Image Deepfake Detection Technologies: A Comprehensive Investigation of Methodologies, Challenges, and Future Trends

Abstract

The rapid advancement of deep generative models, especially Generative Adversarial Networks (GANs) and Diffusion Models, has escalated the creation of highly realistic synthetic media, posing significant threats to information security through misinformation and fraud. The core of current detection methodologies encompasses four primary domains: Spatial Domain methods take the use of specialized CNN architectures like Xception, utilizing depthwise separable convolutions to isolate subtle "CNN fingerprints" such as checkerboard artifacts caused by upscaling operations; Physical deepfake detection use the laws of physics, such as analyzing corneal specular backscattering to verify lighting consistency or measuring geometric deviations in pupil shapes; Frequency Domain Analysis adopts transforms like DWT and DT-CWT to figure out statistical footprints that generative models struggle to hide while preserving spatial information. Although such achievements have been achieved, image deepfake detection still faces critical bottlenecks: generalization across unknown datasets and generative algorithms, as well as robustness against adversarial attacks that erase microscopic traces.