Abstract
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.
Highlights
Security issues are always a challenge in Internet of Things (IoT) scenario
Image steganography is an important branch of information hiding, and it is an effective and safe transmission method with image as cover in IoT scenario [2]
Considering that image steganography puts higher requirements for real-time performance and privacy protection of the IoT, image steganography is implemented based on mobile edge computing in practice [3], [4]
Summary
Security issues are always a challenge in IoT scenario. The traditional passive cryptography is easy to expose the communication process, making the sensitive data to attract the attention of attackers. The more secret embedded in the cover, the lower the imperceptibility and security, so the embedded capacity can be used as a constraint condition in multi-objective optimization problem. Artificial immune algorithm was used to deal with multi-objective optimization problems for image steganography. Multiple directional and non-directional high-pass filters bank is used to preprocess the cover image and filter residuals are aggregated to form the candidate locations of the perturbation These aggregated residuals correspond to noise and texture regions that are difficult to model. EMOsteg takes the embedded capacity as the constraint condition and formally defines the multi-objective optimization problem by minimizing the imperceptibility and maximizing the security. Based on artificial immune theory, EMOsteg transforms the multi-objective optimization problem into an antigen, transforms the perturbation locations into an antibody.
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