Pindrop it! Audio and Visual Deepfake Countermeasures for Robust Detection and Fine Grained-Localization
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866917041089282048 |
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| author | Klein, Nicholas Tak, Hemlata Fullwood, James Regmi, Krishna Spinoulas, Leonidas Sivaraman, Ganesh Chen, Tianxiang Khoury, Elie |
| author_facet | Klein, Nicholas Tak, Hemlata Fullwood, James Regmi, Krishna Spinoulas, Leonidas Sivaraman, Ganesh Chen, Tianxiang Khoury, Elie |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2508_08141 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Pindrop it! Audio and Visual Deepfake Countermeasures for Robust Detection and Fine Grained-Localization Klein, Nicholas Tak, Hemlata Fullwood, James Regmi, Krishna Spinoulas, Leonidas Sivaraman, Ganesh Chen, Tianxiang Khoury, Elie Computer Vision and Pattern Recognition Sound Audio and Speech Processing 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. |
| title | Pindrop it! Audio and Visual Deepfake Countermeasures for Robust Detection and Fine Grained-Localization |
| topic | Computer Vision and Pattern Recognition Sound Audio and Speech Processing |
| url | https://arxiv.org/abs/2508.08141 |