Exposing Inpainting Forgery in JPEG Images under Recompression ...

2016 15th IEEE International Conference on Machine Learning and Applications

Exposing Inpainting Forgery in JPEG Images under Recompression Attacks Qingzhong Liu

Andrew H. Sung

Department of Computer Science Sam Houston State University Huntsville, TX 77341, USA [email protected]

School of Computing University of Southern Mississippi Hattiesburg, MS 39406, USA [email protected]

Bing Zhou

Mengyu Qiao

Department of Computer Science Sam Houston State University Huntsville, TX 77341, USA [email protected]

Department of Mathematics and Computer Science South Dakota School of Mines and Technology Rapid City, SD 57701, USA [email protected]

Abstract— Inpainting, originally designed in computer vision to reconstruct lost or deteriorated parts of images and videos, has been used for image tampering, including region filling and object removal to alter the truth. While several types of tampering including copy-move and seam carving forgery can now be successfully exposed in image forensics, there has been very little study to tackle inpainting forgery in JPEG images, the detection of which is extremely challenging due to the postrecompression attacks performed to cover or compromise original inpainting traces. To date, there is no effective way to detect inpainting image forgery under combined recompression attacks. To fill such a gap in image forensics and reveal inpainting forgery from the post-recompression attacks in JPEG images, we propose in this paper an approach that begins with large feature mining in discrete transform domain, ensemble learning is then applied to deal with the high feature dimensionality and to prevent the overfitting that generally happens to some regular classifiers under high feature dimensions. Our study shows the proposed approach effectively exposes inpainting forgery under post recompression attacks; especially, it noticeably improves the detection accuracy while the recompression quality is lower than the original JPEG image quality, and thus bridges a gap in image forgery detection.

studied [5, 8, 14, 17, 19, 21, 24]. For example, double JPEG compression is one of the most adopted manipulations: while one decodes the bit stream of a JPEG image and implements the manipulation in spatial domain, and then compresses the modified image back to JPEG format, if the newly adopted quantization table is different from the one used by original JPEG image, the modified JPEG image is said to have undergone a double JPEG compression. Although JPEG based double compression does not by itself prove malicious or unlawful tampering, it is an evidence of image manipulation. The detection of double JPEG compression has been widely studied [5, 8,14,19]. To this date, although the detection is very effective while the second compression quality is higher than the first compression quality, it is not so good while the second is lower than the first. . A crafty forgery maker may exploit the weakness of the current detection methods, manipulate the images and produce the forgery at a lower quality level, to prevent it from being detected. In addition to JPEG-double compression, the detection of several popular tampering types, including region duplication (copy paste and the variants), image splicing, and seam carving-based forgery, has been successfully conducted [17, 20, 24].

Keywords— inpainting, combination recompression, image forgery detection

Although significant progresses have been made in image forgery detection, inpainting, also known as image completion, aiming to reconstruct lost or corrupted parts of images and videos, has been ignored to great extents. There are many applications of this technique, ranging from film restoration, deterioration reverse, to image and video editing and restoration, including but not limited to removal of occlusions, such as texts, subtitles, stamps, logos, watermarks, wrinkles, and unwanted objects from digital images and/or videos [1, 11, 26].

I.

forgery,

JPEG,

INTRODUCTION

In computer vision and media forensics, the detection of forgery on joint photographic experts group (JPEG) images is an interesting and challenging work. As a standardized lossy compression, JPEG is the most popular digital image format in use; JPEG image-based forensics has therefore become one of the hot topics in multimedia forensics. In terms of the manipulation of JPEG image forgery, the tampering generally involves several basic operations, such as image resizing, rotation, splicing, double compression. The detection of these fundamental manipulations and relevant forgery has been well 978-1-5090-6167-9/16 $31.00 © 2016 IEEE DOI 10.1109/ICMLA.2016.93

Most inpainting methods in the literature can be mainly classified into geometry- and texture-oriented methods [1]. Geometry-oriented methods are performed by using a partial differential equation (PDE), derived from variation principles 164

they claimed that “the proposed method can detect and locate the inpainting-tampered region efficiently and accurately when an inpainting image is saved in an uncompressed format or in JPEG compressed format of higher quality than the original JPEG image quality” [28], it is clear that the forgery detection method essentially makes use of the detection of the JPEG double compression that the second compression quality is higher than the first (original) compression quality, the detection of the inpainting forgery on the same quantization is still not available, leaving alone the combination attacks, for instance, the doctored image after inpainting is encoded in JPEG with the same quantization table with the one before inpainting, then filtered, and resaved at lower image qualitylevel, etc.

[22], showing good performance in propagating smooth level lines or gradients, but undesirable in the presence of texture. Geometry-oriented methods are local in the sense the PDEs only involve the interactions among neighboring pixels on the image grid [2]. Texture-oriented methods model texture as a probabilistic graphical model [16], which has been extensively used for inpainting [11, 23]. These methods are called exemplar-based approaches. The authors in [6] combined copy-paste texture synthesis, geometric PDEs and coherence among neighboring pixels and proposed a comprehensive framework for image inpainting, being able to approximately minimize proposed energy function. Several inpainting tools are currently available on the Internet, and cyber criminals may easily obtain these inpainting tools to disguise objects and conceal the truth of digital photos, which might then be presented as authentic for various purposes. Therefore, there is a heightened need for effective methods to uncover such tampering in digital JPEG images since the study of detecting inpainting-based forgery in digital images, especially those followed by JPEG recompression attacks, has barely been conducted.

Generally, after inpainting manipulation, the postcombination attacks aim to cover or compromise original inpainting traces. It is very hard to model the processing by inpainting followed by these attacks. To date, no literature is available to exposing the inpainting forgery from these subsequent combination attacks. To solve this open problem and fill such a gap in image forensics, in this paper, we conduct an empirical study by a large feature mining in the Discrete Cosine Transform (DCT) domain. Judicious learning machine and statistical metrics are employed. Our study shows that proposed approach is effective. The remainder of this paper is organized in this way: Section 2 discusses the problems in inpainting forgery under post-recompression attacks. Section 3 describes our approach, followed by experiments and discussions in section 4. Conclusions are made in section 5.

In image forensics, the detection of copy-paste or copymove forgery has been widely investigated. It is possible to adopt the detection of copy-move forgery for inpainting forgery detection although the challenging level of inpainting detection is much higher than copy-move forgery detection. Christlein et al. [9] examined the 15 most prominent feature sets for copy-move forgery detection. In a number of experiments, their results show that “a keypoint-based method, e. g. based on SIFT features, can be very efficiently executed. Its main advantage is the remarkably low computational load, combined with good performance. Keypoint-based methods, however, are sensitive to “low-contrast regions and repetitive image content”, and “block-based methods can clearly improve the detection results”. Among block-based methods, Christlein et al. [9] recommended the use of ZERNIKE [24]. Additionally, a fast copy-move forgery detection [17] was notable for itsvery low computational cost and good detection performance.

II. PROBLEM STATEMENT As a common procedure in JPEG image tampering, JPEG double compression has been widely investigated. Generally, while the second compression quality is higher than the first compression quality, that is, the quantization steps at the second compression are smaller than the quantization steps at the first compression, the detection of the JPEG double compression performs very well. However, while the second compression quality is lower than the first compression quality (we called JPEG down recompression), most detection methods are underperformed.

In inpainting forgery detection, Das et al. [12] proposed a method based on zero-connectivity feature and fuzzy membership. Chang et al. [7] designed a detection based on multi-region relation. Cozzolino et al. [10] utilized the computation of a dense motion field by PatchMatch-based detector algorithm [3]. Trung et al. [25] argued that these methods “either are complex or unreliable” and they presented a blind inpainting detection method by similar patches with the aid of KD-tree algorithm [4] and located inpainting forgery by the centroid connected component. However, Trung et al. did not clearly present whether their detection method is effective for exposing the inpainting forgery from compressed tampering images.

The experimental results in detecting JPEG double compression, conducted by Bianchi and Piva [5] with the use of three methods [5, 8, 21] show that the detection of JPEG down-recompression are not effective, especially when the first compression quality is considerably higher than the second compression quality, for example, when the first compression quality factor is higher than 77 and the second compression factor is lower than 57, all values of detection accuracy are lower than 60% and barely above 50%. By merging marginal density and neighboring joint density in DCT domain, we developed a feature mining-based method to detect JPEG double compression and compared the detection method and a Markov-process-based approach and demonstrated the superiority of the integration of marginal density and joint density. Unfortunately, the study of the detection of misaligned JPEG recompression was not

Although the authors in [7, 27] proposed methods to detect inpainting tampering, these detection are not effective while the doctored image is compressed and stored in JPEG format due to JPEG compression leading to the modification of the pixel values in the tampering area. The authors in [28] developed the detection of JPEG-based inpainting forgery, as

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domain. Before we present our approach, we first discuss the JPEG down recompression while the second recompression quality is lower than the original JPEG compression quality. While JPEG compression is performed to a never compressed image, the DCT coefficients before quantization and after quantization are denoted pcmn and cmn respectively at the frequency coordinate (m, n) (m=0, 1…7; n=0, 1…7), the

conducted. Meanwhile, although the detection of JPEG downrecompression has been noticeably improved, detection under certain conditions still leaves room for improvement; for instance, the detection accuracy is only 82.5% to detect the down-recompression while the first compression quality is 90 and the second compression quality is 40 [19]. To detect double JPEG compression with the same quantization matrix, Huang et al. [14] designed a method based on an observation, that is, in the process of recompressing a JPEG image with the same quantization matrix over and over again, the number of different JPEG coefficients between the two consecutive versions will monotonically decrease in general. There are, however, serious flaws with the method. Firstly, the observation is far from being always true. For example, by using the image database in the references [18, 20], with the use of the same quantization matrix, there is not any difference of the quantized DCT coefficients between the first compression and the second compression among 20% JPEG images. In other words, the foundation of the detection method designed in [14] may not be statistically sound. Secondly, the detection cannot tell us whether the doubly compressed images were tampered or not.

( 2 ) , and quantization step is denoted Qmn

§ pc · ¸ cmn = R¨¨ (mn 2) ¸ © Qmn ¹

(1)

We have

§ pcmn · pcmn (2 ) − R ¨ ¨ Q (2 ) ¸¸ < 0.5 , or Qmn © mn ¹ (2 ) (cmn − 0.5)Qmn ≤ pcmn < (cmn + 0.5)Qmn(2 ) − 0 .5 ≤

As for down-recompression, the quantization steps at the (1) and ( 2 ) first and the second compression are denoted by Qmn Qmn

To date, methods to expose inpainting forgery under subsequent attacks that aim to remove or compromise the existing inpainting traces are still not available, which will be solved by our proposed hybrid large feature mining-based approach. In this study, we target the detection of the inpainting forgery with the following combined attacks:

(1) < ( 2 ) ). Without losing generality, suppose c ( Qmn Qmn mn is positive, the quantized DCT coefficient after down-

recompression is denoted by

(2 ) cmn , here we ignore the truncate

error,

1) Inpainting manipulation is applied to original JPEG image with the compression quality QF1, the doctored image is saved in JPEG format at the same quality of QF1;

(1) · § § pcmn · Qmn ¨ ¨ ¸ cmn = R¨ R¨ (1) ¸ • (2 ) ¸¸ © © Qmn ¹ Qmn ¹ § pc · (1) ¸ = R¨¨ (mn cmn 1) ¸ © Qmn ¹ (2 )

2) Inpainting manipulation is applied to original JPEG image with the compression quality QF1, the doctored image is firstly saved in JPEG format at the same quality of QF1, then resaved in JPEG format at a lower quality of QF2 (QF1>QF2);

Let

(2)

Then,

§ c(1)Q(1) · (2 ) ¸ = R¨¨ mn (2mn cmn ) ¸ © Qmn ¹

3) Inpainting manipulation is applied to original JPEG image with the compression quality QF1, the doctored image is firstly saved in JPEG format at the same quality of QF1, then image filtering is applied and the filtered version is resaved in JPEG format at a lower quality of QF2 (QF1>QF2);

(3)

(

)

(1) (1) (2) (1) (1) ­cmn −1, cmn Qmn < (cmn −0.5)Qmn ≤ pcmn < cmn + 0.5 Qmn ° (2) (1) (1) ( 2) (cmn −0.5)Qmn(2) ≤ cmn cmn Qmn < (cmn + 0.5)Qmn = ® cmn, °c +1, c(1) −0.5 Q(1) ≤ pc < c + 0.5 Q(2) ≤ c(1) Q(1) mn mn mn mn mn mn mn ¯ mn

4) Inpainting manipulation is applied to original JPEG image with the compression quality QF1, the doctored image is firstly saved in JPEG format at the same quality of QF1, then image resampling (down scale) is applied and the rescaled (down scaled) image is resaved in JPEG format at a lower quality of QF2 (QF1>QF2). III.

R(•) denotes the round

function.

(

)

(

(4)

)

As indicated by equation (4), compared to the single compression, the JPEG down-recompression may change the value of the DCT coefficient by 1, although the modification ratio is small. As a result, the marginal density of the DCT coefficients at each individual frequency and the neighboring joint density will be modified. Generally JPEG down recompression and relevant operations including inpainting modify the pixel values of original image, and hence changes the original DCT coefficients. As a result, inpainting also changes the individual frequency-based marginal density and joint density. Based on our previous study in steganalysis, the analytic results in equation (4) is similar to the embedding ±1 to quantized DCT

METHODOLODGY

Although it is very hard to model the inpainting with combined attacks, one may start with the heuristic used in previous work in steganlaysis and forgery detection [13, 15, 18, 20] that in general, the manipulations modify the pixel values and transform coefficients. To explore the detection of inpainting forgery under combined attacks, we propose an empirical approach with large feature mining in the transform

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2)

coefficients in steganography production, we design the following rich features to detect inpainting forgery under JPEG recompression attacks. The quantized DCT coefficient array of the image contains B1×B2 blocks. The Fpq-th block (p = 1, 2, …, B1; q = 1, 2, …, B2) consists of 8×8 quantized DCT coefficients. The coefficient at the frequency coordinate (u, v) of the block Fpq (u=0,1, …,7, and v=0,1,…,7) is denoted by c pq (u, v) . The

format at the same quality factor; 3) Extract the marginal density and neighboring joint density features calculated by equations (5) to (9) respectively. In the DCT domain, we extract 64*(84+700+700) = 94,976 features, denoted by LF-DCT (large features in DCT transform).

marginal density of the absolute coefficients is given by absM. B1

B2

¦¦ δ (c absM ( u, v; h ) =

pq ( u , v )

p =1 q =1

=h

IV.

(5)

The individual frequency-based neighboring joint density on horizontal direction and vertical direction are given by: B1

absNJ 1h (u, v; x, y ) =

pq

p =1 q =1

(u, v ) = x & c pq (u, v + 1) = y ) B1 B 2

B1

absNJ 1v (u, v; x, y ) =

B2

¦¦ δ (c B2

¦¦ δ (c

pq

p =1 q =1

(u, v ) = x & c pq (u + 1, v ) = y ) B1 B 2

(6)

(7)

The inter-block neighboring joint density on individual frequency band along the horizontal and vertical direction, the features are constructed as follows: B1 B2 −1

absNJ 2h (u, v; x, y ) =

¦ ¦ δ (c

pq

p =1 q =1

B1 (B 2 − 1)

B1 −1 B2

absNJ 2 v (u, v; x, y ) =

¦¦ δ (c p =1 q =1

(u, v ) = x & c p ( q +1) (u, v ) = y )

pq

(u, v ) = x & c ( p +1) q (u, v ) = y ) (B1 − 1)B 2

EXPERIMENTS

After comparing several open source tools on image inpainting, we select the Teorex inpainting tool at http://www.theinpaint.com/ for tampering since it delivers the best inpainting outcomes without any perceivable distortion. We designed different types of combination attack experiments, described in the following. Combination attack 1: Original images are in JPEG format at the quality of ‘75’ and resaved in JPEG format at the quality of ‘75’ (untouched). After applying inpainting to the original images in JPEG format at the quality of ‘75’, the doctored images are saved in JPEG format at the same quality factor of ‘75’ (forgery). Combination attack 2: Original images are in JPEG format at the quality of ‘75’, and resaved in JPEG format at the quality of ‘75’, and finally resaved in JPEG format at the quality “40’ (untouched). After applying inpainting to the original images in JPEG format at the quality of ‘75’, the doctored images are saved in JPEG at the quality of ‘75’, and then resaved in JPEG format at the quality of ‘40’ (forgery). Combination attack 3: Original images are in JPEG format at the quality of ‘75’, resaved in JPEG format at the quality of ‘75’, followed by down-scaling, and finally stored in JPEG format at the quality “40’ (untouched). After applying inpainting to the original images in JPEG format at the quality of ‘75’, the doctored images are saved in JPEG at the quality of ‘75’, followed by down-scaling and then stored in JPEG at the quality of ‘40’ (forgery). Combination attack 4: Original images are in JPEG format at the quality of ‘75’, resaved in JPEG format at the quality of ‘75’, followed by image filtering, and finally stored in JPEG format at the quality “40’ (untouched). After applying inpainting to the original images in JPEG format at the quality of ‘75’, the doctored images are saved in JPEG at the quality of ‘75’, followed by image filtering and then stored in JPEG at the quality of ‘40’ (forgery). Image median filtering was adopted in this attack. In attack 1 and 2, we conducted two different sizes of images: a) 256×256, 6000 untouched and 6000 tampered images; and b) 128×128, 12300 untouched and 12300 tampered. In attacks 3 and 4, we only examined 6000 untouched and 6000 tampered images on the size of 256×256. The goal is to discriminate the forgery from the untouched under these different combination attacks. Since no other methods have been established to detect such inpainting forgery under combination attacks that aim to compromise or cover original inpainting forgery, we compare the proposed detectors LF-DCT to absNJ [18], CC-absNJ [20], and

)

B1 B2

Compress the shifted spatial image M d1d 2 to the JPEG

(8)

(9)

In equations (5) to (9), į = 1 if its arguments are satisfied, otherwise į = 0; h is the integer from 0 to 5, x and y are integers ranging from 0 to 4. The frequency coordinate pair (u, v) is set to (0,1), (1,0), (2,0), (1,1), (0,2), (0,3), (1,2), (2,1), (3,0), (4,0), (3,1), (2,2), (1,3), and (0,4), a subtotal of 84 marginal density features in equation (5), 700 joint density features in equations (6) and (7) on the intra-block, and 700 joint density features in equations (8) and (9) on the interblock. The calibration features in the DCT domain is generated according to the following processing: Decode the JPEG image under examination to spatial domain, which is denoted by matrix M. For d1= 0 to 7, and d2 = 0 to 7, while (d1,d2) (0,0): 1) Crop the matrix M by d1 rows and d2 columns in the spatial domain, and generate a shifted spatial image M d1d 2 (d1, d2) ∈ {(0,1),..., (0,7 ), (1,0 ),..., (7,7 )} ;

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Markov-Y/Cb/Cr detectors for color images [29] and the combinations. Table I lists the mean accuracy over 30 experiments by applying the ensemble classifier [18] in attack 1. Table II lists the mean detection accuracy over 30 experiments in attack 2. Table III lists the mean accuracy over 30 experiments in attack 3 and Table IV list the mean accuracy over 30 experiments in attack 4. In each experiment, 50% observations are randomly selected for training and other 50% observations are used for testing. The outcomes of testing are classified as True Positive (TP), False Positive (FP), False Negative (FN), and True Negative (TN). The detection accuracy is calculated by 0.5*TP/(TP+FN)+0.5*TN/(TN+FP). The experimental results show that our proposed large feature mining-based detectors considerably outperform other detectors and effectively expose the inpainting forgery under the combined attacks.

Markov-Cr

Markov-Cb

Markov-CrCb

MarkovYCrCb

LF-DCT

Table I. Detection accuracy (%) in attack 1 (doctored image stored at the same quality ‘75’) Detector

Mean detection accuracy 256×256 128×128 73.2 68.1 77.3 75.3 59.1 58.2 70.3 66.6 66.3 64.9 71.5 68.2 73.5 69.0 98.2 87.1

absNJ CC-absNJ Markov-Y Markov-Cr Markov-Cb Markov-CrCb Markov-YCrCb LF-DCT

Table IV. Detection accuracy (%) in attack 4 (forgery saved in JPEG at quality ‘75’, followed by median filter then in JPEG quality ‘40’) Detector absNJ CC-absNJ Markov-Y Markov-Cr Markov-Cb Markov-CrCb Markov-YCrCb LF-DCT

Table II. Detection accuracy (%) in attack 2 (doctored images saved in JPEG at quality ‘75’ and then restored in JPEG at the quality ‘40’)

absNJ CC-absNJ Markov-Y Markov-Cr Markov-Cb Markov-CrCb Markov-YCrCb LF-DCT

256×256 69.7 72.2 61.5 70.0 67.9 70.3 70.9 97.6

V.

128×128 69.9 70.6 60.7 66.9 66.4 66.9 67.5 90.3

CC-absNJ

Markov-Y

Scale factor 0.5 0.6 0.7 0.8 0.5 0.6 0.7 0.8 0.5 0.6 0.7 0.8

CONCLUSIONS

We proposed an empirical approach with large feature mining, containing the marginal density and joint density features, for a total of almost 100,000 features from the DCT domain and the calibrated versions. Ensemble learning is then employed for the detection, in order to deal with the high dimensionality and avoid the overfitting that happens to some regular classifiers. Our approach effectively exposes inpainting forgery under combined attacks and bridges the gap in image forgery detection under post recompression attacks.

Table III. Detection accuracy (%) in attack 3 (forgery saved in JPEG at quality ‘75’, followed by down-scaling then in JPEG at quality ‘40’)

absNJ

Detection accuracy 71.9 73.9 62.8 70.0 68.0 70.6 70.7 97.4

Detection accuracy over the two sizes

Detector

Detector

70.8 70.3 70.1 70.1 69.1 69.0 68.7 68.5 71.0 71.1 70.3 70.6 71.8 71.5 71.1 71.3 98.6 78.6 79.4 79.3

0.5 0.6 0.7 0.8 0.5 0.6 0.7 0.8 0.5 0.6 0.7 0.8 0.5 0.6 0.7 0.8 0.5 0.6 0.7 0.8

ACKNOWLEDGMENT

Detection accuracy 71.4 68.9 69.2 69.2 72.4 69.7 69.9 69.7 62.1 61.4 61.1 60.8

The support for this study from NSF under the award 1318688 is highly appreciated. REFERENCES [1]

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