Lightweight Homomorphic Pixel Scrambling for Privacy-Preserving Image Fusion

Abstract

Image fusion integrates complementary multi-source visual information, yet plaintext fusion poses severe privacy risks. Conventional lattice-based homomorphic encryption enables ciphertext computation but incurs substantial computational overhead and exhibits poor compatibility with image fusion tasks. This work investigates lightweight privacy-preserving image fusion built upon pixel scrambling. Any pixel-scrambling technique that only rearranges pixel coordinates without modifying pixel values inherently satisfies the homomorphic properties required for pixel-level spatial fusion. In this paper, we adopt full-size random permutation matrix scrambling as a representative pixel-disordering method for systematic theoretical and experimental verification. The scheme generates a secret key matching the resolution of test images; it merely reorders pixel positions while preserving all original intensity values, allowing direct cipher-domain fusion that yields distortion-free outputs for averaging, weighted averaging, maximum-value and minimum-value fusion rules. Free from intricate lattice calculations and ciphertext expansion, the proposed lightweight framework achieves an optimal trade-off among security, computational efficiency and fusion quality for cloud computing scenarios.