A CPU-GPU heterogeneous parallel encryption scheme for raster remote sensing images using hybrid DNA operations and cellular automaton diffusion

The widespread use of high-resolution remote sensing images in meteorology, geology, and national security calls for highly efficient and secure protection mechanisms. However, conventional image encryption methods often face substantial limitations when handling large-scale geospatial data, due to memory-bandwidth constraints and insufficient computational throughput. To address these bottlenecks, this paper proposes a novel Heterogeneous CPU-GPU Parallel Image Encryption Scheme (HC-PIES), which structurally integrates chaotic permutations, DNA-level operations, and cellular-automaton (CA)-based diffusion. Within the HC-PIES architecture, the CPU initially performs a global spatial permutation stage driven by a 2D-SLMM chaotic map, utilizing session-dependent parameters dynamically derived from the SHA-512 hash of the plaintext image. Subsequently, to mitigate global memory access latency and accelerate the diffusion phase, a block-based GPU parallelization strategy is introduced. Specifically, a fused GPU kernel architecture is designed to execute chaotic sequence generation and hybrid DNA-CA diffusion within the on-chip shared memory, thereby reducing memory overhead and improving parallel execution efficiency. Extensive experiments conducted on standard test datasets and remote sensing images demonstrate that the proposed HC-PIES achieves both strong security and practical computational efficiency. For a [Formula: see text] remote sensing image, the ciphertext information entropy reaches 7.9999 bits, closely approaching the theoretical ideal. Furthermore, the measured NPCR and UACI values satisfy the mathematical expectations for 8-bit image encryption. Performance evaluation shows that the proposed implementation encrypts a [Formula: see text] image in 0.0853 s on an entry-level GPU, achieving a significant acceleration ratio over a sequential CPU implementation. These results indicate that HC-PIES is highly promising for secure and real-time processing of massive remote sensing data.