Summary of Understanding Audiovisual Deepfake Detection: Techniques, Challenges, Human Factors and Perceptual Insights, by Ammarah Hashmi et al.
Understanding Audiovisual Deepfake Detection: Techniques, Challenges, Human Factors and Perceptual Insights
by Ammarah Hashmi, Sahibzada Adil Shahzad, Chia-Wen Lin, Yu Tsao, Hsin-Min Wang
First submitted to arxiv on: 12 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Multimedia (cs.MM); Sound (cs.SD); Image and Video Processing (eess.IV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Deep Learning has been successfully applied in various fields, including deepfake detection, which is crucial for preventing deceitful content. Despite extensive research on unimodal deepfake detection, identifying complex deepfakes through joint analysis of audio and visual streams remains relatively unexplored. To address this gap, the paper provides an overview of audiovisual deepfake generation techniques, applications, and their consequences, followed by a comprehensive review of state-of-the-art methods that combine audio and visual modalities to enhance detection accuracy. The strengths and limitations of these methods are summarized and critically analyzed. Additionally, existing open-source datasets for deep learning-based audiovisual methods in video forensics are discussed. By bridging the gap between unimodal and multimodal approaches, this paper aims to improve the effectiveness of deepfake detection strategies and guide future research in cybersecurity and media integrity. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Deepfakes are fake videos that can be used to deceive people. Right now, there isn’t a good way to detect these complex fakes by looking at both audio and video together. This paper helps fill this gap by showing how different methods work together to improve detection accuracy. It also talks about the open-source datasets that can help researchers learn more about detecting deepfakes. |
Keywords
* Artificial intelligence * Deep learning
