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.
@article{arxiv.2508.08141,
title = {Pindrop it! Audio and Visual Deepfake Countermeasures for Robust Detection and Fine Grained-Localization},
author = {Nicholas Klein and Hemlata Tak and James Fullwood and Krishna Regmi and Leonidas Spinoulas and Ganesh Sivaraman and Tianxiang Chen and Elie Khoury},
journal= {arXiv preprint arXiv:2508.08141},
year = {2025}
}