通过融合自监督任务特征，提升通用深度伪造检测效果
Fusion-SSAT: Unleashing the Potential of Self-supervised Auxiliary Task by Feature Fusion for Generalized Deepfake Detection
- 将自监督辅助任务的特征与主任务融合，增强表示能力
- 在7个数据集上跨数据集检测准确率超越现有最优方法
- 适合需要强泛化能力的深度伪造检测场景
本文旨在释放自监督学习作为辅助任务在通用深度伪造检测中的潜力。通过探索不同训练策略组合，发现融合自监督任务的特征表示能有效提升主任务性能。该融合特征同时保留了自监督与主任务的特性，显著增强模型泛化能力。我们在包括DF40、FaceForensics++、Celeb-DF、DFD、FaceShifter、UADFV在内的多个数据集上进行实验，结果表明，在跨数据集评估中，该方法优于当前最先进的检测器。
原文摘要 · Abstract (English)
In this work, we attempted to unleash the potential of self-supervised learning as an auxiliary task that can optimise the primary task of generalised deepfake detection. To explore this, we examined different combinations of the training schemes for these tasks that can be most effective. Our findings reveal that fusing the feature representation from self-supervised auxiliary tasks is a powerful feature representation for the problem at hand. Such a representation can leverage the ultimate potential and bring in a unique representation of both the self-supervised and primary tasks, achieving better performance for the primary task. We experimented on a large set of datasets, which includes DF40, FaceForensics++, Celeb-DF, DFD, FaceShifter, UADFV, and our results showed better generalizability on cross-dataset evaluation when compared with current state-of-the-art detectors.
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