Deepfake detection methods have shown promising results in recognizing forgeries within a given dataset, where training and testing take place on the in-distribution dataset. However, their performance deteriorates significantly when presented with unseen samples. As a result, a reliable deepfake detection system must remain impartial to forgery types, appearance, and quality for guaranteed generalizable detection performance. Despite various attempts to enhance cross-dataset generalization, the problem remains challenging, particularly when testing against common post-processing perturbations, such as video compression or blur. Hence, this study introduces a deepfake detection framework, leveraging a self-supervised pre-training model that delivers exceptional generalization ability, withstanding common corruptions and enabling feature explainability. The framework comprises three key components: a feature extractor based on vision Transformer architecture that is pre-trained via self-supervised contrastive learning methodology, a graph convolution network coupled with a Transformer discriminator, and a graph Transformer relevancy map that provides a better understanding of manipulated regions and further explains the model's decision. To assess the effectiveness of the proposed framework, several challenging experiments are conducted, including in-data distribution performance, cross-dataset, cross-manipulation generalization, and robustness against common post-production perturbations. The results achieved demonstrate the remarkable effectiveness of the proposed deepfake detection framework, surpassing the current state-of-the-art approaches.

翻译：深度伪造检测方法在特定数据集内的训练和测试中已展现出识别伪造内容的潜力，但在面对未见样本时性能显著下降。因此，可靠的深度伪造检测系统需保持对伪造类型、外观和质量的鲁棒性，以确保泛化检测能力。尽管已有多种尝试提升跨数据集泛化性能，但在应对视频压缩或模糊等常见后处理扰动时，该问题仍具挑战性。为此，本文提出一种深度伪造检测框架，采用自监督预训练模型，具备卓越的泛化能力、抗常见干扰性及特征可解释性。该框架包含三个核心组件：基于视觉Transformer架构的特征提取器（通过自监督对比学习预训练）、图卷积网络与Transformer判别器的耦合模块，以及图Transformer相关性映射图——可增强对篡改区域的理解并解释模型决策。为评估框架有效性，我们开展了多项挑战性实验，包括分布内性能、跨数据集/跨操作泛化性，以及抗常见后期制作扰动的鲁棒性。结果表明，所提深度伪造检测框架表现卓越，超越了现有最新方法。