1 0-1 4 July 201 7
Proceedings of theIEEE International Conference on Multimedia and Expo Workshops (ICMEW) 201 7
AUTOMATED JPEG FORGERY DETECTION WITH CORRELATION BASED LOCA LIZATION
Diangarti Bhalang Tariangi, Aniket Royi, Rajat Subhra Chakrabortyi and Ruchira Naska? 1
Department of Computer Science and Engineering
Indian Institute of Technology, Kharagpur, West Bengal, India 2 Department of Computer Science and Engineering National Institute of Technology, Rourkela, Odisha, India
-
-
721302.
769008.
Email: {diazz.tariang.aniketroy}@
[email protected]@nitrkl.ac.in
ABSTRACT
in a scenario where an adversary pastes slices from JPEG im age files into another RAW (JPEG, TIFF, etc.) image file, and then saves it in uncompressed format (TIFF). In such case, the presence of JPEG compression traces would suggest that the image has been already processed by someone." Identifica tion of JPEG traces in uncompressed format images has been recently reported in the field of digital forensics [3-6]. In this paper we present a blind JPEG based forgery detection and localization technique, based on the principle of image cor relation matrix computation of re-compressed images. The technique identifies if a TIFF image has been compromised with JPEG based forgery attacks. Though our technique has conceptual similarities to Farid's work [7]; however, our ad ditional major contributions as compared to the existing liter ature may be summarized as follows:
With the proliferation of the availability of highly sophis ticated editing tools, the authenticity of digital images has now become questionable. An adversary may perform copy paste forgery where portions from JPEG image files are copy pasted into another image file (JPEG, TIFF, etc.) and then save it in uncompressed format (TIFF). It is known that tam pered JPEG images contain traces of compression artifacts. In this paper, we propose an automated blind JPEG image forgery detection and localization technique with high de tection accuracy, which is effective for identifying traces of JPEG compression in digital images saved in uncompressed format (TIFF). The forgery detection and localization is based on the computation and analysis of a correlation matrix cal culated by recompressing the given (possibly tampered) im age at different quality factors and then comparing the re compressed versions with the given image. The experimental results prove our technique has high detection and localiza tion accuracy as compared to the existing techniques.
•
Farid's approach can only detect JPEG forgeries, but forgery localization is not taken care of in that ap proach. In addition to forgery detection, we also devise a localization technique to locate the tampered regions of the image. The proposed technique is capable of de tecting multiple tampered regions in a single image
•
We minimize the rate of false positives and false neg atives i.e. unaltered parts of the image incorrectly de tected to be forged, and vice-versa.
•
Farid's technique detects JPEG forgeries by recom pressing the image at different quality factors. The technique lacks automation; instead the author manu ally investigated all the recompressed images one-by one, and found that one of those produced a JPEG ghost artifact indicating the forged region. However, our pro posed technique is fully automated in generating an er ror image that depicts the existence of forgery as well as localizing the exact forged region.
•
Farid's technique requires extensive computations in case of DCT nonaligned attack, where all 64 possible alignments have to be considered to detect the JPEG
Index Terms- Digital image forensics, Joint Photo graphic Experts Group (JPEG), compression, tamper detec tion, tamper localization, quality factor. 1. INTRODUCTION
Protection of authenticity and legitimacy of images is a major concern in domains dealing with sensitive information, e.g. legal, criminal investigation, medical, military and broadcast ing. The situation is exacerbated by the easy and widespread access to highly sophisticated and user-friendly image pro cessing software tools. Digital Image Forensic techniques [1] provide solutions for such important problems of protection of image authenticity, investigation of image forgeries, as well as identification of image sources. As stated in [2] : "Normally, crime-scene and laboratory analysts save RAW files and archive them. As TIFF images are accepted as quality images within the forensic commu nity, the RAW data file are converted to a workable TIFF im age file for distribution and analysis. Forgeries can take place
ICMEW201 7
978-1 -5386-0560-8/1 71$31 .00 ©201 7 IEEE 226
2.2.1. Basics of JPEG Compression
ghosts. In contrast, while our proposed approach also involves recompression of the given image, it incurs much lesser computation overhead, since it is agnostic of the DCT coefficient alignment.
compression
EE§ :J�:
The rest of the paper is organized as follows. In Section 2, we present an overview of related literature, and a brief review of those features of JPEG compression which are relevant to our work in Section 2. In Section 3, the proposed technique for detection and localization of JPEG forgeries has been de scribed in detail. We present our experimental setup and de tection accuracy results and performance in Section 4. We conclude the paper with directions for future research in Sec tion 5.
�
� ---+
Souree Image
B OCT(B)
DB
---+
t±fj+-L:2J+- g Reconstructed +- J PEG Decompression JPEGImage ��r=lOB �
Fig. 1. JPEG Compression and Decompression process
2. BACKGROUND
JPEG compression process begins by dividing the im age of size NxN into 8x8 non-overlapping pixel blocks, B, followed by applying the two-dimensional Discrete Cosine Transform (DCT) to each block B to obtain its corresponding DCT coefficient block, DB. Next the DCT coefficients DB is uniformly and independently quantized by the 8x8 matrix Q quantization matrix Qq. The quantization matrix is defined by an integer quality factor q (q=1,2,. . . ,100). JPEG decompression works by reversing the above com pression method. The dequantized DCT D-B coefficients are recovered by mUltiplying the quantized coefficients QG{j with the corresponding quantization steps Qq stored in the quantization matrix Qq. The value of the DCT coefficient D-B in every 8 x 8 block in the image, is a multiple of Qq. The decompressed block, B' is obtained by computing the inverse DCT (IDCT) and the result is rounded and finally truncated off to integers in the range [0,255] (for an 8-bit grayscale image).
2. 1. Related Work
Asserting if an image in uncompressed format had undergone any prior JPEG compression is an important cue in image forensics. In [3] Fan and Queiroz identify traces of JPEG compressions in bitmap images by detecting the blockiness artifacts introduced by JPEG compression along with estimat ing the JPEG quantization table for the JPEG image. The statistical distribution of first digit of DCT coefficients is ex ploited in [5] based on Benford Distribution Law. In [4], Benford-Fourier coefficients have been used to analyze the distribution in the DCT quantized coefficients for detecting traces of previous compressions. JPEG compression noise is exploited by Wang et al. in [6, 8]. It can be noted that the JPEG compression noise of tampered region and that of unchanged region are quite different. The authors in [6] employed Principal Component Analysis (PCA) to separate different spatial frequencies quantization noises, i.e. low, medium and high frequency quantization noise and extract high frequency quantization noise for tampered region local ization. In [8] distributions of DCT coefficients in the forged and unforged region are used as mixture model. By estimat ing the parameters of the mixture model, likelihood map for each DCT block of being doubly compressed is computed. Authors in [8] exploited the prior knowledge that forged re gion should retain their smoothness and the defined energy function be minimized by using graph cut algorithm to accu rately localize the forged region. In addition, for a tampered image saved in a lossless format, the quantization noises of high frequency DCT coefficients is exploited to improve the localization method in [6 ].
2.2.2. Compression Error-Matrix
Note that the dequantization process is not the inverse func tion of quantization process due to the rounding function round(.) at the time of quantization resulting to a lossy com pression. When a JPEG image is re-compressed with the same compression ratio (Qq') as that used in the preced ing compression (Qq), i.e. Qq' Qq, the resulting re compressed DCT coefficient
D- B'
=
(
' D-B
round round
=
becomes:
(DQ;B)
x
Qq Qq
)
(1)
As mentioned earlier, during the JPEG dequantization pro cess, the obtained value of the DCT coefficient D-B in every 8 x 8 block in the image, is an exact multiple of Qq. Thus, Eqn. (1) becomes:
2.2. JPEG Compression Phenomenon
In this section we provide a brief overview of those features of JPEG compression and decompression and the notations which are relevant to our work. Fig. 1 illustrates the typi cal steps of JPEG Compression. For further details of JPEG compression of images, the readers may refer to [9].
===?
227
D- B'
=
round
(DQ;B)
x Qq
(2)
'
Therefore, we have the dequantized DCT coefficients D-B of the second compression equal to the dequantized DCT co efficients D-B of the first compression: ' D-B
= QC:
x
Qq = D-B
Algorithm 1
AUTOMATED_QUALITY..FACTOR.ESTIMATION
/*Automatically estimate the quality factor to derive the op timal image error-matrix from the tampered JPEG image */ Input: Tampered JPEG image
(3)
(1).
Output: Optimum quality factor for forgery detection
In other words S(i,j) 0, \/(i,j), where S represents the compression error-matrix between the two compressed images, such that S(i,j) stores the difference between the (i,j) th pixels of the two images. In case Qq' i- Qq, the corresponding pixel values of both the compressed im ages differ largely and the compression error-matrix, S has its entries S(i,j) i- 0 for most (i,j).
1:
=
2:
-
(QFo).
The tampered image (1) is re-compressed at JPEG quality factor QFq, where QFq = 40. Let IQFq denote the re-compressed version of the image. The error matrix S corresponding to IQF , is computed as fol q lows:
S(i,j) = [I(i,j) - IQFq(i,j)]lO where i,j ::; N.
(4)
3: The error matrix image,
S is divided into 8 x 8 non-overlapping pixel blocks B(r,s) row-wise. The quality factor of each block, QFq,(r,s) is estimated as:
3. JPEG FORGERY DETECTION AND LOCALIZATION THROUGH AUTOMATED
4: if
QUALITY FACTOR INVESTIGATION
5: 6:
In this section, we present an efficient blind JPEG-based forensic technique capable of automating the detection and localization of forged region(s). The proposed technique is blind in the sense that it requires no data pre-processing; it is completely based on post-processing of the image. Next, we provide details of the proposed method.
7:
8:
B(r,s)(i,j) = 0, V(i,j), 1::; i,j ::; 8, then QFq,(r,s) +-- QFq where r, s = 1, 2 , 3 , · . . , N/8
end if
The number of blocks having QFq,(r,s) = QFq, is recorded by a counter CQF • q The above steps 1-4 are now repeated for QFq 41..90 in steps of 1. Hence, for all QFq in 40 .. 90, the corresponding number of image blocks having matching quality factors are recorded in =
C40 ..C90. 9:
3. 1. Forgery Detection
The proposed method involves the process of automating the estimation of the desired quality factor at which an optimal error-matrix may be generated, so as to detect the existence of forgery. To do so, we investigate the differences between the forged image and different versions of it, obtained through re-compression at varied JPEG quality factors, block-wise, where the size of each block is 8 x 8. Let the tampered image of size NxN be denoted by I. Let us denote the actual compression ratio of an image as QF and QFq as the compression ratio used to re-compress the image. According to the discussion in Section 2.2.2 when the tam pered image is re-compressed at QFq, and the resultant error matrix S hasS(i,j) 0 \/(i,j), we can infer that the preced ing compression ratio QF is equal to QFq i.e. QF QFq. If the tampered image is re-compressed with QFq QF, the error matrix S would have S(i,j) 0 for authentic regions of the image, and S(i,j) i- 0 for most forged regions. To de tect the forgery we propose to find the optimal error-matrixS for which most of its entriesS(i,j) O. In Algorithm 1 (AU TOMATED QUALITY FACTOR ESTIMATION) we present the proposed quality factor estimation technique where the forgery is detected by estimating the quality factors at varied regions of the image. When most regions have quality fac tor equal to QF the corresponding error-matrix is selected as the optimal one. This "optimal" error-matrix depicts the ex istence of forgery in a tampered JPEG image. Algorithm 1 first re-compresses the tampered image at varying quality factor QFq, where QFq E (40, 41, 42···, 90). For each
The desired quality factor (QFo) at which the optimal error matrix image will be generated, would correspond to the maxi mum of C40 ..C90, i.e.,
such thatCQFx
QFo +-- QFx =maximum(C40,C41,'" ,C90).
re-compressed version of the tampered image, the error ma trix S is computed using Eqn. (4). Each error matrix image obtained at QFq is divided into 8x8 non-overlapping pixel blocks. Next, the quality factor of each block is estimated to be equal to QFq if all of its 8x8 pixel values is equal to zero. A counter is used to record the number of blocks having the same estimated quality factor, QFq. Finally the desired opti mum quality factor (QFo) at which the optimal error-matrix image will be generated, would correspond to the quality fac tor that has the maximum block number count.
=
=
3.2. Localization of Forged Region
=
=
In this subsection we propose a method to localize the forged region by utilizing the optimum quality factor QFo obtained through the procedure described in Algorithm 1 of Sec tion 3.1). As discussed in Section 2.2.2, when a JPEG image undergoes re-compression with the same quality factor as its original version, the compression error-matrix S has most of its entries equal to zeros i.e the re-compressed image is sim ilar to its previously compressed version. In other words the corresponding image pixel values of the re-compressed image and its previously compressed version are highly correlated. Therefore, to localize the forged region we compute the cor relation coefficients between the corresponding 8 x 8 blocks
=
228
Algorithm 2 FORGED-REGIONLOCALIZATION
/* Localize the forged region in the JPEG image */ Input: Tampered JPEG image
(I), Optimum quality factor(QFo).
Output: Location of the forged block. 1:
2:
Re-compress the tampered image (I) with the optimum quality factor (QFo), producing IQFo. Divide both the tampered image (I) and the re-compressed ver sion of the tampered image with optimum quality factor (IQFo)
Fig. 2. Forgery Detection and Localization: (a) tampered image; (b)
optimal error-matrix image in which the tampered regions are most clearly visible; (c) block number (B(r,s)) vs. correlation coefficients (R) plot classifying blocks with R -=1= 1 as tampered; (d) localized tampered regions.
into 8 x 8 blocks. Let B(r,s) and B;r,s),r, s 1, 2 , 3 , · · · , N/8 denotes the 8 x 8 blocks of I and IQFo respectively. =
3:
For each pair of corresponding blocks, pute the correlation matrix as:
4:
Count
5:
for
i
j 1 to 8 do if B(r,s)(i,j) -=1= B;r,s) (i,j) then
7:
end if
9:
12: 13:
14: 15: 16: 17: 18: 19: 20: 21: 22: 23: 24: 25:
=
Increment Count by 1
8:
11:
A complete visual depiction of the tampering detection and localization techniques is presented in Fig. 2. If the im age is indeed tampered, the resultant error matrix S obtained using Eqn. (4) by re-compressing the image at QFo, when viewed as an image, allows the forged region to be distin guished clearly in form of a grayish dot like pattern. Fig. 2(a) shows a tampered image, while Fig. 2 (b) shows the optimal error-matrix image corresponding to the automatically gen erated optimum quality factor. Fig. 2(b) shows the modified image region very clearly. The vs. plot for the
0
1 to 8 do
=
for
6:
10:
=
B(r,s) and B;r,s)' com-
end for
end for if
Count> Threshold then
(B(T,s»)
R(r,s) 0 where R(r,s) denotes the correlation matrix of the two corresponding blocks. =
R
else
R(r,s)
=
end if
for
1
1 to (N/8) do 1 to (N/8) do if R(r,s) -=1= 1 then
r
for
(B(T's»)
R
=
s
3.3. Elimination of False Positives and False Negatives
=
Mark
else
B(r,s) as forged block
In order to improve detection accuracy, we next reduce the number of false positives and false negatives, namely, forged blocks that are detected as un-forged and vice versa. To do so we modify the resulting refinement algorithm in [lO] as follows. By using the eight-connected component labeling algorithm [11], connected regions are first identified from the binary image of the localized tampered results of Algorithm 2 (white pixels indicating forged regions and black pixels indi cating un-forged regions). Small connected forged regions or un-forged regions, whose enclosing rectangles are smaller than 72 x 72 pixels, are expanded by considering the 8neighborhood 8 x 8 blocks for each 8 x 8 blocks of its regions. If the fraction of the forged blocks in the expanded region is greater than 0.6, the block is inferred to be forged otherwise it is inferred to be un-forged. Fig. 2 (d) shows the localization result of the forged region following Algorithm 2 and after application of the refinement process.
B(r,s) is unforged block
end if
end for
end for
of the tampered image I, and its re-compressed version IQFo obtained at the optimum quality factor QFo as decided in Sec tion 3.1. Correlation coefficient value equal to 1 indicates that the corresponding blocks are highly correlated (i.e similar). In Algorithm 2 (FORGED�EGION�OCALIZATION), we present the proposed localization technique to locate the tam pered blocks of the tampered image.
8
R
tampered image has been shown in Fig. 2(c). The vs. plot demonstrates a sudden change in the correlation coef ficient values with i- 1, corresponding to certain blocks of the tampered image. Such blocks are classified as tampered.
B(r,s)
B�r,s)'
Let and r, s 1, 2, 3, ·. . , NI8 denote the x 8 blocks of I and IQFo respectively. For each correspond-
B(T,s)
=
B�T's)'
ing blocks and we compute the correlation ma trix by checking if the corresponding pixel intensities of the blocks are equal. A variable Count is used to keep track of the number of pixels that are not equal. If the Count value is within a particular Threshold value, then 1, where denotes the correlation matrix of the two corre sponding blocks. In this paper, Threshold is empirically set to 25. Next we plot the block numbers of the tam pered image I against the correlation coefficients This plot helps us to locate the exact blocks which are tampered in a JPEG image.
R(T,s)
R(T,s)
R(T,s)
(B(T,s»)
3.4. Detection and Localization of Multiple JPEG Forg eries
=
In this paper, in addition to a single forgery in an image, we also consider the case where multiple regions of an image are forged. The proposed detection and localization methods pre sented in Section 3.1 and Section 3.2 respectively, when ap plied on the tampered image with multiple forged regions are efficient enough to detect and localize all those regions. The
R.
229
Ql �
Fig. 3. Multiple forgeries detection and localization: (a) tampered
image; (b) optimal error image depicting the existence of forgeries; (c) B(r,s) vs. R classifying blocks with R i= I as tampered; (d) localized tampered regions.
� Ql
50
Proposed
60
Proposed
70
Proposed
80
Proposed
90
Proposed
[8] [8]
[8]
[8] [8]
50
60
70
80
90
42.5% 49.3% 48.1% 42.3% 53.7% 42.6% 47.1% 41.3% 37.3% 29.7%
45.7% 50.6% 47.4% 53.3% 52.1% 42.1% 46.8% 40% 36.8% 30.6%
45% 49.3% 48% 53.7% 43.1% 47.6% 44.2% 45.5% 35.2% 32.4%
43.7% 45.2% 46.1% 51.7% 53.2% 58.2% 47.8% 45.9% 35.6% 29.2%
42.1% 48% 46.1% 53.3% 53.1% 58.8% 47.6% 52% 36.1% 32.2%
Table 1. Success Rates on Tampered Image Datasets
50
Proposed
60
Proposed
70
Proposed
80
Proposed
90
Proposed
[8] [8]
[8]
[8] [8]
50
60
70
80
90
96.1% 92.1% 94.2% 84.4% 93.6% 86.3% 97.3% 88.6% 96.9% 77%
94.8% 88.2% 95% 84.6% 93.4% 85.6% 97.4% 88.6% 97.9% 72.7%
97.5% 87.6% 94.8% 84.8% 93.9% 86.6% 96.9% 89.3% 95.8% 73.6%
96.4% 90.5% 95.1% 84.4% 94.8% 86.2% 97.1% 88.7% 97.4% 70.8%
60
Proposed
70
Proposed
80
Proposed
90
Proposed
[8]
[8] [8]
[8] [8]
70
80
90
98.7% 97.4% 100% 97.3% 100% 98.6% 100% 96% 100% 93.2%
98.9% 96.3% 100% 92.3% 100% 95.2% 100% 97.2% 100% 91.7%
98.7% 92.5% 100% 95.8% 100% 94.1% 100% 94.5% 100% 90.2%
97.8% 94.8% 100% 91.2% 100% 95.4% 100% 95.9% 100% 93%
99.4% 92.5% 100% 96.3% 100% 92.8% 100% 97.2% 100% 91.7%
pered Region Proportion of 50%.
image, where 1 :s; m, n < 512 and re-saved it with a second quality factor QF2. Because compression artifacts can be vi sualized in JPEG images compressed with low value quality factors, we have considered using quality factors, QF1 and QF2 E [40,45,··· ,95]. The size of the tampered region is 10%, 30% or 50% of image size as in [8] generating three datasets of tampered images viz. T1, T2 and T3. The re compressed region is later transplanted back to the same lo cation of the original image. To illustrate the effectiveness of the proposed method over JPEG forgeries involving diverse compression ratios, the at tack on the above-mentioned test images have been conducted for both cases of aligned and non-aligned JPEG attack. In or der to measure the performance of the proposed approach, success rate of localization (SR), we adopted the evaluation metric defined in [8]:
TI with Tam
96.5% 93.4% 94.8% 82.5% 94.7% 86.7% 96.9% 89.3% 96.7% 68.4%
Proposed
60
Table 3. Success Rates on Tampered Image Datasets T3 with Tam
pered Region Proportion of 10%. Ql �
50
50
SR
=
-'-i=_l=--_ ___ -::-::-
(5)
K
where K is the number of test images in the dataset, on values from 1 to K, a threshold Th 2/3 and
i
takes
=
Table 2. Success Rates on Tampered Image Datasets
T2 with Tam
if
pered Region Proportion of 30%.
forgeries are detected from the optimal error image shown in Fig. 3 (b) and localized from the vs. R plot shown in Fig. 3 (c). Shown in Fig. 3 (d) is the localized result of the forged regions.
B(r,s)
FI(i)
� others
Th
2 x Precision x Recall Precision + Recall TruePositive Preczswn .. TruePositive + FalsePositive TruePositive =--=----.,.-..--=---:.,----::--=--,--RecaII TruePositive + FalseNegative FI
=
=
4. EXPERIMENTAL RESULTS
=
The proposed technique was implemented in MATLAB (v 2015A), using the MATLAB Image Processing Toolbox. To evaluate the performance of the proposed technigue, we used 1338 uncompressed colour images (TIFF format) with size 512 x 384 or 384 x 512 from the VClD image database [12]. We performed JPEG compression of the VCID images with compression ratios QFl using the imw r i t e function of MATLAB. We manually forged selected regions of the JPEG test im ages for our experiments, the manual forgery being induced in the following way. We extracted a m x n region of the
(6) (7) (8)
The localization success rates of our proposed work and the method proposed in [8] on each of the three datasets, TI, T2 and T3, for different combinations of QI and Q2 (QI is the compression ratio of the un-forged regions and Q2 that of the forged regions) are tabulated in Table 1, 2 and 3. However, because of space constraints in the paper, we have exclusively shown results only for combinations of (Q1, Q2) viz. 50-50,50-60,50-70,. · . ,90-90. We found that the remain ing combinations of (QI, Q2) not reported here show similar trends. Both techniques have lower success rate values when
230
Size of tampering
10% 30% 50%
Method Ours
Success Rate
[8] Ours
[8]
and its tampered regions have been previously JPEG com pressed. We devised an efficient blind JPEG forgery detection and localization techniques that enable the forgery detection and localization process to be completely automated without the need for human intervention. The proposed method is ef ficient not only for detecting single forged regions but also multiple forged regions within the image. We achieved high detection and localization accuracy, and superiority of results with respect to previously proposed techniques. In our future work, we aim to improve the algorithm so as to accurately de tect tampered images with small scale tampering region and also to have a more generalized technique to detect all possi ble attacks on JPEG images.
88.7%
85.9%
98.9%
93.7%
Ours
99.9%
[8]
97.6%
Table 4. Success Rates on CASIA Tampered Image Dataset with
tampered images saved in TIFF format.
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5. CONCLUSION
"C ASIA Tampered Image Detection Evaluation Database," Available at http://forensics.idealtest.org/.
In this paper we investigated a specific type of tampering of JPEG images where the tampered image is in a lossless format
231
2009,
