JPEG Steganography Research Articles

An efficient steganography scheme based on wavelet transformation for side-information estimation

Previous studies have specifically demonstrated that incorporating high-quality side-information can notably enhance the steganographic security of JPEG images. To obtain more precise side-information estimates, researchers utilize sophisticated deblocking or denoising filters, which enhance security but require a substantial amount of time. The challenge lies in achieving a significant improvement in security and efficiency. This paper introduces a steganographic network named WTSNet, which leverages wavelet transform for estimating side-information through iterative optimization in both the pixel and wavelet domains. The wavelet domain is purposely designed to capture intricate high-frequency texture details and facilitate precise estimation of the side-information. Furthermore, the embedding cost is adjusted based on the polarity of the estimated rounding error, ensuring both secure and efficient performance. Experimental results demonstrate that the proposed scheme substantially enhances the security of conventional JPEG steganography, which relies on additive distortion. Moreover, it outperforms the state-of-the-art JPEG steganography methods based on estimating side-information while consuming less time.

Dual-domain joint optimization for universal JPEG steganography

JPEG steganography hides secret messages by modifying DCT coefficients in cover images to ensure the undetectability. Currently, most JPEG steganography methods rely on the additive distortion model, assigning adaptive costs to individual coefficients. However, these costs are often designed within a single domain, either DCT or decompressed domain, thereby making the hiding behavior detectable by multi-domain steganalysis features. To improve the steganography security, this paper proposes an universal Dual-Domain JPEG Additive Distortion (DDJAD) framework, which minimize additive distortion in both JPEG and decompressed domain concurrently. A measure to reduce the perceptibility of coefficient modifications within pixel blocks is first formulated and then incorporated into the traditional JPEG steganography model. Finally, the new additive distortion model is solved by fine-tuning existing costs, illustrating the universality of the proposed method. Experimental results showcase the superiority of the proposed method over previous works across various quality factors and payloads, employing typical steganalysis features.

Robust JPEG steganography based on the robustness classifier

Because the JPEG recompression in social networks changes the DCT coefficients of uploaded images, applying image steganography in popular image-sharing social networks requires robustness. Currently, most robust steganography algorithms rely on the resistance of embedding to the general JPEG recompression process. The operations in a specific compression channel are usually ignored, which reduces the robustness performance. Besides, to acquire the robust cover image, the state-of-the-art robust steganography needs to upload the cover image to social networks several times, which may be insecure regarding behavior security. In this paper, a robust steganography method based on the softmax outputs of a trained classifier and protocol message embedding is proposed. In the proposed method, a deep learning-based robustness classifier is trained to model the specific process of the JPEG recompression channel. The prediction result of the classifier is used to select the robust DCT blocks to form the embedding domain. The selection information is embedded as the protocol messages into the middle-frequency coefficients of DCT blocks. To further improve the recovery possibility of the protocol message, a robustness enhancement method is proposed. It decreases the predicted non-robust possibility of the robustness classifier by modifying low-frequency coefficients of DCT blocks. The experimental results show that the proposed method has better robustness performance compared with state-of-the-art robust steganography and does not have the disadvantage regarding behavior security. The method is universal and can be implemented in different JPEG compression channels after fine-tuning the classifier. Moreover, it has better security performance compared with the state-of-the-art method when embedding large-sized secret messages.

Toward high imperceptibility deep JPEG steganography based on sparse adversarial attack

JPEG steganography is an important branch of information hiding. However, with the development of deep learning-based steganalysis models, steganography is facing great challenges. To better resist these steganalysis models, this paper proposes a deep JPEG steganography framework based on sparse adversarial attacks. According to the vulnerability of deep steganalysis models to adversarial attacks, sparse adversarial attacks are introduced into the deep JPEG steganographic structure to improve the security of stego images. In addition, to resist steganalysis from JPEG and spatial domains, both JPEG and spatial domain steganalysis models are involved in adversarial training. Finally, to further enhance the imperceptibility of adversarial stego images, the visual perception loss is designed from the perspective of human eyes. Experimental results indicate that compared with the existing methods, the proposed method has higher imperceptibility and security and can resist modern deep steganalysis models in both JPEG and spatial domains to a certain extent. The source code is available at https://github.com/imagecbj/SAE-JS.

A secure adaptive Hidden Markov Model-based JPEG steganography method

A secure adaptive Hidden Markov Model-based JPEG steganography method

JPEG Steganography With Content Similarity Evaluation.

Content similarity is a representative property of natural images, for example, similar regions, which is utilized by modern steganalysis. Existing JPEG steganographic methods mainly focus on the complexity of content but ignore content similarity. This article investigates content similarity to improve the undetectability of JPEG steganography. Specifically, the content similarity of DCT blocks and the 64 parallel channels is used to design the distortion function. Given a JPEG image, initial embedding costs are assigned for quantized DCT coefficients using an appropriate algorithm among the existing distortion functions. Then, the similarities of blocks and channels are used to update the initial embedding costs, respectively. After combination, the final distortion function can be obtained. Using syndrome trellis coding (STC), which achieves minimal embedding distortion with respect to a given distortion function, secret data are embedded into the cover image with a final distortion function. Experimental results show that our scheme achieves better undetectability than current state-of-the-art JPEG steganographic methods.

Universal Deep Network Steganalysis of Color Based on Channel Representation

Up to now, utmost being steganalytic styles are designed for grayscale images, and they aren't suitable for color images that are extensively used in current social networks. To begin with, we divide the input image based on the different embedding spaces of its colors, resulting in three separate channels.. The proposed system includes preprocessing, convolutional, and bracket modules. To save the steganographic vestiges in each color channel, in preprocessing module, we originally separate the input image into three channels according to the corresponding embedding spaces(i.e RGB for spatial steganography and YCbCr for JPEG steganography), and also prize the image residuals with 62 fixed high- pass pollutants, eventually concatenated all abbreviated residuals for posterior analysis analysis rather than adding them together with normal complication like being CNN- grounded steganalyzers. To accelerate the network confluence and effectively reduce the number of parameters, in convolutional module, we precisely design three types of layers with different roadway connections and group complication structures to further learn high- position steganalytic features. In bracket module, we employ a global normal pooling and completely connected subcaste for bracket

Toward High Capacity and Robust JPEG Steganography Based on Adversarial Training

JPEG steganography has become a research hotspot in the field of information hiding. However, the capacity of conventional JPEG steganography methods is hard to meet the requirements in high-capacity application scenarios and also can not extract secret messages accurately after JPEG compression. To mitigate these problems, we propose a high-capacity and robust JPEG steganography based on adversarial training called HRJS, which implements an end-to-end framework in the JPEG domain for the first time. The encoder is responsible for embedding the secret message while the decoder can reconstruct the original secret message. To enhance robustness, an attack module forces the neural network to automatically learn how to correctly recover the secret message after an attack. Experimental results show that our method achieves near 100 % decoding accuracy against JPEG_50 compression at 1/3 bits per channel (bpc) payload while preserving the imperceptibility of the stego image. Compared with conventional JPEG steganography methods, the proposed method is feasible with high capacity (e.g., 1 bpc) and has an obvious advantage in terms of robustness against JPEG compression at the same time.

Cost polarization by dequantizing for JPEG steganography

In this article, we study a recently proposed method for improving empirical security of steganography in JPEG images in which the sender starts with an additive embedding scheme with symmetrical costs of ± 1 changes and then decreases the cost of one of these changes based on an image obtained by applying a deblocking (JPEG dequantization) algorithm to the cover JPEG. This approach provides rather significant gains in security at negligible embedding complexity overhead for a wide range of quality factors and across various embedding schemes. Challenging the original explanation of the inventors of this idea, which is based on interpreting the dequantized image as an estimate of the precover (uncompressed) image, we provide alternative arguments. The key observation and the main reason why this approach works is how the polarizations of individual DCT coefficients work together. By using a MiPOD model of content complexity of the uncompressed cover image, we show that the cost polarization technique decreases the chances of “bad” combinations of embedding changes that would likely be introduced by the original scheme with symmetric costs. This statement is quantified by computing the likelihood of the stego image w.r.t. the multivariate Gaussian precover distribution in DCT domain. Furthermore, it is shown that the cost polarization decreases spatial discontinuities between blocks (blockiness) in the stego image and enforces desirable correlations of embedding changes across blocks. To further prove the point, it is shown that in a source that adheres to the precover model, a simple Wiener filter can serve equally well as a deep-learning based deblocker

Quaternary Quantized Gaussian Modulation With Optimal Polarity Map Selection for JPEG Steganography

Quaternary Quantized Gaussian Modulation With Optimal Polarity Map Selection for JPEG Steganography

Compressing Face Images Using Genetic and Gray Wolf Meta-heuristic Algorithms Based on Variable Bit Allocation

In image processing, compression plays an important role in monitoring, controlling, and securing the process. The spatial resolution is one of the most effective factors in improving the quality of an image; but, it increases the amount of storage memory required. Based on meta-heuristic algorithms, this article presents a compression model for face images with block division and variable bit allocation. Wavelet transform is used to reduce the dimensions of high spatial resolution face images. In order to identify important and similar areas of identical macroblocks, genetic algorithms and gray wolves are used. A bit rate allocation is calculated for each block to achieve the best recognition accuracy, average PSNR, and SSIM. The CIE and FEI databases have been used as case studies. The proposed method has been tested and compared with the accuracy of image recognition under uncompressed conditions and using the common SPIHT and JPEG coding methods. Recognition accuracy increased from 0.18% for 16×16 blocks to 1.97% for 32×32 blocks. Additionally, the gray wolf algorithm is much faster than the genetic algorithm in reaching the optimal answer. Depending on the application type of the problem, the genetic algorithm or the gray wolf may be preferred to achieve the maximum average PSNR or SSIM. At the bit rate of 0.9, the maximum average PSNR for the gray wolf algorithm is 34.92 and the maximum average SSIM for the genetic algorithm is 0.936. Simulation results indicate that the mentioned algorithms increase PSNR and SSIM by stabilizing or increasing recognition accuracy.

Robust JPEG steganography based on DCT and SVD in nonsubsampled shearlet transform domain

Robust JPEG steganography based on DCT and SVD in nonsubsampled shearlet transform domain

Multiperspective Progressive Structure Adaptation for JPEG Steganography Detection Across Domains.

The aim of steganography detection is to identify whether the multimedia data contain hidden information. Although many detection algorithms have been presented to solve tasks with inconsistent distributions between the source and target domains, effectively exploiting transferable correlation information across domains remains challenging. As a solution, we present a novel multiperspective progressive structure adaptation (MPSA) scheme based on active progressive learning (APL) for JPEG steganography detection across domains. First, the source and target data originating from unprocessed steganalysis features are clustered together to explore the structures in different domains, where the intradomain and interdomain structures can be captured to provide adequate information for cross-domain steganography detection. Second, the structure vectors containing the global and local modalities are exploited to reduce nonlinear distribution discrepancy based on APL in the latent representation space. In this way, the signal-to-noise ratio (SNR) of a weak stego signal can be improved by selecting suitable objects and adjusting the learning sequence. Third, the structure adaptation across multiple domains is achieved by the constraints for iterative optimization to promote the discrimination and transferability of structure knowledge. In addition, a unified framework for single-source domain adaptation (SSDA) and multiple-source domain adaptation (MSDA) in mismatched steganalysis can enhance the model's capability to avoid a potential negative transfer. Extensive experiments on various benchmark cross-domain steganography detection tasks show the superiority of the proposed approach over the state-of-the-art methods.

Novel Hidden Bit Location Method towards JPEG Steganography

Image steganalysis has been widely studied, most of which can only complete the binary task of identifying the existence of hidden bits in an inquiry image. Currently, although some algorithms have been proposed to locate hidden bits, most of them mainly focus on the spatial domain, while usually ignoring the study of locating secret bits hidden in DCT domain for JPEG image. To address that challengeable problem, in this paper, towards two classical steganographic algorithms JSteg and F5, hiding bits in DCT domain, we propose a novel payload location method. The principal step of payload location is to estimate the cover image. We novelly propose to assign the different weights to the DCT coefficient residual in virtue of the texture of regions measured by the local variance, leading to the remarkable improvement of location results. Compared with the state of the art, the numerical experiments empirically verify that our proposed location method achieves the superior performance. In particular, when locating hidden bits in JSteg steganographic images with quality factor of 95 at the payload 0.1, the accuracy of location is remarkably improved from 46.18% to 90.22%.

Joint compressed sensing and JPEG coding based secure compression scheme in OFDM-PON

This paper proposes a secure compressed communication scheme in orthogonal frequency division multiplexing passive optical network (OFDM-PON) based on joint compressed sensing and JPEG coding (JCS-JPEG) and multilevel encryption, with a focus to its application in secure storage and transmission. Specifically, working with the under-sampling data of compressed sensing (CS), we combine the JPEG coding standard to deepen compression into the storage and transmission link while inserting random factors into Deoxyribonucleic Acid (DNA) encryption to improve the security of the physical layer. The rationale behind the joint compression is that the data after CS is suitable for block quantization and entropy encoding methods to compress further, similar to the JPEG encoding. For the compression of multimedia data, we demonstrate that it can achieve a satisfactory reconstruction effect and provide an additional 5 to 8 times compression ratio on images. Moreover, we show that the DCT coefficient encryption and random DNA encryption can be seamlessly incorporated into the encryption in CS through the two dimensions of a 2D-chaotic map, thus realizing secure compression and transmission when under-sampling. The proposed scheme is expected to reduce the computational complexity and storage pressure of light resource-constrained sensors, simultaneously improve bandwidth utilization and sensitive information protection in OFDM-PON.

Quantized Gaussian JPEG Steganography and Pool Steganalysis

Currently, algorithms for compressed image steganography mainly embed hidden message by minimizing the resulting distortion or statistical detectability. However, as a result of purely heuristic distortion definitions and numerically solvable equations in statistical models, there are no closed-form solutions for JPEG steganography. The absence of closed-form expression to model JPEG steganography is the main limitation on understanding single image and pool steganalysis behavior. In this study, building upon our previously proposed framework for spatial steganography, we develop a statistical framework for JPEG steganography in which the cover and the hidden message are modeled by multivariate Gaussian distribution. Based on this statistical model, we propose a novel quantized Gaussian JPEG steganography that is able to accomplish embedding using any costs defined in spatial or discrete cosine transform (DCT) domain as well as residual variances. We conduct our experiments using a popular database with different compression qualities to determine the effectiveness of the proposed model. The experimental results show that the proposed model improves the security of previous works and outperforms the state-of-the-art JPEG steganography algorithms. Furthermore, we extend the closed-form expression of single image steganalysis error to pool steganalysis for an omniscience optimal detector. We employ the derived expression to approximate the empirical results of pool steganalysis based on the empirical detection error of single image steganalysis. The practical advantage of the approximation is that even though it is derived based on the adopted statistical model, it is accurate regardless of payload, embedding domain, embedding method, and steganalysis feature as long as the pooling strategy is optimal. In addition to approximation of the error, we employ the proposed model to make predictions about the variance behavior of pool steganalysis error. We mathematically show that the variance increases as the pool size increases in small payloads. The same behavior is observed in experimental results which re-validates our analytical model. We conclude that although pooling improves detector’s performance, it makes the detector less stable in low payloads and high pool sizes.

Enhanced Standard Compatible Image Compression Framework Based on Auxiliary Codec Networks.

Recent deep neural network-based research to enhance image compression performance can be divided into three categories: learnable codecs, postprocessing networks, and compact representation networks. The learnable codec has been designed for end-to-end learning beyond the conventional compression modules. The postprocessing network increases the quality of decoded images using example-based learning. The compact representation network is learned to reduce the capacity of an input image, reducing the bit rate while maintaining the quality of the decoded image. However, these approaches are not compatible with existing codecs or are not optimal for increasing coding efficiency. Specifically, it is difficult to achieve optimal learning in previous studies using a compact representation network due to the inaccurate consideration of the codecs. In this paper, we propose a novel standard compatible image compression framework based on auxiliary codec networks (ACNs). In addition, ACNs are designed to imitate image degradation operations of the existing codec, which delivers more accurate gradients to the compact representation network. Therefore, compact representation and postprocessing networks can be learned effectively and optimally. We demonstrate that the proposed framework based on the JPEG and High Efficiency Video Coding standard substantially outperforms existing image compression algorithms in a standard compatible manner.

Multivariate Side-Informed Gaussian Embedding Minimizing Statistical Detectability

Steganography schemes based on a deflection criterion for embedding posses a clear advantage against schemes based on heuristics as they provide a direct link between theoretical detectability and empirical performance. However, this advantage depends on the accuracy of the cover and stego model underlying the embedding scheme. In this work we propose an original steganography scheme based on a realistic model of sensor noise, taking into account the camera model, the ISO setting and the processing pipeline. Exploiting this statistical model allows us to take correlations between DCT coefficients into account. Several types of dependency models are presented, including a very general lattice model which accurately models dependencies introduced by a large class of processing pipelines of interest. We show in particular that the stego signal which minimizes the KL divergence under this model has a covariance proportional to the cover noise covariance. The resulting embedding scheme achieves state-of-the-art performances which go well beyond the current standards in side-informed JPEG steganography.

Data-independent low-complexity KLT approximations for image and video coding

The Karhunen–Loève transform (KLT) is often used for data decorrelation and dimensionality reduction. The KLT is able to optimally retain the signal energy in only few transform components, being mathematically suitable for image and video compression. However, in practice, because of its high computational cost and dependence on the input signal, its application in real-time scenarios is precluded. This work proposes low-computational cost approximations for the KLT. We focus on the blocklengths N∈{4,8,16,32} because they are widely employed in image and video coding standards such as JPEG and high efficiency video coding (HEVC). Extensive computational experiments demonstrate the suitability of the proposed low-complexity transforms for image and video compression.

Point of Sale Framework-Based Code Igniter and Model View Controller Using Lighthouse Testing

The success of companies engaged in the field of distributors is not only from good and smart human resources, but how ready the company's way to provide tools or programs supporting the work of employees. Some of the problems are the administration system between the company's internal divisions, so there are restrictions on the processing of input data, processes, outputs that are still manual. Web Application is made to facilitate employees to conduct administrative activities with the access role of each user admin division, for example inventory data, creation of sales invoices, road letters, procurement forms and bookkeeping. In addition, The Web View application is already responsive in this sense can adjust the device PC / Tablet / Mobile, then for visualization of data calculation already using cart / data graphics in real time, and there is also a feature preview, download and print JPEG, PNG, PDF, Barcode and QR code. Web Testing This application with black box method and shows it is running well and no error method, so it can be declared passed. Web Creation This application involves the LTE 2.0 Admin Template with bootstrap CSS, for programmer frameworks using MVC model view controllers that exist in the Code Igniter 3.0 framework so that it is faster and easier to implement or developed in other field companies or in the manufacturing industry. Tests using lighthouse for perform by 94% showed that the application designed to have a good performance for display results, and its access value of 60%, indicating that the process related to responsiveness between users is appropriate both using PC / Tablet / Mobile