The ISC International Journal of Information Security

Abstract Least Significant Bit Matching (LSBM) is a simple steganography approach that has been detected under multiple attacks. Imperceptibility (i.e., maintenance of high perceptual image quality) and security are significant parameters in steganography. However, most conventional steganography techniques rely on single-objective optimization, which focuses on improving one parameter while often compromising others. This limitation underscores the need for approaches that balance conflicting objectives. To address this, the present study employs the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) to optimize security and imperceptibility. This methodology includes a cover image division into blocks, each with two critical decisions: (1) seed determination for the pseudo-random number generator to simultaneously identify optimal pixels for data embedding and (2) selecting whether the pixel value should be increased or reduced upon a mismatch between the data bit and pixel LSB. Pixels with the highest data bit–LSB correspondence are optimal, and a pixel value change (increase or reduction) is to minimize block histogram variation. This multiobjective optimization is carried out using NSGA-II. It was comparatively revealed that the developed methodology remarkably improved image quality metrics and decreased detection accuracy at different embedding rates. At embedding rates of 0.3, 0.5, and 0.8 bpp, the Peak Signal-to-Noise Ratio (PSNR) was approximately 57.65, 55.55, and 52.75, respectively. This result represents a 1.5-2.5% improvement compared to conventional LSBM techniques. 

Abstract The paper presents a novel approach to Secret Image Sharing (SIS) that combines (t, n)-threshold schemes with steganography, utilizing Joseph’s problem and Homogeneous Linear Recursion (HLR) to enhance security. The methodology involves dividing a secret image into shadow images, embedding these shadows into cover images using a Least Significant Bit (LSB) method guided by Joseph’s problem. The study aims to increase the security of SIS while maintaining high visual quality in the stego images. The authors validate their approach through various experiments, demonstrating that the proposed method improves Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) compared to existing methods.

Abstract Steganography is a solution for covert communication and blockchain is a p2p network for data transmission, so the benefits of blockchain can be used in steganography. In this paper, we discuss the advantages of blockchain in steganography, which include the ability to embed hidden data without manual change in the original data, as well as the readiness of the blockchain platform for data transmission and storage. By reviewing the previous four steganography schemes in blockchain, we have examined their drawback and shown that most of them are non-practical schemes for steganography in blockchain. We have proposed two algorithms for steganography in blockchain, the first one is a high-capacity algorithm for the key and the steganography algorithm exchange and switching, and the second one is a medium-capacity algorithm for embedding hidden data. The proposed method is a general method for steganography in each blockchain, and we investigate how it can be implemented in two most popular blockchains, Bitcoin and Ethereum. Experimental result shows the efficiency and practicality of proposed method in terms of execution time, latency and steganography fee. Finally, we have explained the challenges of steganography in blockchain from the steganographers' and steganalyzers' point of view.

Abstract In this study, a novel approach which uses combination of steganography and cryptography for hiding information into digital images as host media is proposed. In the process, secret data is first encrypted using the mono-alphabetic substitution cipher method and then the encrypted secret data is embedded inside an image using an algorithm which combines the random patterns based on Space Filling Curves (SFC) and the optimal pair-wise LSB matching method. We employ a modified Imperialist Competitive Algorithm by Genetic Algorithm operations, namely Discrete Imperialist Competitive Algorithm (DICA), to perform the optimal pair-wise LSB matching method and find the suboptimum adjustment list. The performance of the proposed method is compared with other methods with respect to Peak Signal to Noise Ratio (PSNR). The PSNR value of the proposed method is higher than the state-of-the-art methods by almost 4dB to 5dB.

Abstract Perturbed Quantization (PQ) steganography scheme is almost undetectable with the current steganalysis methods. We present a new steganalysis method for detection of this data hiding algorithm. We show that the PQ method distorts the dependencies of DCT coefficient values; especially changes much lower than significant bit planes. For steganalysis of PQ, we propose features extraction from the empirical matrix. The proposed features can be exploited within an empirical matrix of DCT coefficients which some most significant bit planes were deleted. We obtain four empirical matrices and fuse resulted features from these matrices which have been employed for steganalysis. This technique can detect PQ embedding on stego images with 77 percent detection accuracy on mixed embedding rates between 0.05 _ 0.4 bits per non-zero DCT AC coefficients (BPNZC). Comparing the results, we also show that the detection rates are effectively comparable with respect to current steganalysis techniques for PQ steganography.

Abstract In this paper a steganalysis method is proposed for pixel value differencing method. This steganographic method, which has been immune against conventional attacks, performs the embedding in the difference of the values of pixel pairs. Therefore, the histogram of the differences of an embedded image is di_erent as compared with a cover image. A number of characteristics are identified in the difference histogram that show meaningful alterations when an image is embedded. Five distinct multilayer perceptrons neural networks are trained to detect different levels of embedding. Every image is fed in to all networks and a voting system categorizes the image as stego or cover. The implementation results indicate an 88.6% success in correct categorization of the test images.