2018 · 997 citations · 27 references
ComputingComputer ScienceArtificial IntelligenceComputer VisionMachine LearningDeep LearningFace RecognitionGenerative ModelingNeural NetworksPattern RecognitionDeepfake DetectionGenerative Adversarial NetworksNeural Video GenerationPhysiological Signal AnalysisPhysiological Signal ProcessingPhysiological Signal Representation
The new developments in deep generative networks have significantly improve the quality and efficiency in generating realistically-looking fake face videos. In this work, we describe a new method to expose fake face videos generated with deep neural network models. Our method is based on detection of eye blinking in the videos, which is a physiological signal that is not well presented in the synthesized fake videos. Our method is evaluated over benchmarks of eye-blinking detection datasets and shows promising performance on detecting videos generated with DNN based software DeepFake.
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Deep Residual Learning for Image Recognition
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Adam: A Method for Stochastic Optimization
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