Inverted LSB image steganography using adaptive pattern to improve imperceptibility

Mobile edge computing provides high computing power, data storage capacity and bandwidth requirements for Internet of Things (IoT) through edge servers that process data close to data sources or users. In practical, mobile edge computing can be used to implement image steganography in IoT. Considering imperceptibility, security and capacity are important indicators for image steganography, this paper propose an image steganography based on evolutionary multi-objective optimization (EMOsteg). The EMOsteg preprocesses the image through a high-pass filters bank to find noise and texture regions that are difficult to model. By perturbing the image on noise and texture regions in multiple directions, the embedded capacity is increased. By defining the imperceptibility and security as an antigen, defining the perturbation positions of the cover image as an antibody, the EMOsteg uses the artificial immune principle to heuristically obtain the perturbation population through feature extraction of the perturbation and adaptive evolution operations. And the Pareto optimal is used to find the optimal perturbation in the last generation population. The simulation experiments analyze the convergence of the algorithm and the diversity of the solutions. In simulation experiments, the MSE, PSNR and SSIM were adopted to evaluate the imperceptibility, and the results show that the MSE value of our algorithm is 0.000308, the PSNR is 82.7501 and the SSIM approaches 1, they are better than comparison algorithms. The average detection error $P_{E}$ under SPA was adopted to detect the security, and the results show that our algorithm is more robust against anti-SPA steganalysis. In order to evaluate the performance of real-time, the embedding time of the same secret under different algorithms were compared, and the results show that our algorithm is faster than comparison algorithms in the terminal. In summary, the proposed algorithm can maintain the image quality while resist steganalysis tools, and realize real-time processing.

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Image Steganography Based on Artificial Immune in Mobile Edge Computing With Internet of Things

Digital image steganography survey and investigation (goal, assessment, method, development, and dataset)

With the advent of a fifth-generation mobile network and developments in technologies such as the Internet of Medical Things, smart healthcare systems are becoming ubiquitous in our daily lives. Patients, doctors, and other medical personnel rely on the safe and efficient storage, transmission, and analysis of electronic health records, particularly medical images, for successful diagnosis, treatment, and management of different ailments. In this study, a multiwatermarking scheme is proposed for medical images based on quantum random walk and the brain storm optimization algorithm. A logo image used to verify medical image integrity is embedded in regions of interest, and text data are embedded in regions of non-interest to conceal private hospital and patient information. This process improves the accuracy of medical image verification and helps to ensure authenticity. A series of experiments were conducted to validate the capacity, security, robustness, and imperceptibility of the proposed multiwatermarking scheme. 

A Multiwatermarking Scheme for Verifying Medical Image Integrity and Authenticity in the Internet of Medical Things

High-capacity adaptive steganography based on LSB and Hamming code

Image steganography is a technique for hiding secret data in cover image. In recent years, chaotic systems have become increasingly important in steganography. This paper proposes an embedding technique based on cyclic chaos, which effectively embeds secret messages into color images. The cyclic chaos is divided into two parts: the chaotic function of generating seeds and the pseudo random number generator (PRNG). A pixel is found through the chaotic cycle system, and a three-bit secret message is embedded into the R, G, and B channels of the pixel through a specific embedding algorithm. Compared with other color image embedding methods, the proposed algorithm has higher visual quality and improved security.

LSB Color Image Embedding Steganography Based on Cyclic Chaos

Intrusion detection is an effective measure to prevent host and network attacks. The use of an intrusion detection system makes up for the shortcomings of traditional firewall technology, signature authentication technology, and access control technology in security protection. However, due to the imbalance of the distribution of intrusion detection data samples, the mutual redundancy between sample features seriously affects the accuracy and efficiency of specific attack detection. In this paper, a proposal was forward, the appropriate feature subsets are selected respectively based on mutual information and the firefly algorithm, and then proposed a feature selection strategy combining two feature subsets. Experiment outcomes show that the accuracy and efficiency of detection are improved by using the best feature set, compared to the use of all features.

Yanting Wang

Papers

High-capacity adaptive steganography based on LSB and Hamming code

LSB Color Image Embedding Steganography Based on Cyclic Chaos

A New Feature Selection Method for Intrusion Detection