Suppressing Forgery-Specific Shortcuts for Generalizable Deepfake Detection

• URL: http://arxiv.org/abs/2606.01843v1
• Date: Mon, 01 Jun 2026 07:54:58 GMT
• Title: Suppressing Forgery-Specific Shortcuts for Generalizable Deepfake Detection
• Abstract summary: Deepfake detection suffers from poor generalization across forgery methods.<n>Recent approaches attempt to improve generalization, but lack explicit mechanism to identify and suppress such shortcuts.<n>We propose Shortcut Subspace Suppression (S3) framework that explicitly characterizes and suppresses method-specific shortcuts.
• Score: 59.056076147081775
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deepfake detection suffers from poor generalization across forgery methods, as existing models tend to rely on spurious method-specific shortcuts that fail to transfer to unseen manipulations. While recent approaches attempt to improve generalization, they lack an explicit mechanism to identify and suppress such shortcuts in learned representations. In this work, we propose Shortcut Subspace Suppression (S^3) framework that explicitly characterizes and suppresses method-specific shortcuts via subspace modeling. Our key insight is that variations distinguishing different forgery methods capture method-specific artifacts and thus serve as an effective proxy for method-specific shortcuts. To this end, we train a lightweight linear probe for forgery method classification and perform Singular Value Decomposition (SVD) to extract the dominant shortcut subspace. Building on this formulation, we develop two complementary strategies to reduce shortcut reliance. During training, we softly suppress the shortcut subspace in feature representations, encouraging the model to rely on more generalizable cues for real/fake discrimination. At inference time, we introduce a training-free counterpart that attenuates neurons aligned with the identified shortcut directions, enabling plug-and-play generalization enhancement with improved interpretability. Extensive experiments on multiple benchmarks demonstrate that our method significantly improves cross-method generalization while maintaining strong in-domain performance. The code will be released upon acceptance of the submission.

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