Image Encryption via Data-Identified Discrete Chaotic Maps
For cryptography researchers, this work introduces a novel method to generate data-dependent chaotic maps, enhancing security by making the map structure part of the key, though the improvement over existing chaos-based methods is incremental.
This paper proposes a data-driven image encryption framework that learns chaotic maps from data using SINDy-PI, achieving near-ideal security metrics (NPCR ~99.6%, UACI ~33.5%, entropy ~8 bits) and extreme sensitivity to initial conditions. The approach establishes a new paradigm for chaos-based cryptography beyond fixed maps.
In this work, we propose a data-driven image encryption framework that identifies chaotic maps directly from data using the SINDy-PI algorithm. Unlike conventional encryption schemes relying on predefined maps, our method learns the full explicit dynamics -- including cross-terms and higher-order nonlinearities -- from observational data. The validity of this approach is verified on three distinct chaotic systems: the H{é}non map, the three-dimensional logistic map, and the piecewise-linear Lozi map, demonstrating its generality. The encryption key consists solely of initial conditions; the map structure itself becomes data-dependent, introducing an extra layer of security. Moreover, even when the initial conditions are fixed, different training data (e.g., with a tiny noise seed) lead to slightly different maps, which produce completely different ciphertexts (NPCR $\approx 99.6\%$, UACI $\approx 33.5\%$). Numerical experiments on the H{é}non system show near-ideal information entropy ($\approx 8$ bits), negligible inter-pixel correlation, and extreme sensitivity to initial conditions: a perturbation of $10^{-16}$ causes total decryption failure. The scheme resists both differential and statistical attacks, with NPCR and UACI values matching theoretical ideals. Our results establish a new paradigm for chaos-based cryptography beyond fixed maps.