Active Defense for Deepfakes Using Watermark-Guided Original Face Recovery

At present, active defense strategies based on digital watermarking mainly rely on post-event watermark extraction, which verifies the occurrence of deepfake events by measuring the degree of watermark degradation, or on adversarial watermarks to interfere with image generation. To overcome these limitations, we propose a unified watermarking framework that can restore the original content of images tampered with by deepfakes. This scheme integrates three core components: an encoder for watermark pre-embedding, a decoder for robust watermark extraction, and a face restorer for watermark-guided image restoration. Numerous experiments have shown that this method has achieved good results in terms of extraction accuracy and recovery performance, thereby verifying the effectiveness of this approach.