A Quality-Centric Framework for Generic Deepfake Detection

Understand

Detecting AI-generated images, particularly deepfakes, has become increasingly crucial, with the primary challenge being the generalization to previously unseen manipulation methods.

• This paper tackles this issue by leveraging the forgery quality of training data to improve the generalization performance of existing deepfake detectors.
• Generally, the forgery quality of different deepfakes varies: some have easily recognizable forgery clues, while others are highly realistic.
• Existing works often train detectors on a mix of deepfakes with varying forgery qualities, potentially leading detectors to short-cut the easy-to-spot artifacts from low-quality forgery samples, thereby hurting generalization performance.