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Adapted Deep Key Generation Using Fourier–Riesz Features for Secure Video Encryption

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  • Haingonirina Ignace Rajaosolomanantena

    (University of Antananarivo, Madagascar)

  • Toky Basilide Ravaliminoarimalalason

    (University of Antananarivo, Madagascar)

  • Hery Zo Andriamanohisoa

    (University of Antananarivo, Madagascar)

Abstract

Video encryption protects multimedia data over insecure networks. This paper introduces a hybrid key-generation framework combining Fourier– Riesz features with an adapted deep neural model to produce dynamic, frame-dependent keys. A four-channel representation integrating spectral magnitude, spectral phase, directional amplitude, and orientation ensures key decorrelation. Experiments conducted on standard video datasets showed entropy values ranging between 7.96 and 7.99 bits, a strong avalanche effect with an average Hamming distance of 129.62, near-zero inter-frame and inter-channel correlations, and preserved visual quality with a PSNR of 42 dB. Security analysis confirmed overall robustness through extensive evaluations.

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Handle: RePEc:epw:ejai00:v:5:y:2026:i:2:id:70120
DOI: 10.24018/ejai.2026.5.2.70120
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