Pindrop it! Audio and Visual Deepfake Countermeasures for Robust Detection and Fine Grained-Localization
It addresses the need for robust deepfake detection to combat synthetic content proliferation, which is crucial for security and media integrity, though it appears incremental as it builds on existing detection methods.
The paper tackled the problem of detecting and localizing deepfake videos with subtle manipulations in visual and audio domains, achieving best performance in temporal localization and top four in classification in the ACM 1M Deepfakes Detection Challenge.
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.