Advances in image generation technologies have raised growing concerns about their potential misuse, particularly in producing misinformation and deepfakes. This creates an urgent demand for effective methods to detect AI-generated images (AIGIs). While progress has been made, achieving reliable performance across diverse generative models and scenarios remains challenging due to the absence of source-invariant features and the limited generalization of existing approaches. In this study, we investigate the potential of using image entropy as a discriminative cue for AIGI detection and propose Multi-granularity Local Entropy Patterns (MLEP), a set of feature maps computed based on Shannon entropy from shuffled small patches at multiple image scales. MLEP effectively captures pixel dependencies across scales and dimensions while disrupting semantic content, thereby reducing potential content bias. Based on MLEP, we can easily build a robust CNN-based classifier capable of detecting AIGIs with enhanced reliability. Extensive experiments in an open-world setting, involving images synthesized by 32 distinct generative models, demonstrate that our approach achieves substantial improvements over state-of-the-art methods in both accuracy and generalization. Our code and models are available at https://www.github.com/fkeufss/MLEP/.

Array Tomographic Synthetic Aperture Radar (TomoSAR) is an advanced technique with elevation resolution through acquiring images from different angles. Due to the special single-pass mode, there is no temporal decorrelation caused by revisit time between multiple images. Therefore, the accuracy of the elevation reconstruction of the observed scene can be improved, especially in mountain area. In this paper, a framework to achieve 3D reconstruction in mountain area for Array TomoSAR is proposed. Furthermore, the complex overlays and terrain continuity are fully considered. To validate the effectiveness of the proposed framework, the experimental results are compared to SRTM-1 data obtained by Shuttle Radar Topography Mission (SRTM). Also, the original 2D image with scattering intensity is utilized.