Optimized Frequency Collaborative Strategy Drives AI Image Detection

Artificial intelligence (AI) image generation powered by large language model (large language modelss (LLMs)) enables highly realistic image synthesis and manipulation, posing significant security risks to Internet of Things (IoT) systems, particularly in identity authentication and data integrity. Although multidomain synthetic image detection has advanced, how spatial and frequency domain features affect decision making is still an open question, causing models to emphasize less critical areas and fall into local optimality. Through multidomain empirical analysis, we reveal the common contrastive differences in image textures and further demonstrate that frequency analysis helps capture the spectral differences in images. Building on this, we propose the collaborative spatial and frequency detector (CSFD). First, the image is decomposed into strong and weak texture regions in the spatial domain. Second, it aggregates different components in the frequency domain, using weighted channel attention to enhance spatial reasoning. Finally, the texture regions are combined to discriminate synthetic images. Experimental results demonstrate that incorporating channel attention based on frequency information improves the detection of synthetic images with spectral defects. On a comprehensive AI-generated image detection benchmark, the proposed method improves accuracy by 2.61% over current methods. Our code is available at https://github.com/JackPotProject/Frequency-Collaborative.