Multimodal deepfakes can exhibit subtle visual artifacts and cross-modal inconsistencies, which remain challenging to detect, especially when detectors are trained primarily on curated synthetic forgeries. Such synthetic dependence can introduce dataset and generator bias, limiting scalability and robustness to unseen manipulations. We propose SAVe, a self-supervised audio-visual deepfake detection framework that learns entirely on authentic videos. SAVe generates on-the-fly, identity-preserving, region-aware self-blended pseudo-manipulations to emulate tampering artifacts, enabling the model to learn complementary visual cues across multiple facial granularities. To capture cross-modal evidence, SAVe also models lip-speech synchronization via an audio-visual alignment component that detects temporal misalignment patterns characteristic of audio-visual forgeries. Experiments on FakeAVCeleb and AV-LipSync-TIMIT demonstrate competitive in-domain performance and strong cross-dataset generalization, highlighting self-supervised learning as a scalable paradigm for multimodal deepfake detection.

翻译：多模态深度伪造可能呈现出微妙的视觉伪影和跨模态不一致性，这对其检测构成挑战，尤其当检测器主要基于精心制作的合成伪样本进行训练时。此类合成依赖性会引入数据集和生成器偏差，限制了对未见操作的扩展性和鲁棒性。我们提出SAVe——一种完全基于真实视频学习的自监督音视频深度伪造检测框架。SAVe会即时生成保留身份、区域感知的自混合伪操作以模拟篡改伪影，使模型能够学习跨多种面部粒度的互补视觉线索。为捕获跨模态证据，SAVe还通过音视频对齐组件建模唇语-语音同步，能够检测音视频伪造中特有的时间错位模式。在FakeAVCeleb和AV-LipSync-TIMIT上的实验表明，该方法在领域内性能具有竞争力，且具备强大的跨数据集泛化能力，彰显了自监督学习作为多模态深度伪造检测可扩展范式的潜力。