Deepfake technology has given rise to a spectrum of novel and compelling
applications. Unfortunately, the widespread proliferation of high-fidelity fake
videos has led to pervasive confusion and deception, shattering our faith that
seeing is believing. One aspect that has been overlooked so far is that current
deepfake detection approaches may easily fall into the trap of overfitting,
focusing only on forgery clues within one or a few local regions. Moreover,
existing works heavily rely on neural networks to extract forgery features,
lacking theoretical constraints guaranteeing that sufficient forgery clues are
extracted and superfluous features are eliminated. These deficiencies culminate
in unsatisfactory accuracy and limited generalizability in real-life scenarios.
In this paper, we try to tackle these challenges through three designs: (1)
We present a novel framework to capture broader forgery clues by extracting
multiple non-overlapping local representations and fusing them into a global
semantic-rich feature. (2) Based on the information bottleneck theory, we
derive Local Information Loss to guarantee the orthogonality of local
representations while preserving comprehensive task-relevant information. (3)
Further, to fuse the local representations and remove task-irrelevant
information, we arrive at a Global Information Loss through the theoretical
analysis of mutual information. Empirically, our method achieves
state-of-the-art performance on five benchmark datasets.Our code is available
at https://github.com/QingyuLiu/Exposing-the-Deception, hoping to inspire
researchers.

通过提取多个非重叠的局部表征并将它们融合成全局的语义丰富特征，凭借信息瓶颈理论派生局部信息损失，保证局部表征的正交性同时保留全面的任务相关信息，通过互信息的理论分析得到全局信息损失，最终达到融合局部表征并且去除任务无关信息的目的，实证结果在五个基准数据集上取得了最新的性能，我们的代码可以在 https://github.com/QingyuLiu/Exposing-the-Deception 访问，希望能够激发研究人员的灵感。