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This paper introduces Modality-Specific Frequency Distillation (MSFD), a novel framework for continual video deepfake detection that separates video features into spatial, temporal, and spatiotemporal modalities. By preserving each modality independently during model updates, MSFD effectively adapts to evolving deepfake patterns that leverage different cues. Experimental results demonstrate that MSFD outperforms existing state-of-the-art methods in maintaining detection performance across various continual deepfake video scenarios.
Deepfake video detection can be revolutionized by a framework that preserves spatial and temporal cues independently, leading to superior adaptation to new forgery patterns.
The continuous emergence of high-quality video deepfakes requires detectors that continually adapt to new forgery patterns, yet existing approaches, which are designed for deepfake images, fail to capture video-specific cues. Unlike deepfake images that contain only spatial artifacts, deepfake videos leave distinct evidence along both spatial and temporal axes, necessitating the separate preservation of each modality during sequential model updates. To overcome this limitation, we introduce a continual deepfake video detection framework, Modality-Specific Frequency Distillation (MSFD), that explicitly decomposes video features into spatial, temporal, and spatiotemporal modalities in the frequency domain. This decomposition enables independent preservation of each modality, as different deepfake video types exhibit varying reliance on spatial and temporal cues across tasks. Furthermore, MSFD adopts a cross-modality decorrelation loss that encourages spatiotemporal representations to remain orthogonal to single-modality cues. Extensive experiments show that our framework achieves stronger adaptation and preserves performance more effectively than state-of-the-art methods across diverse continual deepfake video scenarios.
