首页|Study Findings from Maharshi Dayanand University Update Knowledge in Pattern Recognition and Artificial Intelligence (Image Steganography Using Optimized Twin Attention-Based Convolutional Capsule Network)
Study Findings from Maharshi Dayanand University Update Knowledge in Pattern Recognition and Artificial Intelligence (Image Steganography Using Optimized Twin Attention-Based Convolutional Capsule Network)
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NETL
NSTL
World Scientific
New research on pattern recognition and artificial intelligence is the subject of a new report. According to news originating from Rohtak, India, by NewsRx editors, the research stated, “The secret information hiding inside the cover image is termed image steganography, in which the secret information may be either in visual or text format.” Our news journalists obtained a quote from the research from Maharshi Dayanand University: “The concealment of secret information inside the cover image is devised by converting the information into the standard form using conventional image steganography. Here, the cover image is usually systematically altered to carry the secret binary bits after the translation of secret data into binary bits. The cover image may get distorted due to overload, making the hidden information obvious. As a result, the conventional image steganography approaches have a limited ability to conceal. Hence, this research introduces a novel image steganography using the optimized deep learning technique. For novel image steganography, an improved archerfish hunting optimization-based twin attention convolution capsule network (ImAhoTACCNet) is introduced for image steganography.” According to the news reporters, the research concluded: “Here, the proposed ImAho is utilized for modifying the tunable parameters of the TACCNet to enhance the efficiency of the image steganography process in terms of minimum mean square error (MSE) and maximal peak signal-to-noise ratio (PSNR). Besides, secret information compression and recursive encryption techniques further enhance the security of secret information. The analysis of ImAho-TACCNet based on various assessment measures like PSNR, SSIM and MSE accomplished enhanced outcomes with the values of 62.37, 0.9923, and 0.0165 for the hidden network model and 64.23, 0.9989, and 0.0125 for the extraction network model.”
Maharshi Dayanand UniversityRohtakIndiaAsiaCybersecurityMachine LearningPattern Recognition and Artificial Intelligence
NETL
NSTL
World Scientific
