GAN Detection

Detection and forensics of imagery generated by Generative Adversarial Networks (GANs). As GANs have become increasingly capable of synthesizing photorealistic images and video, forensic techniques to distinguish synthetic from real imagery have become critical for online misinformation detection and media authentication.

GAN detection approaches fall into two broad categories: architectural analysis (identifying fingerprints left by generator networks) and learning-based detection (training classifiers to distinguish fake from real). Architectural approaches tend to be more interpretable and may generalize better across GAN variants, while learning-based methods can achieve higher accuracy but risk being circumvented by adversarial training.

Key papers

• Detecting GAN-generated Imagery using Color Cues — Forensic detection via color formation properties and saturation statistics

Related topics

• Deepfake Detection (application domain)
• Synthetic Media Detection (broader category)
• Image forensics (methodology)