AI-Synthesized Image Detection: Source Camera Fingerprinting to Discern the Authenticity of Digital Images

Detecting AI-synthesized images remains a challenge due to their increasing realism. Traditional methods often fall short in addressing this evolving landscape where testing images can be produced by more powerful generative models or combined with various post-processing operations. To address this, we propose a robust method capitalizing on the distinctive global fingerprints present in real images from physical cameras and fixed patterns in synthesized images generated by generative models. Our approach builds on adapting camera fingerprinting initially designed for source camera identification, leveraging global fingerprints from the mid-frequency band. We rigorously evaluated our methodology through cross-model testing, demonstrating its exceptional generalization capability across different generative models and datasets. Our results, validated on the $DF^{3}$ and CNN-aug datasets with a broader range of generative models, highlight the method’s effectiveness in detecting AI-synthesized images and distinguishing between generative models. Comparisons with state-of-the-art methods show that our approach achieves superior performance, particularly in scenarios involving post-processing operations. This camera fingerprinting approach stands out for its resilience and accuracy, offering a promising solution for image forensics in addressing the challenges posed by sophisticated AI-generated content.