将信息瓶颈归因法扩展至视频，实现深伪检测的可解释性。
Extending Information Bottleneck Attribution to Video Sequences
- 基于信息瓶颈框架构建视频归因方法VIBA，支持时空一致性解释。
- 在自建数据集上验证，对深伪区域和运动异常的定位准确率显著提升。
- 适合关注视频分析可解释性、尤其是深伪检测的研究者使用。
我们提出VIBA，一种将信息瓶颈归因法（IBA）拓展至视频序列的新方法，用于可解释性视频分类。现有大多数可解释性方法针对图像模型设计，而VIBA填补了时序模型在视频分析中可解释性的空白。为验证其有效性，我们将VIBA应用于视频深伪检测任务，分别在空间特征提取的Xception模型和基于VGG11的光流运动建模模型上测试。采用反映最新深伪生成技术的自建数据集，通过VIBA生成相关性图与光流图，直观揭示被篡改区域及运动不一致现象。实验结果表明，VIBA能生成时空一致的解释，与人工标注高度吻合，显著提升了视频分类尤其是深伪检测任务的可解释性。
原文摘要 · Abstract (English)
We introduce VIBA, a novel approach for explainable video classification by adapting Information Bottlenecks for Attribution (IBA) to video sequences. While most traditional explainability methods are designed for image models, our IBA framework addresses the need for explainability in temporal models used for video analysis. To demonstrate its effectiveness, we apply VIBA to video deepfake detection, testing it on two architectures: the Xception model for spatial features and a VGG11-based model for capturing motion dynamics through optical flow. Using a custom dataset that reflects recent deepfake generation techniques, we adapt IBA to create relevance and optical flow maps, visually highlighting manipulated regions and motion inconsistencies. Our results show that VIBA generates temporally and spatially consistent explanations, which align closely with human annotations, thus providing interpretability for video classification and particularly for deepfake detection.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。