Wavelet-Enhanced CLIP with Noise Residual Learning for Generalizable Deepfake Detection

The rapid development of artificial intelligence-generated content (AIGC) methods has posed severe threats to security and privacy, as photorealistic face forgeries (deepfakes) can be easily generated by modern generative models. Since existing CLIP-based deepfake detection methods mainly focus on high-level semantic information, they may struggle to capture subtle tampering artifacts in the frequency domain, leading to unsatisfactory generalization ability. To address this issue, in this paper, a frequency-aware deepfake detection network, which is composed of a CLIP-based semantic branch and a residual-based noise branch, is proposed to jointly capture forgery traces in the spatial and frequency domains. In the semantic branch, a wavelet convolution module is incorporated with the image encoder of CLIP to enhance multi-frequency representations. To characterize statistical inconsistencies around forged boundaries, a constrained convolutional layer and a noise encoder are adopted to extract residual features, which are fused with semantic features through learnable weighted fusion. To further improve generalization ability, an image–text class prototype alignment strategy is proposed to align the fused visual features with text embeddings of pre-defined prompts corresponding to specific classes. Extensive experimental results on several benchmark datasets demonstrate that the proposed method achieves competitive performance against other state-of-the-art methods on cross-dataset evaluations.