The Effectiveness of Temporal Dependency in Deepfake Video Detection
Deepfakes are a form of synthetic image generation used to generate fake videos of individuals for malicious purposes. The resulting videos may be used to spread misinformation, reduce trust in media, or as a form of blackmail. These threats necessitate automated methods of deepfake video detection. This paper investigates whether temporal information can improve the deepfake detection performance of deep learning models. To investigate this, we propose a framework that classifies new and existing approaches by their defining characteristics. These are the types of feature extraction: automatic or manual, and the temporal relationship between frames: dependent or independent. We apply this framework to investigate the effect of temporal dependency on a model's deepfake detection performance. We find that temporal dependency produces a statistically significant (p < 0.05) increase in performance in classifying real images for the model using automatic feature selection, demonstrating that spatio-temporal information can increase the performance of deepfake video detection models.
Code (0)
등록된 구현이 없습니다.
Tasks
DeepFake DetectionFace Swappingfeature selectionImage GenerationMisinformationSimilar Papers 제목 키워드 기반
Reduced Spatial Dependency for More General Video-level Deepfake Detection
As one of the prominent AI-generated content, Deepfake has raised significant safety concerns. Although it has been demonstrated that temporal consistency cues offer better generalization capability, existing methods bas…
DeepFake DetectionFace SwappingDeepfake Video Detection with Spatiotemporal Dropout Transformer
While the abuse of deepfake technology has caused serious concerns recently, how to detect deepfake videos is still a challenge due to the high photo-realistic synthesis of each frame. Existing image-level approaches oft…
Data AugmentationFace SwappingISTVT: Interpretable Spatial-Temporal Video Transformer for Deepfake Detection
With the rapid development of Deepfake synthesis technology, our information security and personal privacy have been severely threatened in recent years. To achieve a robust Deepfake detection, researchers attempt to exp…
DeepFake DetectionFace SwappingExtending Information Bottleneck Attribution to Video Sequences
We introduce VIBA, a novel approach for explainable video classification by adapting Information Bottlenecks for Attribution (IBA) to video sequences. While most traditional explainability methods are designed for image …
DeepFake DetectionFace SwappingOptical Flow EstimationVideo ClassificationEfficient Temporally-Aware DeepFake Detection using H.264 Motion Vectors
Video DeepFakes are fake media created with Deep Learning (DL) that manipulate a person's expression or identity. Most current DeepFake detection methods analyze each frame independently, ignoring inconsistencies and unn…
DeepFake DetectionFace SwappingOptical Flow Estimation