JPEG Steganography Research Articles

Improved PHARM for JPEG Steganalysis: Making PHARM More Efficient and Effective

The PHase-Aware pRojection Model (PHARM) feature set, built as phase-aware histograms of quantized projections obtained by convolving residuals with random matrices, achieves competitive detection performance against modern adaptive JPEG steganography while having significant computational cost. In this paper, we propose three improvements to the original PHARM, making it more efficient and effective. First, we reduce the maximum projection matrix size to decrease the computational complexity of convolution and better capture steganographic embedding changes. Second, we select more than one phase pair per projection to compute phase-aware histograms, thus correspondingly reducing the number of projections for each residual. Third, the transposition symmetry is also taken into consideration to make our features more robust while preserving the feature dimensionality. The numerous experiments are given to demonstrate the efficiency and effectiveness of our improved PHARM.

Asymmetric Distortion Function for JPEG Steganography Using Block Artifact Compensation

This article describes how the existing distortion functions for JPEG steganography allot same cost for ±1 embedding changes. Because of the correlation of natural image, however, changes with different polarities make different influences on image. Therefore, the embedding costs for ±1 embedding changes should not be equivalent. This article proposes a general method to distinguish the embedding costs for different polarities of embedding changes for JPEG images with the help of reference images constructed by block artifact compensation. The original JPEG image is decompressed into spatial domain firstly, and then the block artifact is compensated by smoothing filtering implemented on border pixels of each 8 × 8 block. After that, the compensated image which is more similar to the original uncompressed image is recompressed into DCT domain and adopted as side information to guide the adjusting of the given distortion function. Experiment results show that after the proposed method is employed, the security performance of current popular JPEG steganographic methods is observably increased.

Colorization-based image coding using graph Fourier transform

This paper deals with the colorization-based image coding algorithm. In this algorithm, a color image is compressed by encoding its luminance image by a standard coding method such as JPEG coding and by storing several color pixels called as representative pixels (RPs). In decoding phase, a color image is restored from a luminance image and some color information of RPs using the image colorization technique. While previous studies have achieved a high coding performance, the compression method of RPs has not been considered because the positions of RPs are inhomogeneous. In order to improve the image coding performance, this paper proposes the RPs compression algorithm using the graph Fourier transform, where the chrominance image is transformed to graph spectrum and compressed. Using this RPs compression algorithm and a colorization technique, a new colorization-base image coding algorithm is proposed. Numerical results show that the proposed algorithm achieves better performance than some previous studies and JPEG2000 coding.

A New Distortion Function Design for JPEG Steganography Using the Generalized Uniform Embedding Strategy

Nowadays, the most prevailing approach to steganography is the minimal embedding distortion framework, which includes an optimizable distortion function for each cover element and an encoding method to minimize the distortion. With the emergence of Syndrome-Trellis Code, the distortion function plays an increasingly important role in modern adaptive image steganography. In this letter, a new distortion function called generalized uniform embedding distortion (GUED) is proposed for JPEG steganography. The proposed GUED consists of the new distortion measures for both Alternating Current (AC) mode and Discrete Cosine Transform (DCT) block, which are represented in a more general exponential model, aiming to flexibly allocate the embedding data so as to minimize the global changes of the statistics of quantized DCT coefficients after embedding. In addition, an empirical rule is developed to determine the parameters of the exponential function according to the payload and quality factor. By exploring the statistics of both DCT and spatial domains, the proposed GUED is shown to be more consistent with the objective of generalized uniform embedding strategy, i.e., maintaining the relative changes of DCT coefficients to be proportional to their coefficients of variations. Extensive experiments demonstrate that the proposed GUED gains significant performance improvements when compared with its original UERD, and outperforms the state-of-the-art J-UNIWARD with markedly reduced computation time.

An efficient multi balanced cuckoo search K-means technique for segmentation and compression of compound images

The images comprise not only photographic images but also graphic and text images, they are determined in magazines, brochures and websites. The segmentation and compression of compound images (for instance, computer-generated images, scanned documents and so on) are tough to the procedure.The existing segmentation and compression techniques do not provide a complete comprehensive solution. To solve the problems in existing techniques, here we segmented the compound images via an optimization depended on K-means clustering technique along with AC (Alternate Current) coefficient method for the dynamic segmentation and then compressed individually. The AC coefficient based segmentation method results in detachment of smooth (background) and non-smooth (text, image and overlapping) areas. Further, the non-smooth part is segmented via the optimization depended on K-means clustering technique. Also, the density of segmented objects is headed applying different compression strategies such as the Huffman coder, arithmetic coder, and Jpeg coders. With the being approaches, the entire projected architecture is implemented in MATLAB and the function of the scheme is measured and equated. Our proposed system achieves better compression ratio (21.16), and also improves the performance for image quality index (0.931574), PSNR (Peak Signal to Noise Ratio) (34.91338), RMSE (Root Mean Square Error) (0.931574), SSIM (Structural Similarity) (0.546882), and SDME (Second Derivative-like Measure of Enhancement) (44.91293) than the available CS K-means algorithm.

Steganalysis of content-adaptive JPEG steganography based on scale co-occurrence matrix with diverse quantization

Feature extraction is very important for steganalysis of content-adaptive JPEG steganography. The scale co-occurrence matrix feature based on a two-dimensional (2-D) Gabor filter is proposed, and diverse quantization for filter residuals is utilized to improve the detection performance. First, the definition of scale co-occurrence matrix based on a 2-D Gabor filter is given and the rules for feature merge are analyzed. Then, the influence of the scale parameter and quantization step on the detection performance of the scale co-occurrence matrix feature is discussed and verified. Next, the effect of diverse quantization strategy is presented. Last, the detailed extraction process of the proposed steganalysis feature is described. The experimental results show that the proposed steganalysis feature can achieve a performance that is competitive with the state-of-the-art steganalysis features when used for the detection of the latest content-adaptive JPEG steganography algorithms.

Efficient JPEG Steganography Using Domain Transformation of Embedding Entropy

Nowadays, JPEG steganographic schemes, e.g., J-UNIWARD, which take into account the effects of embedding in the spatial domain tend to exhibit higher security and introduce less artifacts that can be captured by the prevalent steganalyzers. Following the paradigm, this letter proposes a new design of the distortion measure for JPEG steganography by incorporating the statistics of both the spatial and discrete cosine transform (DCT) domains. The spatial statistics of the decompressed JPEG images are first well characterized with distortion measures of some efficient steganographic schemes in the spatial domain, e.g., HILL, and the resulting embedding entropies of spatial blocks in alignment with DCT blocks are then transformed into the DCT domain to obtain the distortion measures for JPEG steganography. Experimental results show that the proposed method outperforms considerably other state-of-the-art JPEG steganographic schemes, i.e., J-UNIWARD and UERD, for the most effective feature set GFR at present, and rivals them for other feature sets, e.g., DCTR and CC-JRM.

AC coefficient and K‐means cuckoo optimisation algorithm‐based segmentation and compression of compound images

Compound images are containing palletise regions including text or graphics and continuous tone images. The compression of compound images is a challenging function and which is complicated to achieve it without degrading the quality of the images. This document is mainly used to improve the compression ratio and an efficient segmentation method is created to separate the background image, text and graphics from the compound images for to make the compression independently. The segmentation is performed through AC coefficient-based segmentation method resulting in smooth and non-smooth regions. The non-smooth region is again segmented by means of K-means cuckoo optimisation algorithm. In the second phase, the segmented background image, text and graphics were compressed by means of arithmetic coder, Huffman coder and JPEG coder, respectively. This proposed technique is implemented in the working platform of MATLAB and the results were analysed.

Mobile Healthcare Application Development on Android OS in Cloud Computing

Health care application is implemented in pervasive devices has faced several challenges. In Android operating system development of health services is a benefit for the common people. Cloud computing data retrieval and management interoperability and availability of resources, security and privacy unified and ubiquitous access. The aim of the proposed application is to managing healthcare information system based on Cloud computing by using Android operating system. Cloud Computing provides the facility to access shared resources and common infrastructure in a ubiquitous and pervasive manner, offering services on demand over the network to perform operations that meet changing needs in mobile healthcare application. The proposed system has been implemented using Amazon's S3 cloud service, the security of the system is maintained the concept of bring your own device (BYOD) and the mobile application is developed in android operating system. Finally management of patient health records and medical images generated by the DICOM format and JPEG2000 coding.

On Improving Distortion Functions for JPEG Steganography

This paper proposes a framework to improve the existing distortion functions designed for JPEG steganography, which results in a better capability of countering steganalysis. Different from the existing steganography approach that minimizes image distortion, we minimize the feature distortion caused by data embedding. Given a JPEG image, we construct a reference image close to the image before JPEG compression. Guided by both the reference image and the feature distortion minimization, the state-of-the-art distortion functions designed for syndrome trellis coding embedding are improved by distinguishing the embedding costs for +1 versus −1 embedding. This paper has three contributions. First, the proposed framework outperforms the traditional, since we use the constructed reference image and the public steganalytic knowledge for data embedding. Second, the proposed framework is universal for improving distortion functions that were designed for JPEG steganography. Finally, experimental results also prove that the proposed approach has a better undetectability when examined by modern steganalytic tools.

Detection for JPEG steganography based on evolutionary feature selection and classifier ensemble selection

Detection for JPEG steganography based on evolutionary feature selection and classifier ensemble selection

Distortion function based on residual blocks for JPEG steganography

Steganography aims to embed additional data into digital media secretly and minimize the distortion caused by data embedding. Up to now, the key point of minimal distortion embedding framework is to design proper distortion function. In this paper, a distortion function for JPEG steganography based on residual blocks is proposed. To obtain less statistical detectability, Residual Block Values (termed as RBVs for short) and quantization steps are both involved in the proposed distortion function. RBVs are exploited to determine the embedding risk caused by modifications in corresponding DCT block while quantization steps are exploited to determine the embedding risk of the selection channel in a block. By implementing the syndrome trellis coding (STC) to embed secret data, the modifications are constrained in hard-to-detect religions. Experiments show that the proposed method performs better than current state-of-the-art methods of JPEG steganography.

Distortion Function for JPEG Steganography Based on Image Texture and Correlation in DCT Domain

ABSTRACTThis paper proposes a novel distortion function for JPEG steganography, which depends on the magnitude of discrete cosine transformation (DCT) coefficients, the first- and second-order residuals, and the amount of zeros in a DCT block. The magnitude of a DCT coefficient indicates the ability of the current coefficient to conceal modification trace. The first- and second-order residuals make sufficient use of the correlation in DCT domain, and the amount of zeros in a DCT block illustrates the block texture well. These elements are combined together to measure the risks of detection due to the modification on the cover data. By using this proposed distortion function, the embedding changes are limited in texture regions when employing the syndrome trellis coding to embed secret data. Thus less detectable artefacts could be found in stego images. The experimental results demonstrate that the proposed scheme performs less statistical detectability when comparing to current state-of-the-art steganographic methods for JPEG images.

Steganalysis of content-adaptive JPEG steganography based on Gauss partial derivative filter bank

A steganalysis feature extraction method based on Gauss partial derivative filter bank is proposed in this paper to improve the detection performance for content-adaptive JPEG steganography. Considering that the embedding changes of content-adaptive steganographic schemes are performed in the texture and edge regions, the proposed method generates filtered images comprising rich texture and edge information using Gauss partial derivative filter bank, and histograms of absolute values of filtered subimages are extracted as steganalysis features. Gauss partial derivative filter bank can represent texture and edge information in multiple orientations with less computation load than conventional methods and prevent redundancy in different filtered images. These two properties are beneficial in the extraction of low-complexity sensitive features. The results of experiments conducted on three selected modern JPEG steganographic schemes—uniform embedding distortion, JPEG universal wavelet relative distortion, and side-informed UNIWARD—indicate that the proposed feature set is superior to the prior art feature sets—discrete cosine transform residual, phase aware rich model, and Gabor filter residual.

2D Gabor filters-based steganalysis of content-adaptive JPEG steganography

To improve the detection accuracy for content-adaptive JPEG steganography, which often constrains the embedding changes to complex texture regions, a new steganalysis feature called the GRF (Gabor Rich Feature) based on two-dimensional (2D) Gabor filters is proposed. First, the diverse 2D Gabor filters are generated and used to filter the decompressed JPEG image. Second, five types of statistical features are extracted from the filtered images and these features are merged according to their respective symmetry. Third, all the features are combined and feature selection is performed to reduce dimensionality. Last, an ensemble classifier is used to assemble the steganalysis feature as well as the final steganalyzer. The experimental results show that the proposed steganalysis feature can achieve a performance that is competitive with the state-of-the-art steganalysis features when used for the detection of the latest content-adaptive JPEG steganography algorithms.

Quality and Error Robustness Assessment of Low-Latency Lightweight Intra-Frame Codecs for Screen Content Compression

Today, many existing types of video transmission and storage infrastructure are not able to handle UHD uncompressed video in real time. To reduce the required bit rates, a low-latency lightweight compression scheme is needed. To this end, several standardization efforts, such as Display Stream Compression, Advanced DSC, and JPEG XS, are currently being made. Focusing on screen content use cases, this paper provides a comparison of existing codecs suited for this field of application. In particular, the performance of DSC, VC-2, JPEG 2000 (in low-latency and low-complexity configurations), JPEG and HEVC Screen Content Coding Extension (SCC) in intra mode are evaluated. First, quality is assessed in single and multiple generations. Then, error robustness is evaluated by inserting one-bit errors at random positions in the compressed bitstreams. Unsurprisingly, the most complex algorithm, HEVC SCC intra, achieves the highest compression efficiency on screen content. JPEG 2000 performs well in the three experiments while HEVC SCC does not provide multi-generation robustness. DSC guarantees quality preservation in single generation at high bit rates and VC-2 provides very high error resilience. This work gives the reader an overview of the objective quality assessment that will be conducted as part of JPEG XS evaluation procedures.

All Phase Discrete Sine Biorthogonal Transform and Its Application in JPEG-like Image Coding Using GPU

Discrete cosine transform (DCT) based JPEG standard significantly improves the coding efficiency of image compression, but it is unacceptable event in serious blocking artifacts at low bit rate and low efficiency of high-definition image. In the light of all phase digital filtering theory, this paper proposes a novel transform based on discrete sine transform (DST), which is called all phase discrete sine biorthogonal transform (APDSBT). Applying APDSBT to JPEG scheme, the blocking artifacts are reduced significantly. The reconstructed image of APDSBT-JPEG is better than that of DCT-JPEG in terms of objective quality and subjective effect. For improving the efficiency of JPEG coding, the structure of JPEG is analyzed. We analyze key factors in design and evaluation of JPEG compression on the massive parallel graphics processing units (GPUs) using the compute unified device architecture (CUDA) programming model. Experimental results show that the maximum speedup ratio of parallel algorithm of APDSBT-JPEG can reach more than 100 times with a very low version GPU. Some new parallel strategies are illustrated in this paper for improving the performance of parallel algorithm. With the optimal strategy, the efficiency can be improved over 10%.

Hybrid distortion function for JPEG steganography

A hybrid distortion function for JPEG steganography exploiting block fluctuation and quantization steps is proposed. To resist multidomain steganalysis, both spatial domain and discrete cosine transformation (DCT) domain are involved in the proposed distortion function. In spatial domain, a distortion value is allotted for each 8×8 block according to block fluctuation. In DCT domain, quantization steps are employed to allot distortion values for DCT coefficients in a block. The two elements, block distortion and quantization steps, are combined together to measure the embedding risk. By employing the syndrome trellis coding to embed secret data, the embedding changes are constrained in complex regions, where modifications are hard to be detected. When compared to current state-of-the-art steganographic methods for JPEG images, the proposed method presents less detectable artifacts.

Improving side-informed JPEG steganography using two-dimensional decomposition embedding method

Side-informed JPEG steganography is a renowned technology of concealing information for the high resistance to blind detection. The existed popular side-informed JPEG steganographic algorithms use binary embedding method with the corresponding binary distortion function. Then, the embedding methods and binary distortion functions of popular side-informed JPEG steganographic algorithms are analyzed and the wasted secure capacity by using the binary embedding operation is pointed out. Thus, the detection resistance of the side-informed JPEG steganographic algorithms can be improved if the embedding operation is changed to ternary mode which causes less changes than binary embedding at same payload. The problem of using ternary embedding is to define a suitable ternary distortion function. To solve this, a two-dimensional decomposition embedding method is proposed in this paper. The proposed ternary distortion function is defined by transforming the problem into two different binary distortion functions of two layers that based on the ternary entropy decomposition. Meanwhile, the proposed method ensures the minimal value of the distortion function on each layer can be reached in theory. Several popular side-inform JPEG steganographic algorithms (NPQ, EBS, and SI-UNIWARD) are improved through defining ternary distortion function by the proposed method. The experimental results on parameters, blind detection and processing time show that the proposed method increases the blind detection resistance of side-informed steganographic algorithm with acceptable computation complexity.

Quality Scalability Aware Watermarking for Visual Content.

Scalable coding-based content adaptation poses serious challenges to traditional watermarking algorithms, which do not consider the scalable coding structure and hence cannot guarantee correct watermark extraction in media consumption chain. In this paper, we propose a novel concept of scalable blind watermarking that ensures more robust watermark extraction at various compression ratios while not effecting the visual quality of host media. The proposed algorithm generates scalable and robust watermarked image code-stream that allows the user to constrain embedding distortion for target content adaptations. The watermarked image code-stream consists of hierarchically nested joint distortion-robustness coding atoms. The code-stream is generated by proposing a new wavelet domain blind watermarking algorithm guided by a quantization based binary tree. The code-stream can be truncated at any distortion-robustness atom to generate the watermarked image with the desired distortion-robustness requirements. A blind extractor is capable of extracting watermark data from the watermarked images. The algorithm is further extended to incorporate a bit-plane discarding-based quantization model used in scalable coding-based content adaptation, e.g., JPEG2000. This improves the robustness against quality scalability of JPEG2000 compression. The simulation results verify the feasibility of the proposed concept, its applications, and its improved robustness against quality scalable content adaptation. Our proposed algorithm also outperforms existing methods showing 35% improvement. In terms of robustness to quality scalable video content adaptation using Motion JPEG2000 and wavelet-based scalable video coding, the proposed method shows major improvement for video watermarking.