Pindrop it! Audio and Visual Deepfake Countermeasures for Robust Detection and Fine Grained-Localization
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Abstract (arXiv)
The field of visual and audio generation is burgeoning with new state-of-the-art methods. This rapid proliferation of new techniques underscores the need for robust solutions for detecting synthetic content in videos. In particular, when fine-grained alterations via localized manipulations are performed in visual, audio, or both domains, these subtle modifications add challenges to the detection algorithms. This paper presents solutions for the problems of deepfake video classification and localization. The methods were submitted to the ACM 1M Deepfakes Detection Challenge, achieving the best performance in the temporal localization task and a top four ranking in the classification task for the TestA split of the evaluation dataset.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 26 of its 43 references that are in the map. First posted on arXiv 11 Aug 2025.
Builds on
26 of 43 references.
References · 26
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14 more
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Citing papers per year: 2026: 3.
3 papers.