Anti-JPEG Compression Steganography Based on the High Tense Region Locating Method

Robust data hiding techniques attempt to construct covert communication in a lossy public channel.Nowadays,the existing robust JPEG steganographic algorithms cannot overcome the side-information missing situation.Thus,this paper proposes a new robust JPEG steganographic algorithm based on the high tense region location method which needs no side-information of lossy channel.First,a tense region locating method is proposed based on the Harris-Laplacian feature point.Then,robust cover object generating processes are described.Last,the advanced embedding cost function is proposed.A series of experiments are conducted on various JPEG image sets and the results show that the proposed steganographic algorithm can resist JPEG compression efficiently with acceptable performance against steganalysis statistical detection libraries GFR(Gabor Filters Rich model)and DCTR(Discrete Cosine Transform Residual).

Steganography Based on BaselineSequential JPEG Compression

Information hiding in Joint Photographic Experts Group （JPEG） compressed images are investigated in this paper. Quantization is the source of information loss in JPEG compression process. Therefore, information hidden in images is probably destroyed by JPEG compression. This paper presents an algorithm to reliably embed information into the JPEG bit streams in the process of JPEG encoding. Information extraction is performed in the process of JPEG decoding. The basic idea of our algorithm is to modify the quantized direct current （DC） coefficients and non zero alternating currenl （AC） coefficients to represent one bit information （0 or 1 ）. Experimental results on gray images using baseline sequential JPEG encoding show that the cover images （images without scoret information） and the stego-images （images with secret information） are perceptually indiscernible.

An Overview of Double JPEG Compression Detection and Anti-detection

JPEG(Joint Image Experts Group)is currently the most widely used image format on the Internet.Existing cases show that many tampering operations occur on JPEG images.The basic process of the operation is that the JPEG file is first decompressed,modified in the null field,and then the tampered image is compressed and saved in JPEG format,so that the tampered image may be compressed several times.Therefore,the double compression detection of JPEG images can be an important part for determining whether an image has been tampered with,and the study of double JPEG compression anti-detection can further advance the progress of detection work.In this paper,we mainly review the literature in the field of double JPEG compression detection in recent years with two aspects,namely,the quantization table remains unchanged and the quantization table is inconsistent in the double JPEG compression process,Also,we will introduce some representative methods of double JPEG anti-detection in recent years.Finally,we analyze the problems existing in the field of double JPEG compression and give an outlook on the future development direction.

Target acquisition performance in the presence of JPEG image compression

This paper presents an investigation on the effect of JPEG compression on the similarity between the target image and the background,where the similarity is further used to determine the degree of clutter in the image.Four new clutter metrics based on image quality assessment are introduced,among which the Haar wavelet-based perceptual similarity index,known as HaarPSI,provides the best target acquisition prediction results.It is shown that the similarity between the target and the background at the boundary between visually lossless and visually lossy compression does not change significantly compared to the case when an uncompressed image is used.In future work,through subjective tests,it is necessary to check whether this presence of compression at the threshold of just noticeable differences will affect the human target acquisition performance.Similarity values are compared with the results of subjective tests of the well-known target Search_2 database,where the degree of agreement between objective and subjective scores,measured through linear correlation,reached a value of 90%.

Detecting Double JPEG Compressed Color Images via an Improved Approach

Detecting double Joint Photographic Experts Group (JPEG) compressionfor color images is vital in the field of image forensics. In previousresearches, there have been various approaches to detecting double JPEGcompression with different quantization matrices. However, the detectionof double JPEG color images with the same quantization matrix is stilla challenging task. An effective detection approach to extract features isproposed in this paper by combining traditional analysis with ConvolutionalNeural Networks (CNN). On the one hand, the number of nonzero pixels andthe sum of pixel values of color space conversion error are provided with 12-dimensional features through experiments. On the other hand, the roundingerror, the truncation error and the quantization coefficient matrix are used togenerate a total of 128-dimensional features via a specially designed CNN. Insuch aCNN, convolutional layers with fixed kernel of 1×1 and Dropout layersare adopted to prevent overfitting of the model, and an average pooling layeris used to extract local characteristics. In this approach, the Support VectorMachine (SVM) classifier is applied to distinguishwhether a given color imageis primarily or secondarily compressed. The approach is also suitable for thecase when customized needs are considered. The experimental results showthat the proposed approach is more effective than some existing ones whenthe compression quality factors are low.

Detecting JPEG image forgery based on double compression

Detecting the forgery parts from a double compressed image is very important and urgent work for blind authentication. A very simple and efficient method for accomplishing the task is proposed. Firstly, the probabilistic model with periodic effects in double quantization is analyzed, and the probability of quantized DCT coefficients in each block is calculated over the entire iraage. Secondly, the posteriori probability of each block is computed according to Bayesian theory and the results mentioned in first part. Then the mean and variance of the posteriori probability are to be used for judging whether the target block is tampered. Finally, the mathematical morphology operations are performed to reduce the false alarm probability. Experimental results show that the method can exactly locate the doctored part, and through the experiment it is also found that for detecting the tampered regions, the higher the second compression quality is, the more exact the detection efficiency is.

Detecting Shifted Double JPEG Compression Tampering Utilizing Both Intra-Block and Inter-Block Correlations

Copy-paste forgery is a very common type of forgery in JPEG images.The tampered patch has always suffered from JPEG compression twice with inconsistent block segmentation.This phenomenon in JPEG image forgeries is called the shifted double JPEG(SDJPEG) compression.Detection of SDJPEG compressed image patches can make crucial contribution to detect and locate the tampered region.However,the existing SDJPEG compression tampering detection methods cannot achieve satisfactory results especially when the tampered region is small.In this paper,an effective SDJPEG compression tampering detection method utilizing both intra-block and inter-block correlations is proposed.Statistical artifacts are left by the SDJPEG compression among the magnitudes of JPEG quantized discrete cosine transform(DCT) coefficients.Firstly,difference 2D arrays,which describe the differences between the magnitudes of neighboring JPEG quantized DCT coefficients on the intrablock and inter-block,are used to enhance the SDJPEG compression artifacts.Then,the thresholding technique is used to deal with these difference 2D arrays for reducing computational cost.After that,co-occurrence matrix is used to model these difference 2D arrays so as to take advantage of second-order statistics.All elements of these co-occurrence matrices are served as features for SDJPEG compression tampering detection.Finally,support vector machine(SVM) classifier is employed to distinguish the SDJPEG compressed image patches from the single JPEG compressed image patches using the developed feature set.Experimental results demonstrate the efficiency of the proposed method.

Passive detection of copy-paste forgery between JPEG images

A blind digital image forensic method for detecting copy-paste forgery between JPEG images was proposed.Two copy-paste tampering scenarios were introduced at first:the tampered image was saved in an uncompressed format or in a JPEG compressed format.Then the proposed detection method was analyzed and simulated for all the cases of the two tampering scenarios.The tampered region is detected by computing the averaged sum of absolute difference(ASAD) images between the examined image and a resaved JPEG compressed image at different quality factors.The experimental results show the advantages of the proposed method:capability of detecting small and/or multiple tampered regions,simple computation,and hence fast speed in processing.

CNN-Based Forensic Method on Contrast Enhancement with JPEG Post-Processing

As one of the most popular digital image manipulations,contrast enhancement(CE)is frequently applied to improve the visual quality of the forged images and conceal traces of forgery,therefore it can provide evidence of tampering when verifying the authenticity of digital images.Contrast enhancement forensics techniques have always drawn significant attention for image forensics community,although most approaches have obtained effective detection results,existing CE forensic methods exhibit poor performance when detecting enhanced images stored in the JPEG format.The detection of forgery on contrast adjustments in the presence of JPEG post processing is still a challenging task.In this paper,we propose a new CE forensic method based on convolutional neural network(CNN),which is robust to JPEG compression.The proposed network relies on a Xception-based CNN with two preprocessing strategies.Firstly,unlike the conventional CNNs which accepts the original image as its input,we feed the CNN with the gray-level co-occurrence matrix(GLCM)of image which contains CE fingerprints,then the constrained convolutional layer is used to extract high-frequency details in GLCMs under JPEG compression,finally the output of the constrained convolutional layer becomes the input of Xception to extract multiple features for further classification.Experimental results show that the proposed detector achieves the best performance for CE forensics under JPEG post-processing compared with the existing methods.

Quantization-Based Robust Image Watermarking Using the Dual Tree Complex Wavelet Transform

Conventional quantization index modulation (QIM) watermarking uses the fixed quantization step size for the host signal.This scheme is not robust against geometric distortions and may lead to poor fidelity in some areas of content.Thus,we proposed a quantization-based image watermarking in the dual tree complex wavelet domain.We took advantages of the dual tree complex wavelets (perfect reconstruction,approximate shift invariance,and directional selectivity).For the case of watermark detecting,the probability of false alarm and probability of false negative were exploited and verified by simulation.Experimental results demonstrate that the proposed method is robust against JPEG compression,additive white Gaussian noise (AWGN),and some kinds of geometric attacks such as scaling,rotation,etc.

Analytic Beta-Wavelet Transform-Based Digital Image Watermarking for Secure Transmission

The rapid development in the information technology field has introduced digital watermark technologies as a solution to prevent unauthorized copying and redistribution of data.This article introduces a self-embedded image verification and integrity scheme.The images are firstly split into dedicated segments of the same block sizes.Then,different Analytic Beta-Wavelet(ABW)orthogonal filters are utilized for embedding a self-segment watermark for image segment using a predefined method.ABW orthogonal filter coefficients are estimated to improve image reconstruction under different block sizes.We conduct a comparative study comparing the watermarked images using three kinds of ABW filters for block sizes 64×64,128×128,and 256×256.We embed the watermark using the ABW-based image watermarking method in the 2-level middle frequency sub-bands of the ABW digital image coefficients.The imperceptibility and robustness of the ABW-based image watermarking method image is evaluated based on the Peak Signal to Noise Ratio(PSNR)and Correlation coefficient values.From the implementation results,we came to know that this ABW-based image watermarking method can withstand many image manipulations compared to other existing methods.

Wavelet-based gray-level digital image watermarking

The watermarking technique has been proposed as a method by hiding secret information into the image to protect the copyright of multimedia data. But most previous work focuses on the algorithms of embedding one dimensional watermarks or two dimensional binary digital watermarks. In this paper, a wavelet based method for embedding a gray level digital watermark into an image is proposed. By still image decomposition technique, a gray level digital watermark is decompounded into a series of bitplanes. By discrete wavelet transform ( DWT ), the host image is decomposed into multiresolution representations with hierarchical structure. The different bitplanes of the gray level watermark is embedded into the corresponding resolution of the decomposed host image. The experimental results show that the proposed techniques can successfully survive image processing operations and the lossy compression techniques such as Joint Photographic Experts Group (JPEG).

Degradation algorithm of compressive sensing

The compressive sensing （CS） theory allows people to obtain signal in the frequency much lower than the requested one of sampling theorem. Because the theory is based on the assumption of that the location of sparse values is unknown, it has many constraints in practical applications. In fact, in many cases such as image processing, the location of sparse values is knowable, and CS can degrade to a linear process. In order to take full advantage of the visual information of images, this paper proposes the concept of dimensionality reduction transform matrix and then se- lects sparse values by constructing an accuracy control matrix, so on this basis, a degradation algorithm is designed that the signal can be obtained by the measurements as many as sparse values and reconstructed through a linear process. In comparison with similar methods, the degradation algorithm is effective in reducing the number of sensors and improving operational efficiency. The algorithm is also used to achieve the CS process with the same amount of data as joint photographic exports group （JPEG） compression and acquires the same display effect.

A digital authentication watermarking scheme for JPEG images with superior localization and security

The drawbacks of the current authentication watermarking schemes for JPEG images, which are inferior localization and the security flaws, are firstly analyzed in this paper. Then, two counterfeiting attacks are conducted on them. To overcome these drawbacks, a new digital authentication watermarking scheme for JPEG images with superior localization and security is proposed. Moreover, the probabilities of tamper detection and false detection are deduced under region tampering and collage attack separately. For each image block, the proposed scheme keeps four middle frequency points fixed to embed the watermark, and utilizes the rest of the DCT coefficients to generate 4 bits of watermark information. During the embedding process, each watermark bit is embedded in another image block that is selected by its corresponding secret key. Since four blocks are randomly selected for the watermark embedding of each block, the non-deterministic dependence among the image blocks is established so as to resist collage attack completely. At the receiver, according to judging of the extracted 4 bits of watermark information and the corresponding 9-neighbourhood system, the proposed scheme could discriminate whether the image block is tampered or not. Owing to the diminishing of false detection and the holding of tamper detection, we improve the accuracy of localization in the authentication process. Theoretic analysis and simulation results have proved that the proposed algorithm not only has superior localization, but also enhances the systematic security obviously.

Wavelet Based Image Authentication and Recovery

In this paper, we propose a secure semi-fragile watermarking technique based on integer wavelet transform with a choice of two watermarks to be embedded. A self-recovering algorithm is employed, that hides the image digest into some wavelet subbands for detecting possible illicit object manipulation undergone in the image. The semi-fragility makes the scheme tolerant against JPEG lossy compression with the quality factor as low as 70%, and locates the tampered area accurately. In addition, the system ensures more security because the embedded watermarks are protected with private keys. The computational complexity is reduced by using parameterized integer wavelet transform. Experimental results show that the proposed scheme guarantees safety of a watermark, recovery of image and localization of tampered area.