Cryptanalysis and Improvement of Memristive Hopfield Neural Network Color Image Cryptosystem

Chaos-based image ciphers may exhibit favorable statistical outputs while retaining exploitable algebraic relations. Following Kerckhoffs’s principle, this paper analyzes a published memristive Hopfield neural network color image cryptosystem as a public algorithm with secret initial conditions. We show that its image-layer transformation is exactly a plaintext-independent permutation followed by a reusable bytewise XOR mask shared by the RGB channels; the induced permutation and mask form an equivalent decryption secret. Based on this reduction, we develop chosen-plaintext, known-plaintext, chosen-ciphertext, differential, and black-box analyses, then validate complete recoveries by exact decryption of an independent challenge image. Next, we introduce target-specific modifications based on counter-dependent HNN state derivation, independent channel permutation material, and bidirectional plaintext-dependent state feedback diffusion. Operation-level inversion and experiments on the redesigned image layer verify exact round-trip recovery, channel separation, counter-dependent ciphertexts, and broad propagation of a controlled plaintext change. The proposed improved method effectively resists the cryptanalytic attacks studied in this paper.