1. science
2. physics

2014 22nd International Conference on Pattern Recognition
Anti-Forensics of JPEG Detectors via Adaptive
Quantization Table Replacement
Chao Chen† , Haodong Li† , Weiqi Luo, Rui Yang‡ and Jiwu Huang§
School of Software, Sun Yat-Sen University, Guangzhou, China, 510006
of Information Science and Technology, Sun Yat-Sen University, Guangzhou, China, 510006
‡ School of Information Management, Sun Yat-Sen University, Guangzhou, China, 510006
§ College of Information Engineering, Shenzhen University, Shenzhen, China, 518060
† School
Abstract—Due to the popularity of JPEG compression standard, JPEG images have been widely used in various applications.
Nowadays, detection of JPEG forgeries becomes an important
issue in digital image forensics, and lots of related works have
been reported. However, most existing works mainly rely on a pretrained classifier according to the quantization table shown in the
file header of the suspicious JPEG image, and they assume that
such a table is authentic. This assumption leaves a potential flaw
for those wise forgers to confuse or even invalidate the current
JPEG forensic detectors. Based on our analysis and experiments,
we found that the generalization ability of most current JPEG
forensic detectors is not very good. If the quantization table
changes, their performances would decrease significantly. Based
on this observation, we propose a universal anti-forensic scheme
via replacing the quantization table adaptively. The extensive
experimental results evaluated on 10,000 natural images have
shown the effectiveness of the proposed scheme for confusing
four typical JPEG forensic works.
I.
random perturbation on the DCT coefficients. In [12], Luo et
al. showed that nonaligned double JPEG compression usually
occurs in JPEG forgeries, and proposed a detection method
based on blocking artifact characteristics matrix. Recently,
Bianchi and Piva [13] proposed an improved method based
on the integer periodicity maps.
Like a cat-and-mouse game, a wise attacker may perform
some anti-forensic operations to remove those tampering traces
and fool the forensic detectors. In order to find out the weaknesses of current forensic methods and further improve their
robustness, researches have began to study the anti-forensic
methods from the perspective of forgers. For instance, Gloe et
al. [14] proposed two methods to trick the resampling detector
and the sensor pattern noise based detector respectively. Stamm
and Liu [15] introduced an anti-forensic scheme for removing
detectable artifacts left by lossy image compression. Sutthiwan
and Shi [16] proposed a method to deceive double JPEG
compression detection by destroying JPEG grid structure via
resizing images, and so on.
I NTRODUCTION
With the help of various powerful digital image editing
software, it is increasingly easy to modify image contents without leaving any obvious visual artifacts. If those sophisticated
tools were abused, it would inevitably lead to some potential
serious moral, ethical and legal consequences. Therefore, digital image forensics [1], [2] becomes an important issue and
it faces great challenges.
So far, most anti-forensic methods aim at attacking a
specific forensic detector, which means that they are difficult
to be extended for confusing other forensic detectors. In this
paper, we try to proposed a universal anti-forensic scheme for
those quantization table based JPEG detectors. Based on our
analysis and experiments, we found that the existing JPEG
forensic detectors usually assume that the quantization table in
the JPEG file header is authentic, and their performances are
highly dependent on this table. If a test JPEG image is fed to
a mismatched JPEG detector for a different quantization table,
the detection performances will drop significantly. Therefore,
we propose a simple yet very effective anti-forensic method
against this common flaw in JPEG forensic methods via just
replacing the quantization table of the file header. In the
proposed strategy, we try to obtain a proper quantization
table by adjusting the visual quality of the resulting JPEG
image adaptively. The experimental results evaluated on 10,000
natural image with four typical double JPEG compression
detectors have shown the effectiveness of the proposed antiforensic scheme.
As the most popular image compression standard, JPEG
has been widely used in various applications due to its high
compression efficiency and good image quality fidelity. Most
image forgeries are now stored as the JPEG format, and thus
lots of related forensic works have been proposed in the past
few years, such as identifying JPEG compression traces from
bitmap images and further estimating the compression parameters previously used [3], [4], exposing those doubly JPEG
compressed images [5], [6], locating the tampered regions in
JPEG composite images [7], [8], and so on. Since double JPEG
compression is a necessary operation after various tampering
operations, double JPEG compression detection attracts considerable attention. In [9], Chen and Shi proposed a method for
detecting double JPEG compression with different quantization
table via modeling the relations among the magnitude of
DCT (discrete cosine transform) coefficients. In [10], Li et
al. extracted a 180-D feature set from the first digit of DCT
coefficients in the individual AC mode for distinguishing
between singly and doubly JPEG compressed images. In [11],
Huang et al. proposed a scheme to detect double JPEG
compression with the same quantization table by employing a
1051-4651/14 $31.00 © 2014 IEEE
DOI 10.1109/ICPR.2014.126
The rest of this paper is arranged as follows. Section II
points out a common flaw in existing JPEG forensic detectors, Section III describes the proposed anti-forensic method,
Section IV demonstrates the experimental results and discussions for four typical JPEG detectors. Finally, the concluding
remarks are drawn in Section V.
672
Training set
with QA
Test image
with QA
Training set
with QB
Fig. 1.
II.
Feature
extraction
Detector A
Appropriate
decision
Detector B
Inappropriate
decision
Feature
extraction
Feature
extraction
JPEG forensic detector selection based on the quantization table of the test JPEG image
TABLE I.
T HE C OMMON F LAW IN JPEG F ORENSIC M ETHODS
TPR
QF2
Detector trained
with QF2 = 70
Detector trained
with QF2 = 80
In the JPEG lossy compression standard [17], the input
image is firstly divided into 8 × 8 non-overlapping blocks,
and then each individual block B in the spatial domain is
transformed to DCT frequency domain and obtain the DCT
coefficients (denoted as D), which are then quantized by a 8×8
matrix called quantization table Q, the quantized coefficients
(denoted as Dq ) are finally entropy encoded as bit streams.
The JPEG decompression works inversely: entropy decode
the bit streams to obtain the Dq , do the de-quantization to
obtain D, and then perform the inverse DCT (IDCT) on D to
obtain the 8 × 8 block in the spatial domain, finally round and
truncate the pixel values into the range of {0, 1, . . . , 255}. The
decompression operations can be formulated as follows:
FOR THE MATCHED / MISMATCHED DETECTORS
Method [9]
Method [10]
Method [9]
Method [10]
50
49.30
16.67
47.04
7.92
60
34.92
7.30
44.40
38.52
70
95.70∗
99.17∗
60.72
27.64
80
82.94
62.84
97.52∗
99.28∗
with QF2 = 50, 60, 70, 80, which are then fed to the pretrained detectors and obtain the true positive rate (TPR)1 .
The experimental results are shown in Table I. It is clearly
observed that when the test doubly compressed images are
detected by a matched detector, all the TPR exceed 95%
as highlighted with asterisks. However, when a mismatched
detector is selected, for example, using a detector trained
with QF2 = 70 to detect doubly compressed images with
QF2 = 80, the TPR will significantly drop. Therefore, we
can conclude that JPEG forensic detectors [9], [10] are highly
dependent on the quantization table as described in previous
analysis.
B = [IDCT(D)] = [IDCT(Dq · Q)]
Please note that the decompressed block B may not be exactly
the same as the input block B due to the errors introduced by
JPEG compression and decompression [4].
Please note that the quantization table Q is stored in the
JPEG file header, it is right after the hexadecimal flag “FFDB”.
In existing JPEG forensic methods, the detector selection is
mainly based on this file header information, and always
assume it is authentic. For a wise attacker, however, he or she
can easily modify the file header information with the help of
some text editors, such as Notepad++, so that a mismatched
detector is selected and thus it would lead to poorer detection
performances of the existing JPEG detectors. This is the main
idea of the proposed anti-forensics method.
Based on above analysis, it is observed that the quantization
table Q plays a very important role in both JPEG compression
and decompression, since it would affect the quality of the
resulting JPEG images. Usually, the smaller quantization steps
we use in Q, the better quality of the resulting JPEG image
we obtain. What is more, the quantization table Q would
significantly affect the statistic properties of resulting DCT
coefficients. Therefore, the common way for JPEG forensic
schemes is that: for a questionable JPEG image with a quantization table, they need to train a corresponding classifier
by some controlled JPEG images with the same quantization
table. Since the statistic properties of DCT coefficients are
quite different for different quantization tables, it is expected
that the generalization ability of the pre-trained classifier
may not be very good, meaning that if a JPEG image with
quantization table QA is fed to a detector B for quantization
table QB , the detector would probably give an inappropriate
decision, as illustrated in Fig. 1.
III.
T HE P ROPOSED A NTI -F ORENSIC M ETHOD
In this section, we propose an anti-forensic method for
fooling JPEG forensic detectors via adaptive quantization table
replacement. As mentioned in Section II, the quantization table
will affect both image quality and the detection performance
of JPEG detectors. Generally, the more quantization steps we
change, the poorer quality of image we obtain, however, the
better we confuse the JPEG detectors. There may be a good
trade off between the two things. Therefore, we should modify
the quantization table carefully.
To verify the above discussions, we conducted the following experiments. Firstly, we selected two forensic detectors [9],
[10] for double JPEG compression detection, and trained two
detectors for each method with quality factors QF2 = 70 and
QF2 = 80 using 2,500 singly and the corresponding doubly
JPEG compressed images with QF2 . In all doubly compressed
images, the primary quality factor QF1 is randomly selected
in the range of {50, 51, . . . 90} (excluding QF2 ). In the same
way, we created other test doubly JPEG compressed images
In order to preserve the image quality, we do not change
the quantization steps for low frequency DCT components
1 True positive rate (TPR) means that double compressed images are predicted correctly, while true negative rate (TNR) means that singly compressed
images are predicted correctly. Please note that the anti-forensic method aims
to reduce the TPR, while the TNR is unchanged.
673
(a) Original JPEG (QF = 70)
(b) The whole quantization table is replaced with QF = 50, 60, 80 and 90 respectively. The corresponding PSNRs compared
with Fig. 2(a) are 19.68dB, 23.68dB, 20.73dB, and 15.78dB.
(c) Preserving the first 5 quantization steps in the Zigzag order when replacing with QF = 50, 60, 80 and 90 respectively. The
corresponding PSNR compared with Fig. 2(a) are 30.76dB, 36.54dB, 36.76dB, and 30.67dB.
Fig. 2.
Illustrations of decompressed JPEG images after replacing the whole and partial quantization tables
(i.e. the first few steps in the Zigzag order). The reason is
that most of natural image information is concentrated in
a few low frequency DCT components. If those steps are
modified, the decompressed images would change a lot, and
some abnormal artifacts would also occur. As illustrated in
Fig. 2, the quantization table of the original JPEG image with
QF = 70 (Fig. 2(a)) is totally replaced with other four tables
with QF = 50, 60, 80 and 90, respectively 2 . It is observed
that the images become either too bright or too dark, since the
quantization steps in low frequency components especially the
DC component become larger and smaller, respectively. On the
other hand, if only the quantization steps for higher frequency
components are changed, the resulting images will look like
the original ones, just as shown in Fig. 2(c). In this case, the
corresponding PSNR between Fig. 2(c) and Fig. 2(a) are all
higher than those between Fig. 2(b) and Fig. 2(a).
three following steps.
1)
Generate a series of candidate quantization tables Qc ,
whose first N steps in Zigzag order are identical to
those in target table Qt , while the (N + 1)-th to the
last steps are replaced by quantization tables with
quality factors QFc , where QFc are the neighbors
of QFt with an interval r and a step 1:
QFc = {QFt − r, · · · , QFt + r} ∩ {1, 2, · · · , 100}
Based on the above analysis, we propose an adaptive antiforensic strategy to replace quantization table as illustrated in
Fig. 3. For a given image I and a target quantization table Qt
with a quality factor QFt , the proposed method includes the
Compress the given image I with each Qc respectively, and then we replace the quantization table
stored in the resulting JPEG file header with the
targeted table Qt .
Calculate the PSNR of the resulting images compared
with their corresponding versions before quantization
table replacement. If there are some images with
PSNR higher than a preset value P dB, we then
choose the one with the smallest PSNR as the output
image; otherwise, the one with the largest PSNR is
selected.
2 In our experiments, we employ quality factors QF ∈ {1, 2, · · · , 100} to
create different test quantization tables. However, our method can be extended
to arbitrarily customized quantization tables.
Please note that there are three parameters, i.e. N , r
and P , in the proposed method. By carefully selecting these
parameters, we can obtain a JPEG image with the target
2)
3)
674
JPEG images with
candidate tables
Input image I
J1
Jn
PSNR(Ji,Ji,Qt)
Replace with
target table Qt
Candidate tables
Target table Qt
……
J1,Qt
……
Selection based
on PSNR
Output JPEG image
with target table Qt
Jn,Qt
JPEG images after replacing
with target table Qt
Fig. 3.
Illustration of the proposed method
TABLE II.
TPR & TNR
WITH THE DETECTOR [9] (%) BEFORE AND
AFTER ANTI - FORENSIC OPERATIONS
TABLE III.
TPR & TNR
WITH THE DETECTOR [10]
AFTER ANTI - FORENSIC OPERATIONS
(%)
BEFORE AND
QF2
No Attack
N =4
TPR
N =5
N =6
50
81.68
↓11.44
↓10.78
↓ 3.74
60
87.14
↓ 2.98
↓ 3.12
↓ 3.80
70
95.94
↓10.72
↓ 4.90
↓ 2.64
80
98.34
↓18.24
↓15.54
↓14.00
90
100
↓20.72
↓16.62
↓18.96
QF2
No Attack
N =4
TPR
N =5
N =6
50
88.62
↓34.04
↓30.66
↓20.04
60
94.80
↓29.50
↓20.96
↓19.70
70
97.94
↓22.26
↓10.96
↓ 6.38
80
98.20
↓36.92
↓24.28
↓22.70
90
98.98
↓33.16
↓25.78
↓32.22
TNR
94.14
95.02
96.98
98.68
100
TNR
92.16
95.76
98.33
99.49
99.99
quantization table and have a good tradeoff between the image
quality and the performance of anti-forensic. We must note
that the proposed method just changes the JPEG file header, all
original quantized DCT coefficients Dq for each 8×8 block are
well preserved, which means that if the original quantization
table Qc is given (or as the side information), we can recover
the original JPEG image without any quality loss.
TPR degrades, the better performances we achieve. Please note
that the algorithm parameters are set as r = 20, P = 35, and
N = 4, 5, 6. Besides, we also show the corresponding TNR in
all the following experiments.
A. Anti-forensics of Double JPEG Compression with the Different Quantization Tables
To show the visual quality of the proposed method, we
illustrate two examples for fooling double JPEG compression
detection with the parameters P = 35, r = 20, N = 5 in Fig.
4. The given singly compressed images and their quantization
tables are shown in Fig. 4(a), while the target quantization
tables Qt are shown in Fig. 4(c). For fooling double JPEG
detection, we adaptively select the most suitable quantization
table Qc for each image according to the proposed method, and
then obtain the doubly compressed images with Qc as shown
in Fig. 4(b). Finally, the quantization table Qc are replaced
with Qt , the resulting JPEG images are shown in Fig. 4(c).
In 4(c), the highlighted quantization steps in Qt are different
from those in Qc . Although most quantization steps have been
changed, there are no obvious artifacts, and the PSNR of the
two images in Fig. 4(c) compared with Fig. 4(b) are both
higher than 35dB.
IV.
In this experiment, we generated doubly compressed images (i.e. positive instances) by successively compressing the
original uncompressed images with primary quality factor QF1
and secondary quality factor QF2 , where QF1 = QF2 . Thus
the negative instances were those singly compressed images
with QF2 . For making positive instances with anti-forensic
operation, we firstly compressed the original uncompressed
images with QF1 , and then performed the anti-forensic operations as described in Section III for a target QF2 . In our
experiments, QF1 was randomly selected from 50 to 90 with
a step 1, and QF2 was in the range from 50 to 90 with step
10. For a given QF2 , we randomly selected 5,000 positive
instances and the corresponding negative instances to train
two SVM classifiers [20] using the feature sets [9] and [10],
respectively. Finally, the remaining 5,000 positive instances
and their corresponding anti-forensically modified counterparts
were fed to the resulting classifiers for calculating the TPR.
E XPERIMENTAL R ESULTS
The experimental results are shown in Table II and Table
III. It is observed that the proposed method can reduce the
TPR in all the cases. For example, when N = 4, the average
decrements are 12.82% and 31.17%, respectively. It is also
observed that the performances tend to decrease when with
increasing the N . Because most of natural image information
is concentrated in lower DCT frequency components, when N
is larger (i.e. the first N quantization steps remain the same), it
is expected that more statistical properties of test JPEG image
would be well preserved after anti-forensic operations, and thus
the pre-trained classifier would still work. We also note that the
performances are dependent on the QF2 . For instance in Table
III, the results for QF2 = 70 are relatively poorer than others.
In our experiments, 10,000 uncompressed gray-scale images of size 512×512 are downloaded from BOSSBase image
database [18], and the MATLAB JPEG Toolbox [19] is used
for JPEG compression and decompression. Three different
kinds of forensic methods for JPEG images are considered,
including double JPEG compression detection with different
quantization tables [9], [10], double JPEG compression detection with the same quantization table [11], and nonaligned
double JPEG compression detection [13]. Since the antiforensic operations aim to confuse those positive instances,
we will compare the TPR of each forensic detector before
and after performing quantization table replacement. The more
675
10 7
6 10 14 24 31 37
6
4
4
6 10 16 20 24
5
5
6
8 10 23 24 22
7
7
6
5
6 10 16 23 28 22
8
8 10 14 24 34 41 34
6
7
9 12 20 35 32 25
8 10 13 17 31 52 48 37
7
9 15 22 27 44 41 31
11 13 22 34 41 65 62 46
10 14 22 26 32 42 45 37
14 21 33 38 49 62 68 55
20 26 31 35 41 48 48 40
29 38 47 52 62 73 72 61
29 37 38 39 45 40 41 40
43 55 57 59 67 60 62 59
8 11 16 35 36 33
(a) Singly JPEG compressed images and their quantization tables (QF =80 and 70, respectively)
3
4
7 11 14 17
6
4
3
5
8 13 16 20
7
7
4
5
7 16 17 15
5
5
4
6
8 19 19 18
8
4
4
7 11 16 19 16
6
4
5
8 13 18 22 18
4
5
6
8 14 24 22 17
4
5
7
9 16 28 26 20
5
6 10 16 19 31 29 22
6
7 12 18 22 35 33 25
10 7
7 10 15 18 23 29 32 26
8 11 18 20 26 33 36 29
14 18 22 24 29 34 34 28
16 20 25 28 33 39 38 32
20 26 27 27 31 28 29 28
23 29 30 31 36 32 33 32
(b) JPEG re-compressed above JPEG images with the selected quantization tables Qc (the corresponding target
tables are shown in Fig. 4(c))
6 10 14 24 31 37
6
4
4
6 10 16 20 24
8 11 16 35 36 33
5
5
6
8 10 23 24 22
8 10 14 24 34 41 34
6
5
6 10 16 23 28 22
8 10 13 17 31 52 48 37
6
7
9 12 20 35 32 25
11 13 22 34 41 65 62 46
7
9 15 22 27 44 41 37
10 7
7
7
8
14 21 33 38 49 62 68 55
10 14 22 26 32 42 45 37
29 38 47 52 62 73 72 61
20 26 31 35 41 48 48 40
43 55 57 59 67 60 62 59
29 37 38 39 45 40 41 40
(c) Resulting JPEG images after replacing with the target tables Qt (QF =70 and 80, respectively)
Fig. 4.
Two image examples generated by the proposed method with parameters N = 5, r = 20, P = 35.
TABLE IV.
The reasons are complicated due to the distributions of DCT
coefficients for QF = 70 and its relation with its adjacent QF ,
the generalization ability of the pre-trained classifier. We will
further analyze such a problem in our future work.
TPR
B. Anti-forensics of Double JPEG Compression with the Same
Quantization Table
In this experiment, we try to evaluate the anti-forensic
performances on attacking the detector proposed in [11], which
aims at exposing double JPEG compression with the same
quantization table. Thus, we created the singly compressed
images (i.e. negative instances) and the doubly compressed
images (i.e. positive instances) by compressing the original
uncompressed ones with the same QF once and twice respectively, where QF was in the range from 70 to 90 with a step
5. The anti-forensic modified images (i.e. positive instance
with anti-forensic operation) were generated by setting QF
as the targeted quality factor and compressing them with the
adaptively selected quantization tables twice. In order to obtain
the best parameter mpnc of the algorithm [11], 2,500 singly
compressed images and 2,500 doubly compressed ones were
used to search the highest accuracy of the detector for each
QF , and then we tested the remaining images and obtain the
TPR (before and after anti-forensic operations) of the detector
with the resulting best parameters.
THE
TNR(%) AND TPR(%)
VARIOUS QF .
OF DETECTOR IN
[11] FOR
QF
No Attack
N =4
N =5
N =6
70
80.00
↓70.94
↓70.07
↓71.27
75
80.65
↓70.79
↓68.86
↓71.12
80
89.20
↓82.87
↓82.74
↓79.67
85
90.80
↓76.41
↓77.88
↓76.68
90
93.93
↓88.60
↓89.40
↓88.40
95
97.61
↓96.68
↓96.81
↓96.88
TNR
54.07
56.08
83.81
86.12
90.67
96.45
decrements of TPR exceed 70% for all the listed cases. Please
note that here the around 18% of the resulting positive JPEG
images with the proposed method are lower than 35dB when
QF = 95 based on our experiments, and the smallest one is
around 30dB. However, we can also obtain the average PSNR
over 35dB in this case.
C. Anti-forensics of Nonaligned Double JPEG Compression
In this experiment, we try to attack the nonaligned double
JPEG compression detector proposed in [13]. In this case,
those singly compressed images (i.e. negative instances) were
created by compressing the original uncompressed ones with
QF2 only once, where QF2 was in the range from 50 to
90 with a step 10. For generating the positive instances, we
firstly compressed the uncompressed ones with QF1 , where
QF1 was randomly selected from 50 to 90 with a step 1,
and then we decompressed the JPEG images and cropped
them with a random shift (i, j), where 1 ≤ i, j ≤ 8, and
(i, j) = (8, 8). Finally we re-compressed the resulting images
The experimental results are shown in Table IV, it is
observed that the proposed method can completely fool the
detector [11], most TPR drop to less than 10% and the average
676
TABLE V.
THE
TNR(%)
AND TPR(%) OF DETECTOR IN
DIFFERENT N AND QF2 .
[13]
R EFERENCES
FOR
[1]
QF2
No Attack
N =4
TPR
N =5
N =6
50
20.34
↓ 6.16
↓ 4.42
↓ 2.76
60
31.80
↓ 9.50
↓ 6.30
↓ 4.18
70
51.26
↓15.36
↓11.04
↓ 8.12
80
70.94
↓17.96
↓13.40
↓11.18
90
90.84
↓28.62
↓20.96
↓18.22
TNR
95.92
96.72
97.76
99.10
98.44
[2]
[3]
with QF2 . For creating those positive instances with antiforensic operation, we compressed each of the cropped ones
with a quantization table selected by our proposed method
as described in Section III, and then replaced it by QF2 . To
determine the best decision threshold of the detector [13], we
selected 5,000 singly compressed images and 5,000 nonaligned
doubly compressed ones for each QF2 to conduct training
stage. The TPR for different cases were obtained by testing
the remaining 5,000 nonaligned double JPEG compressed
images and their corresponding counterparts after anti-forensic
operation.
[4]
[5]
[6]
[7]
[8]
The experimental results are shown in Table V. It is
observed that when the quality factor QF2 are lower such
as 50 and 60, the corresponding TPR before anti-forensic
operation are relatively low with the detector [13]. After
performing the proposed method, we can further decrease the
TPR. When the QF2 is larger, the degree of decrements on
TPR would increase. On average, The larger N is, the smaller
the decrements we obtain. As shown in the Table V, the
average decrements for N = 4, 5, 6 are 15.52%, 11.22%, and
8.89% respectively. The reasons are similar as described in
subsection IV-A.
V.
[9]
[10]
[11]
[12]
C ONCLUSION
[13]
In this paper, we first point out the common flaw in the
existing JPEG forensic methods: the detection performances
of their pre-trained classifiers are highly dependent on the
quantization table in the JPEG file header, which is assumed
to be authentic, and then we propose a universal anti-forensic
scheme against such JPEG detectors via replacing the quantization table according to the visual quality of the resulting JPEG
image. The extensive experiments evaluated on 10,000 images
have shown the effectiveness of the proposed anti-forensic
scheme against four typical JPEG detectors, including double
compression detectors for different and same quantization
tables, and nonaligned double compression detector.
[14]
[15]
[16]
[17]
[18]
In the next step, we will further investigate the relationship
between the generalization ability of the pre-trained classifier
and quantization tables, and try to propose an improved scheme
based on this relationship rather than just considering the visual
quality. Besides, we will consider whether or not the proposed
scheme can be extended to others related works on JPEG
forensics and JPEG steganalysis.
[19]
[20]
ACKNOWLEDGMENT
This work is supported by National Science & Technology Pillar Program (2012BAK16B06), NSFC (61332012,
61272191), the funding of Zhujiang Science and technology
(2011J2200091), the Fundamental Research General Program
of Shenzhen City (JCYJ20120613113535357) and the Guangdong NSF (S2013010012039).
677
H. Farid, “Image forgery detection,” IEEE Signal Processing Magazine,
vol. 26, no. 2, pp. 16–25, Mar. 2009.
W. Luo, Z. Qu, F. Pan, and J. Huang, “A survey of passive technology
for digital image forensics,” Frontiers of Computer Science in China,
vol. 1, no. 2, pp. 166–179, 2007.
D. Fu, Y. Q. Shi, and W. Su, “A generalized benfords law for JPEG
coefficients and its applications in image forensics,” in Proc. SPIE
Electronic Imaging: Security, Steganography, and Watermarking of
Multimedia Contents IX, 2007, vol. 6505, pp. 1L1–1L11.
W. Luo, J. Huang, and G. Qiu, “JPEG error analysis and its applications
to digital image forensics,” IEEE Trans. Information Forensics and
Security, vol. 5, no. 3, pp. 480–491, Sep 2010.
J. Luk´asˇ and J. Fridrich, “Estimation of primary quantization matrix in
double compressed JPEG images,” in Proc. Digital Forensic Research
Workshop, 2003, pp. 5–8.
Y.-L. Chen and C.-T. Hsu, “Detecting recompression of JPEG images
via periodicity analysis of compression artifacts for tampering detection,” IEEE Trans. Information Forensics and Security, vol. 6, no. 2,
pp. 396–406, 2011.
H. Farid, “Exposing digital forgeries from JPEG ghosts,” IEEE Trans.
Information Forensics and Security, vol. 4, no. 1, pp. 154–160, 2009.
T. Bianchi and A. Piva, “Image forgery localization via block-grained
analysis of JPEG artifacts,” IEEE Trans. Information Forensics and
Security, vol. 7, no. 3, pp. 1003–1017, 2012.
C. Chen, Y. Q. Shi, and W. Su, “A machine learning based scheme
for double JPEG compression detection,” in Proc. 18th Int. Conf. on
Pattern Recognition, 2008, pp. 1–4.
B. Li, Y. Q. Shi, and J. Huang, “Detecting doubly compressed JPEG
images by using mode based first digit features,” in Proc. IEEE 10th
Workshop on Multimedia Signal Processing, 2008, pp. 730–735.
F. Huang, J. Huang, and Y. Q. Shi, “Detecting double JPEG compression
with the same quantization matrix,” IEEE Trans. Information Forensics
and Security, vol. 5, no. 4, pp. 848–856, 2010.
W. Luo, Z. Qu, J. Huang, and G. Qiu, “A novel method for detecting
cropped and recompressed image block,” in Proc. IEEE Int. Conf.
Acoustics, Speech and Signal Processing, vol. 2, 2007, pp. 217–220.
T. Bianchi and A. Piva, “Detection of nonaligned double JPEG compression based on integer periodicity maps,” IEEE Trans. Information
Forensics and Security, vol. 7, no. 2, pp. 842–848, 2012.
T. Gloe, M. Kirchner, A. Winkler, and R. B¨ohme, “Can we trust digital
image forensics?” in Proc. 15th ACM Int. Conf. on Multimedia, New
York, NY, USA, 2007, pp. 78–86.
M. Stamm and K. Liu, “Anti-forensics of digital image compression,”
IEEE Trans. Information Forensics and Security, vol. 6, no. 3, pp. 1050–
1065, 2011.
P. Sutthiwan and Y. Q. Shi, “Anti-forensics of double JPEG compression
detection,” in Proc. 10th Int. Workshop on Digital Forensics and
Watermarking, 2012, vol. 7128, pp. 411–424.
G. K. Wallace, “The JPEG still picture compression standard,” Communications of the ACM, vol. 34, no. 4, pp. 30–44, Apr. 1991.
P. Bas, T. Filler, and T. Pevn´y, “Break our steganographic system:
The ins and outs of organizing boss,” in Proc. 13th Int. Workshop on
Information Hiding, vol. 6958, 2011, pp. 59–70, images available at:
http://agents.fel.cvut.cz/stegodata/PGMs/BossBase-1.01-cover.tar.bz2.
P. Sallee, “Matlab JPEG Toolbox,” available at: http://www.philsallee.
com/jpegtbx/index.html.
C. Chang and C. Lin, “Libsvm: A library for support vector machines,” ACM Transactions on Intelligent Systems and
Technology, vol. 2, pp. 27:1–27:27, 2011, software available at
http://www.csie.ntu.edu.tw/˜cjlin/libsvm.