Steganalysis Detection Research Articles

Moiré pattern generation-based image steganography

Generative steganography has developed recently due to its high resistance against steganalysis detection. It usually synthesizes stego images directly from secret information. In contrast to this manner, we propose a generative steganographic scheme by synthesizing Moiré patterns in this paper. Moiré patterns are widely observed in captured photos. They present various shapes, colors, and frequency ranges, which are expected to help carry secret information robustly and securely. The scheme synthesizes Moiré patterns via a Moiré pattern generator. Then a secret message is encoded in these patterns by a parallel encoder. The Moiré pattern generator employs a multi-branch structure to generate sub Moiré patterns at distinct frequencies. A discriminator is further imposed to ensure the realness of synthesized Moiré patterns. The parallel encoder spreads secret information over Moiré patterns in both frequency and spatial domain. A decoder and a steganalyzer are also included to ensure the robustness and undetectability of the information hiding. Furthermore, a two-stage training strategy is designed to accomplish both Moiré pattern generation and information hiding tasks. Experimental results demonstrate that the proposed scheme presents high undetectability against existing steganalytic tools, meanwhile, it can resist common image attacks such as AWGN and JPEG compression.

DUIANet: A double layer U-Net image hiding method based on improved Inception module and attention mechanism

Image hiding is the process of hiding secret image into cover image, and revealing secret image from steganographic image. However, the quality of generated images and the ability to resist steganalysis detection of steganographic image can be further improved. We propose a novel double-layer U-Net image hiding method based on improved Inception and attention mechanism. The secret image undergoes Haar wavelet transform, hiding network hides the wavelet sub-band of secret image into cover image. We use improved Inception while cascading convolution and attention mechanisms to better extract and fuse information, resulting in high-quality steganographic image. In the revealing stage, Attention mechanism can help networks extract more features about secret image, then we using inverse Haar wavelet transform to revealing high-quality secret image. The experimental results show that the method can obtain high quality images and have good security. When hiding multiple images, the method still has good performance.

Secret-to-Image Reversible Transformation for Generative Steganography

Recently, generative steganography that transforms secret information to a generated image has been a promising technique to resist steganalysis detection. However, due to the inefficiency and irreversibility of the secret-to-image transformation, it is hard to find a good trade-off between the information hiding capacity and extraction accuracy. To address this issue, we propose a secret-to-image reversible transformation (S2IRT) scheme for generative steganography. The proposed S2IRT scheme is based on a generative model, i.e., Glow model, which enables a bijective-mapping between latent space with multivariate Gaussian distribution and image space with a complex distribution. In the process of S2I transformation, guided by a given secret message, we construct a latent vector and then map it to a generated image by the Glow model, so that the secret message is finally transformed to the generated image. Owing to good efficiency and reversibility of S2IRT scheme, the proposed steganographic approach achieves both high hiding capacity and accurate extraction of secret message from generated image. Furthermore, a separate encoding-based S2IRT (SE-S2IRT) scheme is also proposed to improve the robustness to common image attacks. The experiments demonstrate the proposed steganographic approaches can achieve high hiding capacity (up to 4 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">bpp</i> ) and accurate information extraction (almost 100% accuracy rate) simultaneously, while maintaining desirable anti-detectability and imperceptibility.

Generative Steganography via Auto-Generation of Semantic Object Contours

As a promising technique of resisting steganalysis detection, generative steganography usually generates a new image driven by secret information as the stego-image. However, it generally encodes secret information as entangled features in a non-distribution-preserving manner for the stego-image generation, which leads to two common issues: 1) limited accuracy of information extraction, and 2) low security in feature-domain. To address the above issues, we propose a generative steganographic framework via auto-generation of semantic object contours, in which a given secret message is encoded as the disentangled features, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e</i> ., object-contours, in a distribution-preserving manner for the stego-image generation. In this framework, we propose a contour generative adversarial nets (CtrGAN) consisting of a contour-generator and a contour-discriminator, which are adversarially trained with reinforcement learning. To realize the generative steganography, by using the contour-generator of the trained CtrGAN, a contour point selection (CPS)-based encoding strategy is designed to encode the secret message as the contours. Then, the BicycleGAN is employed to transform the generated contours to the corresponding stego-image. Extensive experiments demonstrate the proposed steganographic approach achieves superior performance in the aspects of information extraction accuracy, especially under common image attacks, and feature-domain security, compared to the state-of-the-arts.

Improving UNIWARD distortion function via isotropic construction and hierarchical merging

Distortion function is designed for evaluating the cost of modifications in adaptive steganography. UNIWARD is a successful and popular distortion scheme which achieves high performance both for spatial and JPEG images. In this paper, we analyze the UNIWARD scheme with some empirical rules of distortion function designation. Based on that we propose our scheme to improve UNIWARD distortion. In our scheme, we focus on the symmetric characteristic of UNIWARD, and suggest that not only use original wavelet filters but also their flippings to calculate sub-models of UNIWARD distortion to maintain its isotropic properties. Moreover, we design several schemes to merge sub-models, which could maintain its invariance regard to flipping or rotation and improve its security against steganalysis detection. Experimental results show our revised UNIWARD achieves better performance for spatial and JPEG image in comparison with original UNIWARD.

ASSAF: Advanced and Slim StegAnalysis Detection Framework for JPEG images based on deep convolutional denoising autoencoder and Siamese networks

Steganography is the art of embedding a confidential message within a host message. Modern steganography is focused on widely used multimedia file formats, such as images, video files, and Internet protocols. Recently, cyber attackers have begun to include steganography (for communication purposes) in their arsenal of tools for evading detection. Steganalysis is the counter-steganography domain which aims at detecting the existence of steganography within a host file. The presence of steganography in files raises suspicion regarding the file itself, as well as its origin and receiver, and might be an indication of a sophisticated attack. The JPEG file format is one of the most popular image file formats and thus is an attractive and commonly used carrier for steganography embedding. State-of-the-art JPEG steganalysis methods, which are mainly based on neural networks, are limited in their ability to detect sophisticated steganography use cases. In this paper, we propose ASSAF, a novel deep neural network architecture composed of a convolutional denoising autoencoder and a Siamese neural network, specially designed to detect steganography in JPEG images. We focus on detecting the J-UNIWARD method, which is one of the most sophisticated adaptive steganography methods used today. We evaluated our novel architecture using the BOSSBase dataset, which contains 10,000 JPEG images, in eight different use cases which combine different JPEG’s quality factors and embedding rates (bpnzAC). Our results show that ASSAF can detect stenography with high accuracy rates, outperforming, in all eight use cases, the state-of-the-art steganalysis methods by 6% to 40%.

High-Capacity Image Steganography with Minimum Modified Bits Based on Data Mapping and LSB Substitution

Steganography is the art and practice of communication using hidden messages. The least significant bits (LSB) based method is the well-known type of steganography in the spatial domain. Usually, achieving the larger embedding capacity in LSB-based methods requires a large number of LSB bits modification which indirectly reduces the visual quality of stego-image and increases the risk of steganalysis detection attacks. In this study, we propose a novel steganography method with data mapping strategy which can reduce the number of bits modification per pixel. In the proposed method, four secret data bits are mapped with the four most significant bits of a cover pixel. Furthermore, the only two LSBs of a pixel are modified to indicate the mapping strategy. Experimental results show that the proposed method is able to achieve 3.48% larger embedding capacity while enhancing the visual quality (i.e., peak signal to noise ratio (PSNR) 3.73 dB) and reducing the modification of 0.76 bits per pixel. Moreover, the proposed method provides security against basic Regular and Singular groups (RS) steganalysis and histogram steganalysis detection attacks.

Image steganography in spatial domain: A survey

This paper presents a literature review of image steganography techniques in the spatial domain for last 5 years. The research community has already done lots of noteworthy research in image steganography. Even though it is interesting to highlight that the existing embedding techniques may not be perfect, the objective of this paper is to provide a comprehensive survey and to highlight the pros and cons of existing up-to-date techniques for researchers that are involved in the designing of image steganographic system. In this article, the general structure of the steganographic system and classifications of image steganographic techniques with its properties in spatial domain are exploited. Furthermore, different performance matrices and steganalysis detection attacks are also discussed. The paper concludes with recommendations and good practices drawn from the reviewed techniques.

Enhanced high capacity image steganography using discrete wavelet transform and the Laplacian pyramid

This paper introduces a high capacity image hiding scheme with enhanced stego image quality. This new hiding scheme utilizes a multiscale Laplacian pyramid of the cover image in the Discrete Wavelet Transform (DWT) domain. Previous work either enhanced capacity at the expense of stego quality or improved stego quality albeit at lower capacities. The proposed scheme will utilize the high-frequency bands of the DWT of the cover image for increased hiding capacity while further extending the payload capacity by hiding in the lowest level Laplacian pyramid of the DWT low-frequency band using a curve-fitting adaptive region approach in the spectral magnitude discrete cosine transform domain. The proposed scheme results in enhanced visual fidelity as well as high capacities as compared to competing methods. Comparative experimental results will show that the proposed scheme outperforms recent methods in terms of payload capacity as well as various image quality measures. Resistance to data-loss and noise tampering, geometric distortions and Checkmark attacks, and steganalysis detection attacks will further demonstrate the robustness of the proposed scheme.

Adaptive Spatial Steganography Based on the Correlation of Wavelet Coefficients for Digital Images in Spatial Domain

How to define the pixel’s embedding distortion is a key problem of image adaptive steganography. This paper proposes an adaptive spatial steganography in spatial domain based on the correlation of wavelet coefficients for digital images according to the principle of minimizing a suitable defined distortion. Firstly, one-dimensional high-pass and low-pass filters are used to construct directional filters. Secondly, do direction filtering to images through the direction of horizontal, vertical and diagonal, and design the distortion cost function according to the correlation of wavelet coefficients. Finally, the secret message is embedded by STC (Syndrome Trellis Code) according to the pixel’s embedding distortion. The experiment results illustrate that the proposed method not only make the embedding region focus on the texture regions whose content is complex, but also can resist the common steganalysis detection effectively.

A Review on Classification Based Approaches for STEGanalysis Detection

A Review on Classification Based Approaches for STEGanalysis Detection