As an essential tool for secure communications, adaptive steganography aims to communicate secret information with the least security cost. Inspired by the Ranking Priority Profile (RPP), we propose a novel two-step cost function for adaptive steganography in this paper. The RPP mainly includes three rules, i.e. Complexity-First rule, the Clustering rule and the Spreading rule, to design a cost function. We use the two-dimensional Singular Spectrum Analysis (2D-SSA) and Weighted Median Filter (WMF) in designing the two-step cost function. The 2D-SSA is employed in selecting the key components and clustering the embedding positions, which follows the Complexity-First rule and the Clustering rule. Also, we deploy the Spreading rule to smooth the resulting image produced by 2D-SSA with WMF. Extensive experiments have shown the efficacy of the proposed method, which has improved performance over four benchmarking approaches against non-shared selection channel attack. It also provides comparable performance in selection-channel-aware scenarios, where the best results are observed when the relative payload is 0.3 bpp or larger. Besides, the proposed approach is much faster than other model-based methods.INDEX TERMS Image steganography; feature extraction; singular spectrum analysis (SSA); Weighted median filtering (WMF); ranking priority profile.
Steganography is the technique for embedding secret messages into digital media without changing their appearances. As a countermeasure to steganography, steganalysis detects the presence of hidden data in digital content. For the last decade, the majority of image steganalysis approaches can be formed by two stages. The first stage is to extract effective features from the image content and the second is to train a classifier in machine learning by using the features from stage one. Ultimately the image steganalysis becomes a binary classification problem. Since Deep Learning related architecture unifies these two stages and saves researchers lots of time designing hand-crafted features, the design of a CNN-based steganalyzer has therefore received increasing attention over the past few years. In this paper, we will examine the development in image steganalysis, both in the spatial domain and in the JPEG domain, and discuss the future directions.
Abstract. In this work, we firstly investigate directional lifting wavelet transform (DLWT) as a sparse representation of images. Then a block compressive sensing (BCS) measurement matrix is designed by using the generalized Gaussian distribution (GGD) model. The measurement matrix can be used to sense the DLWT coefficients of images, which reflects the feature residual introduced by steganography. Finally, a reconstruction approach of hidden signal is achieved efficiently by the extracted residual. With the residual message, the scheme has a flexible self-recovery quality. Experimental results show that our proposed method is not only universal for detecting spatial domain steganography, but also capable of recovering the secret signal from the stego images.
Multivariate cryptography is one of the most promising candidates for post-quantum cryptography. Applying machine learning techniques in this paper, we experimentally investigate the side-channel security of the multivariate cryptosystems, which seriously threatens the hardware implementations of cryptographic systems. Generally, registers are required to store values of monomials and polynomials during the encryption of multivariate cryptosystems. Based on maximum-likelihood and fuzzy matching techniques, we propose a template-based least-square technique to efficiently exploit the side-channel leakage of registers. Using QUAD for a case study, which is a typical multivariate cryptosystem with provable security, we perform our attack against both serial and parallel QUAD implementations on field programmable gate array (FPGA). Experimental results show that our attacks on both serial and parallel implementations require only about 30 and 150 power traces, respectively, to successfully reveal the secret key with a success rate close to 100%. Finally, efficient and low-cost strategies are proposed to resist side-channel attacks.
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