FALCON-Net: Feature Aggregation of Local Patterns for AI-Generated Image Detection

FALCON-Net is a detection system designed to identify AI-generated images by analyzing their technical flaws. The system works by examining two key weaknesses in generated images: the lack of device-specific sensor noise (natural imperfections that real cameras add) and unnatural pixel intensity variations that result from oversimplified generation processes. FALCON-Net combines two analysis modules (one for noise patterns and one for local pixel variations) to reliably distinguish AI-generated images from real ones, even when tested on image generation models it wasn't trained on.