Robust Frame-Level Detection for Deepfake Videos With Lightweight Bayesian Inference Weighting

Deepfake threatens the authenticity of the information in artificial intelligence Internet of Things (IoT) systems. Recently, several deepfake detection methods have been proposed in academia and industry for securing the authenticity of visual information in the face of artificial intelligence advances. Frame-level detection methods, a widely employed security method against deepfake, have a small model size and offer real-time responsiveness, despite basing their classification decision only on the information contained within the frame they are evaluating. We propose a new lightweight frame-level detection technique based on Bayesian inference weighting (BIW) to improve the robustness of existing frame-level detection models. Our proposed BIW technique employs the Naive Bayesian algorithm to estimate the reliability of any candidate model's detection results. Comprehensive experiments were conducted on the attacked data sets by four designed video interference approaches and edge computing platform, showing that BIW enhances the robustness of all the baselines and improves their detection accuracy with a real-time response.