Summary of Circumventing Shortcuts in Audio-visual Deepfake Detection Datasets with Unsupervised Learning, by Dragos-alexandru Boldisor et al.
Circumventing shortcuts in audio-visual deepfake detection datasets with unsupervised learning
by Dragos-Alexandru Boldisor, Stefan Smeu, Dan Oneata, Elisabeta Oneata
First submitted to arxiv on: 29 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); Sound (cs.SD); Audio and Speech Processing (eess.AS); 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 This paper highlights the crucial role of datasets in machine learning, particularly for safety-critical applications like deepfake detection. The authors reveal a previously unknown issue with widely used audio-video deepfake datasets: the leading silence feature, which allows almost perfect separation of real and fake samples based on this brief moment of silence alone. This feature is exploited by previous models, resulting in decreased performance when the silence is removed. To mitigate such biases, the paper proposes shifting from supervised to unsupervised learning, training models solely on real data. By aligning self-supervised audio-video representations, the authors demonstrate improved robustness in deepfake detection. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper shows how important it is to have good datasets for machine learning. It’s especially important for things like detecting fake videos that can be used to spread misinformation. The problem is that some popular datasets for this task have a hidden feature called “leading silence.” This means that fake videos often start with a very short moment of silence, and if you just look at this silence, you can tell whether the video is real or fake almost perfectly. Some models are good at using this feature to detect deepfakes, but they won’t work well if the leading silence is removed. To fix this problem, the paper suggests training models only on real data, without trying to match them with fake data. This helps get rid of any biases in the dataset and makes the models better at detecting deepfakes. |
Keywords
» Artificial intelligence » Machine learning » Self supervised » Supervised » Unsupervised
