Flexible visually secure image encryption with meta-learning compression and chaotic systems

As digital images are extensively applied across diverse domains, the demand for visually secure image encryption technology has surged remarkably. However, existing schemes generally suffer from insufficient encryption security and low-quality decrypted images. Therefore, this paper proposes a flexible scheme that integrates meta-learning, a chaotic system, traditional deep learning, and the LSB-$2^k$ correction embedding method. The core of this scheme lies in the design of a meta-learning compression reconstruction network with dynamic auxiliary input, which enables high-quality compression of a plain image. Then, a novel chaotic system, IS-DP, is constructed to encrypt the compressed image into a noise-like secret image by combining 2D-IS chaotic system with a traditional deep learning network. Finally, a lossless embedding method with LSB-$2^k$ correction is employed to embed the secret image into a carrier image, resulting in a visually secure cipher image. This scheme fully validates the great potential and feasibility of deep learning methods in encryption and compression. Moreover, the flexibility endowed by the meta-learning mechanism allows users to adjust the inner-loop iteration number according to practical needs, balancing running time and decrypted image quality, thus demonstrating broad application prospects.