Mayada Khairy
Recent progress in generative AI has made it increasingly easy to produce highly realistic satellite imagery. While this advancement enables a wide range of beneficial applications, it also raises serious concerns regarding data reliability, misinformation, and security in critical remote sensing scenarios. Detecting manipulated satellite images remains a challenging task, as they lack human-related cues and instead exhibit complex spatial structures, where artifacts are often subtle and widely distributed. This paper introduces a novel multi-scale framework based on Optical Scanning Holography (OSH) to address this challenge. OSH is employed as a physics-inspired transformation that maps input images into a more informative feature space, where spatial, frequency, and phase characteristics are jointly represented within a unified domain. To further improve feature extraction, OSH is applied at multiple scales to generate complementary representations, which are combined into a multi-channel input. A convolutional neural network (CNN) is then utilized to learn discriminative patterns from this enhanced feature space. Experimental results on a large-scale dataset of real and AI-generated images demonstrate the effectiveness of the proposed framework, achieving an accuracy of 99.31%.