JPEG steganography with particle swarm optimization accelerated by AVX

Digital steganography aims at hiding secret messages in digital data transmitted over insecure channels. The JPEG format is prevalent in digital communication, and images are often used as cover objects in digital steganography. Optimization methods can improve the properties of images with embedded secret but introduce additional computational complexity to their processing. AVX instructions available in modern CPUs are, in this work, used to accelerate data parallel operations that are part of image steganography with advanced optimizations.Web of Science328art. no. e544

A Secure Steganographic Algorithm Based on Frequency Domain for the Transmission of Hidden Information

This contribution proposes a novel steganographic method based on the compression standard according to the Joint Photographic Expert Group and an Entropy Thresholding technique. The steganographic algorithm uses one public key and one private key to generate a binary sequence of pseudorandom numbers that indicate where the elements of the binary sequence of a secret message will be inserted. The insertion takes eventually place at the first seven AC coefficients in the transformed DCT domain. Before the insertion of the message the image undergoes several transformations. After the insertion the inverse transformations are applied in reverse order to the original transformations. The insertion itself takes only place if an entropy threshold of the corresponding block is satisfied and if the pseudorandom number indicates to do so. The experimental work on the validation of the algorithm consists of the calculation of the peak signal-to-noise ratio (PSNR), the difference and correlation distortion metrics, the histogram analysis, and the relative entropy, comparing the same characteristics for the cover and stego image. The proposed algorithm improves the level of imperceptibility analyzed through the PSNR values. A steganalysis experiment shows that the proposed algorithm is highly resistant against the Chi-square attack

Optimization of medical image steganography using n-decomposition genetic algorithm

Protecting patients' confidential information is a critical concern in medical image steganography. The Least Significant Bits (LSB) technique has been widely used for secure communication. However, it is susceptible to imperceptibility and security risks due to the direct manipulation of pixels, and ASCII patterns present limitations. Consequently, sensitive medical information is subject to loss or alteration. Despite attempts to optimize LSB, these issues persist due to (1) the formulation of the optimization suffering from non-valid implicit constraints, causing inflexibility in reaching optimal embedding, (2) lacking convergence in the searching process, where the message length significantly affects the size of the solution space, and (3) issues of application customizability where different data require more flexibility in controlling the embedding process. To overcome these limitations, this study proposes a technique known as an n-decomposition genetic algorithm. This algorithm uses a variable-length search to identify the best location to embed the secret message by incorporating constraints to avoid local minimum traps. The methodology consists of five main phases: (1) initial investigation, (2) formulating an embedding scheme, (3) constructing a decomposition scheme, (4) integrating the schemes' design into the proposed technique, and (5) evaluating the proposed technique's performance based on parameters using medical datasets from kaggle.com. The proposed technique showed resistance to statistical analysis evaluated using Reversible Statistical (RS) analysis and histogram. It also demonstrated its superiority in imperceptibility and security measured by MSE and PSNR to Chest and Retina datasets (0.0557, 0.0550) and (60.6696, 60.7287), respectively. Still, compared to the results obtained by the proposed technique, the benchmark outperforms the Brain dataset due to the homogeneous nature of the images and the extensive black background. This research has contributed to genetic-based decomposition in medical image steganography and provides a technique that offers improved security without compromising efficiency and convergence. However, further validation is required to determine its effectiveness in real-world applications

Handling boundary constraints for particle swarm optimization in high-dimensional search space

Despite the fact that the popular particle swarm optimizer (PSO) is currently being extensively applied to many real-world problems that often have high-dimensional and complex fitness landscapes, the effects of boundary constraints on PSO have not attracted adequate attention in the literature. However, in accordance with the theoretical analysis in [11], our numerical experiments show that particles tend to fly outside of the boundary in the first few iterations at a very high probability in high-dimensional search spaces. Consequently, the method used to handle boundary violations is critical to the performance of PSO. In this study, we reveal that the widely used random and absorbing bound-handling schemes may paralyze PSO for high-dimensional and complex problems. We also explore in detail the distinct mechanisms responsible for the failures of these two bound-handling schemes. Finally, we suggest that using high-dimensional and complex benchmark functions, such as the composition functions in [19], is a prerequisite to identifying the potential problems in applying PSO to many real-world applications because certain properties of standard benchmark functions make problems inexplicit. © 2011 Elsevier Inc. All rights reserved

Triple scheme based on image steganography to improve imperceptibility and security

A foremost priority in the information technology and communication era is achieving an effective and secure steganography scheme when considering information hiding. Commonly, the digital images are used as the cover for the steganography owing to their redundancy in the representation, making them hidden to the intruders. Nevertheless, any steganography system launched over the internet can be attacked upon recognizing the stego cover. Presently, the design and development of an effective image steganography system are facing several challenging issues including the low capacity, poor security, and imperceptibility. Towards overcoming the aforementioned issues, a new decomposition scheme was proposed for image steganography with a new approach known as a Triple Number Approach (TNA). In this study, three main stages were used to achieve objectives and overcome the issues of image steganography, beginning with image and text preparation, followed by embedding and culminating in extraction. Finally, the evaluation stage employed several evaluations in order to benchmark the results. Different contributions were presented with this study. The first contribution was a Triple Text Coding Method (TTCM), which was related to the preparation of secret messages prior to the embedding process. The second contribution was a Triple Embedding Method (TEM), which was related to the embedding process. The third contribution was related to security criteria which were based on a new partitioning of an image known as the Image Partitioning Method (IPM). The IPM proposed a random pixel selection, based on image partitioning into three phases with three iterations of the Hénon Map function. An enhanced Huffman coding algorithm was utilized to compress the secret message before TTCM process. A standard dataset from the Signal and Image Processing Institute (SIPI) containing color and grayscale images with 512 x 512 pixels were utilised in this study. Different parameters were used to test the performance of the proposed scheme based on security and imperceptibility (image quality). In image quality, four important measurements that were used are Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Mean Square Error (MSE) and Histogram analysis. Whereas, two security measurements that were used are Human Visual System (HVS) and Chi-square (X2) attacks. In terms of PSNR and SSIM, the Lena grayscale image obtained results were 78.09 and 1 dB, respectively. Meanwhile, the HVS and X2 attacks obtained high results when compared to the existing scheme in the literature. Based on the findings, the proposed scheme give evidence to increase capacity, imperceptibility, and security to overcome existing issues