Identifying and Resisting Adversarial Videos Using Temporal Consistency

Understand

Video classification is a challenging task in computer vision.

• Although Deep Neural Networks (DNNs) have achieved excellent performance in video classification, recent research shows adding imperceptible perturbations to clean videos can make the well-trained models output wrong labels with high confidence.
• In this paper, we propose an effective defense framework to characterize and defend adversarial videos.
• The proposed method contains two phases: (1) adversarial video detection using temporal consistency between adjacent frames, and (2) adversarial perturbation reduction via denoisers in the spatial and temporal domains respectively.