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Image adaptive encryption using EfficientNet B3 feature guided multi scroll chaotic map with modulo controlled pseudo parallel processing
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Image adaptive encryption using EfficientNet B3 feature guided multi scroll chaotic map with modulo controlled pseudo parallel processing
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Journal Article
Image adaptive encryption using EfficientNet B3 feature guided multi scroll chaotic map with modulo controlled pseudo parallel processing
2025
Overview
A new multistage encryption algorithm is proposed by integrating the deep neural network with a new 4D multi-scroll chaotic map to enhance the efficiency and improve the security of image transmission in the open channel. This combined network expands the key space and maintains the secrecy of the key with the multistage encryption algorithm. Initially, the image adaptive key generation process is implemented by the EfficientNet-B3 network to extract the features from the source image, which are then converted into hash values using SHA 256. The hash values are partitioned into four sections, and each section is normalized to give one distinct initial value for the generation of a multi-scroll chaotic sequence. The pseudo-parallel process routes the split source sub-image blocks (128⨯128) of plain text to branch 1 or branch 2, decided by the seed value of the chaotic sequence, increasing the high robustness against the differential and statistical attacks. Each branch contains row and column-wise permutations, bidirectional selective shuffling, and chaotic intra/inter-pixel diffusion in varying orders. The key image diffusion and dynamic DNA diffusion to the intermediate cypher image exhibit a strong avalanche effect. The simulation evaluation on the natural data set images demonstrates the large key space of 2 to the power of 674, high key sensitivity, uniform histogram with entropy value attains the critical values of 7.9, high NPCR value of 99.9%, UACI values with 33.46%, almost zero-pixel correlation and strong robustness to the cropping and noise attacks.
Publisher
Nature Publishing Group UK,Nature Publishing Group,Nature Portfolio
