Abstract
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.
Abstract (translated)
视觉和音频生成领域的最新技术层出不穷。这种新方法的快速涌现强调了需要开发出稳健的解决方案来检测视频中的合成内容。特别是在对图像、音频或两者进行精细调整的情况下，通过局部操作进行细微修改给检测算法带来了新的挑战。本文提出了解决深度伪造视频分类和定位问题的方法。这些方法提交给了ACM 1M Deepfakes Detection Challenge，在评估数据集TestA部分的时序定位任务中取得了最佳成绩，并在该分组的分类任务中排名第四。
URL
https://arxiv.org/abs/2508.08141
