says that the image has no elements that are correlated, that is every element is unique. Lobes, away from (0,0), suggest repetition. The distance away from (0,0) is the periodicity of the repetition.
Let the minimal size of copy-moved segment is B pixels, then to find the copy-moved segment following steps are undertaken: 1. Autocorrelation r of the forged image is computed. 2. Since autocorrelation is symmetric, half of r is removed. 3. We consider exact overlapping of two images, hence we set
correlation r=0. 4. Maximum of r is found from which shift vector is calculated.
The shift vector is exhaustively searched to detect copy-moved region.
5. If area of the detected region is greater than B, finish. 6. Else repeat step 5 with next maximum r.
Although this method is computationally simple (since we do not have to perform exhaustive search for every shift), it often fails to detect the forged area until forged area is large. It also generates a large number of false negatives while searching for the duplicated regions. 3.3 Exact Block Matching In exact block matching based copy-move forgery detection algorithm [1], the test image is divided into overlapping square blocks of size b×b pixels (say). The square block is slid by one pixel at a time, from left to right and top to bottom. For each block position, the pixel values of the block are extracted in column-wise order into a row of a 2-D matrix A with b2 columns and (M-b+1)×(N-b+1) rows, where M×N is the size of the image in pixels.
Each row in matrix A corresponds to one position of the sliding block. Two identical rows in A correspond to a copy-moved segment of size equal to that of the sliding block. For easy location of identical rows in the matrix A, the matrix A is lexicographically sorted first, such that the identical rows reside adjacent to each other in the sorted matrix. In this state the identical rows may be easily extracted. This algorithm is evidently far more computationally efficient than the previous two. However if the test image is saved in lossy formats such as JPEG (Joint Photographic Experts Group), a vast majority of identical blocks are lost, hence fail successful detection. 3.4 Duplication Detection Algorithm using PCA In Principal Component Analysis (PCA) based copy-moved forgery detection approach [2], images are divided into overlapping blocks, similar to block matching approach, each of which are considerably smaller than the size of duplicated region. An image is represented by a 2-D array, where every block is sorted and stored into one row of the array. Let an image of size M×N pixels be divided into b×b blocks. Then, the covariance matrix of each block is calculated as follows: 𝐶 = ∑ 𝑥𝑖𝑥𝑖𝑇
𝑁𝑏𝑖=1 ……………. (3)
where the blocks are represented by xi for i=1,2,3,…,Nb and Nb = (M+N-b)(M+N-b), gives the total number of blocks. The eigen vectors ej (for j=1,2,…b) of the covariance matrix C, corresponding to eigen values λj (for j=1,2,…b and λ1 > λ2 > … > λb), define the principal components of C. The eigenvectors, ej, form a new linear basis for each image block, 𝑥𝑖 = ∑ 𝑎𝑗𝑥𝑗
𝑏𝑗=1 …………… (4)
where 𝑎𝑗 = 𝑥𝑖𝑇𝑒𝑗 is the new representation for each image block.
The dimensionality of each block is reduced by simply truncating each vector xi to first Nt terms, where Nt is a user-defined parameter. This generates a new Nt-dimensional representation of C, say Ct, having Nb rows and b columns. The operations carried out on Ct to detect the forged blocks, may be summarized as follows: 1) Ct is sorted lexicographically in order to obtain a matrix S.
Let Si denote a row of the matrix S. Each row Si is represented by the tuple (xi, yi). Note that every row in matrix S represents a block’s image co-ordinates.
2) Every pair of rows Si,Sj where |i-j| < Nn. is stored in a list L. Here Nn is a user defined parameter denoting the number of neighboring rows to be search.
3) Offset frequency of each element in L is computed as follows: (𝑥𝑖 − 𝑥𝑗 ,𝑦𝑖 − 𝑦𝑗) if 𝑥𝑖 − 𝑥𝑗 > 0 (𝑥𝑗 − 𝑥𝑖 ,𝑦𝑖 − 𝑦𝑗) if 𝑥𝑖 − 𝑥𝑗 < 0 (0, 𝑦𝑖 − 𝑦𝑗) if 𝑥𝑖 = 𝑥𝑗
4) Offset magnitude of each element in S is computed as:
�(𝑥𝑖 − 𝑥𝑗)2 + (𝑦𝑖 − 𝑦𝑗)
2.
5) The pair with offset frequency less than Nf and offset magnitude less than Nd are discarded. Here Nf and Nd are threshold parameters, whose values are empirically chosen by the user so as to denote the minimum frequency threshold and minimum offset threshold, respectively.
6) The remaining pairs of rows of S, represent the duplicated image regions.
The accuracy of region-duplication detection has been found to be higher compared to the schemes previously discussed in Section 3.1 – 3.3.
In the next section we provide a parameterized platform for measuring and comparing the efficiencies of block-based copy-move forgery detection techniques, and subsequently apply the proposed parameters to evaluate the schemes discussed in Section 3. 4. PROPOSED PARAMETERIZATION In this section we propose three benchmark parameters – Detection Accuracy, False Positive Rate and False Negative Rate – which would comprise a standard performance evaluation for assessing the efficiency of block based copy-move forgery detection schemes. However, their usage may easily be extended for evaluation of other classes of digital image forgery detection. These parameters will help the users to select a particular forgery detection technique, according to his requirements. In this paper we limit our discussion to the applicability of the proposed parameterization to only block based copy-move forgery detection, as discussed in Section 3. The proposed parameters are defined as a function of the forgery size, defined as the area of the duplicated image region in terms of pixels. In the following we define the proposed parameters – Detection Accuracy, False Positive Rate and False Negative Rate, one-by-one. For estimating the efficiency of a block based copy-move forgery detection algorithm, we investigate the variation of those parameters with unit detection block size, where blocks represent the units of region duplication detection and localization, as discussed in Section 3. Detection Accuracy (DA) is the percentage of total number of forged pixels in an image, which a detection scheme succeeds to identify correctly. We represent detection accuracy as a fraction of the total number of pixels, actually duplicated in the image. This
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is a versatile parameter which is applicable to almost all classes of image forgery detection algorithms, for their performance evaluation. Hence, DA = Number of correctly detected copy−moved pixels
Number of pixels actually copy−moved ×100
…………(5) False Positive Rate (FPR) is the proportion of absent events
that yield positive test outcomes, i.e., the conditional probability of a positive test result given an absent event. In the context of image region duplication, False Positive Rate measures the area of those image region(s) which is (are) not copy-moved, but still detected to be forged by the forgery detection algorithm. In other words, False Positive Rate is defined as the total number of authentic image pixels, falsely detected to be forged, and expressed as a fraction of the total number of actually copy-moved pixels in the image. That is,
FPR = Number of pixels falsely detected to be copy−movedNumber of pixels actually copy−moved
× 100
….………(6) False Negative Rate (FNR) is the proportion of present events
that yield negative test outcomes, i.e., the conditional probability of a negative test result given the presence of event. In the context of image region duplication, False Negative Rate measures the area of those image region(s) which is (are) actually duplicated, but the forgery detection algorithm fails to identify them. In other words, False Negative Rate is defined as the total number of actually duplicated image pixels which are overlooked by the detection algorithm, expressed as a fraction of the total number of copy-moved pixels in the image. That is,
FNR = Number of undetected copy−moved pixelsNumber of pixels actually copy−moved
× 100
….………(7) Next we present a case study to demonstrate how the
proposed parameters help to select a particular forgery detection algorithm, based on user’s requirements.
4.1 A Case Study To present the case study we take the example of an image of size 128×128 pixels. In our example image, we manually induced a region duplication be 10% of the entire image area. We selected the unit detection block size to be 12×12 pixels. According to our experiments, Detection Accuracy of 86.5401%, 86.7310%, 94.6321% and 97.5577% were reported in Exhaustive Search [1], Autocorrelation [1] based detection, Exact Block Matching [1] and PCA [2] based detection, respectively (as computed by Eq. 5). The False Negative Rate of the Exhaustive Search, Autocorrelation, Exact Block Matching based detection algorithms were found to be 13.269%, 13.269% and 5.4583% respectively, whereas the False Positive Rate of the PCA based detection algorithm was 3.98271% (computed by Eqs. 6,7). However, the False Negative Rate for PCA based algorithm and the False Positive Rate of Exhaustive Search, Autocorrelation, and Exact Block Matching based algorithms, were found to 0%.
Now, assume a particular court proceeding where a highly security sensitive digital image is provided as an evidence of cyber-crime, with a motivation of detecting even the minimal tampering. In such cases, even a small false positive rate is not acceptable. So, the PCA based forgery detection algorithm would not be a wise choice as we have seen that it is prone to false positives. Other schemes such as the Exhaustive Search, Autocorrelation, and Exact Block Matching based algorithms are more suited here. On the other hand, if we are dealing with a less
sensitive case, where we are allowed to tolerate false positives or negatives to certain (small) extent, The PCA based algorithm is a good pick, as it gives us the highest detection accuracy rate.
For selecting the most appropriate unit detection block size, a-priori knowledge or estimate of the forgery size helps. For example, if the forgery carried out on a document containing text, there is a good probability that the forgery done is very small and it is used to cover some words or phrases. In this case, a smaller block size is more suitable. However, implementing smaller block size (to achieve higher detection accuracy) comes with a trade-off of higher computational complexity. A digital photograph, in which sky, foliage, sand or similar regular patterns are repeated, covering a large part of the image, larger block size would be more suited. Also, this would assume lower computational complexity. 5. RESULTS AND DISCUSSION In this section we present our experimental results along with the inferences drawn from those. We have implemented the block-based copy-move forgery detection techniques discussed in Section 3, and evaluated their performance efficiencies utilising the bench-
Figure 2: Performance Characteristics of Exhaustive Search based Copy-Move Forgery Detection: Variation of Detection Accuracy with B and Forgery Size.
Figure 3: Performance Characteristics of Autocorrelation based Copy-Move Forgery Detection: Variation of Detection Accuracy with B and Forgery Size.
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Figure 4: Performance Characteristics of Exact Block Matching based Copy-Move Forgery Detection: Variation of Detection Accuracy with B and Forgery Size.
Figure 5: Performance Characteristics of PCA based Copy-Move Forgery Detection: Variation of Detection Accuracy with B and Forgery Size.
Figure 6: Performance Characteristics of Exhaustive Search based Copy-Move Forgery Detection: Variation of FNR with B and Forgery Size.
Figure 7: Performance Characteristics of Autocorrelation based Copy-Move Forgery Detection: Variation of FNR with B and Forgery Size.
-mark parameters proposed in Section 4. All the implementations were done using Image Processing Toolbox of MATLAB. While implementing different block-based image region duplication algorithms, we varied the unit block size from 5% to 40% of a test image. Our test images consist of a set of 40 different standard grayscale 512×512 image processing test images, collected from the USC SIPI Image Database [20]. The performance traits of any specific detection algorithm, used in our experiments, was found to be identical for all our test images. Hence, in the following, we present the results for a single test image, the 512×512 grayscale Lena image. For the sake of performance assessment and comparison between different detection algorithms, we have manually induced region-duplication forgeries into our test image, where the forgery size is varied from 0% to 45% of the entire test image. (Here, forgery size is defined as the area of duplicated image region, in pixels).
In this section, we present the performance characteristics of: (a) Exhaustive Search, (b) Autocorrelation, (c) Exact Block Matching and (d) PCA based copy-move forgery detection algorithms, in terms of Detection Accuracy, FPR and FNR. We also show the variation of these performance parameters with unit detection block size and forgery size. In our experiments, we have divided the 512×512 test image into B×B unit detection blocks (having equal width and height), where the size of the unit block has been varied from 8×8 pixels to 36×36 pixels, i.e. B∈[8,36].
The performance characteristics in terms of Detection Accuracy for Exhaustive Search, Autocorrelation, Exact Block Matching and PCA based algorithms are shown in Figures 2, 3, 4, 5 respectively. From Figures 2-5, it is evident that for the first three techniques, the Detection Accuracy decreases with increasing block size, and this rate of decrease falls as the forgery sizes increases.
Figure 8: Performance Characteristics of Exact Block Matching based Copy-Move Forgery Detection: Variation of FNR with B and Forgery Size.
Figure 9: Performance Characteristics of PCA based Copy-Move Forgery Detection: Variation of FPR with B and Forgery Size.
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However, for the PCA based technique, Detection Accuracy is almost constant for varying block size. Higher the forgery size, higher is the detection accuracy. Among all four schemes, the PCA based scheme has the best performance in terms of Detection Accuracy, followed by Exact Block Matching.
The Exhaustive Search, Autocorrelation, Exact Block Matching based schemes suffer from false negatives during forgery detection, which have been presented in the form of variation of FNR with unit block size and forgery size, in Figures 6, 7 and 8, respectively. From Figures 6-8, it is evident that FNR increases with increasing block size, and this rate of increase diminishes with increasing forgery size. According to Figures 6-8, Exact Block Matching demonstrates the best performance among all the three schemes, in terms of FNR. However these schemes do not suffer from false positive detection rate. Hence, FPR for these three schemes are 0%. Figures 2-4 and Figures 6-8 suggest that the rate of change of FNR for these three schemes, is inverse of the rate of change of their Detection Accuracies.
On the other hand, the PCA based forgery detection scheme suffers from false positive forgery detection rate. The performance of the PCA based scheme in terms of FPR has been shown in Figure 9. This scheme does not suffer from false negatives in forgery detection. Hence PNR for the PCA based scheme was found to be 0% during our experiments. From Figures 9, it is evident that FPR for the PCA based scheme increases with increasing block size, and is inversely proportional to the forgery size.
Our experimental results prove that the proposed benchmark parameters constitute a versatile performance evaluation platform for state-of-the-art digital image forgery detection algorithms. We have demonstrated their applications to four state-of-the-art algorithms. However, this may be extended to other forgery detection schemes as well.
6. CONCLUSIONDigital image forensics is a dynamic field, which a lot of researchers are currently investigating. A number of image forgery detection techniques have been proposed during the last decade. However, when it comes to performance measurement, assessment and comparison of those techniques, the existing literature lacks a standard evaluation platform. In this paper we have proposed a benchmark platform, comprising of three different parameters, which encompass three different dimensions of conventional forgery detection operations. The proposed parameterization would help the users select an appropriate forgery detection algorithm according to her requirements, and the expected forgery type. Future research in this direction would include, incorporating more parameters into the proposed platform in order to optimise its efficiency in terms of image forgery detection evaluation and comparison.
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