Locate and Verify: A Two-Stream Network for Improved Deepfake Detection
Chao Shuai, Jieming Zhong, Shuang Wu, Feng Lin, Zhibo Wang, Zhongjie Ba, Zhenguang Liu, Lorenzo Cavallaro, Kui Ren
Abstract
Deepfake has taken the world by storm, triggering a trust crisis. Current deepfake detection methods are typically inadequate in generalizability, with a tendency to overfit to image contents such as the background, which are frequently occurring but relatively unimportant in the training dataset. Furthermore, current methods heavily rely on a few dominant forgery regions and may ignore other equally important regions, leading to inadequate uncovering of forgery cues.
Topics & Concepts
OverfittingGeneralizability theoryComputer scienceArtificial intelligenceStormImage (mathematics)Current (fluid)Data miningMachine learningPattern recognition (psychology)Artificial neural networkStatisticsMathematicsGeologyOceanographyDigital Media Forensic DetectionGenerative Adversarial Networks and Image SynthesisAdversarial Robustness in Machine Learning