Facial Features Matter: a Dynamic Watermark based Proactive Deepfake Detection Approach

TL;DR

This paper introduces a proactive deepfake detection method using dynamic watermarks based on facial features, improving security and generalization over traditional fixed watermark approaches.

Contribution

It proposes a novel facial feature-based watermarking mechanism and verification strategy that enhance deepfake detection robustness and security.

Findings

High detection accuracy across various deepfake techniques

Enhanced security against reverse inference attacks

Effective integration of steganography with dynamic watermarks

Abstract

Current passive deepfake face-swapping detection methods encounter significance bottlenecks in model generalization capabilities. Meanwhile, proactive detection methods often use fixed watermarks which lack a close relationship with the content they protect and are vulnerable to security risks. Dynamic watermarks based on facial features offer a promising solution, as these features provide unique identifiers. Therefore, this paper proposes a Facial Feature-based Proactive deepfake detection method (FaceProtect), which utilizes changes in facial characteristics during deepfake manipulation as a novel detection mechanism. We introduce a GAN-based One-way Dynamic Watermark Generating Mechanism (GODWGM) that uses 128-dimensional facial feature vectors as inputs. This method creates irreversible mappings from facial features to watermarks, enhancing protection against various reverse inference attacks. Additionally, we propose a Watermark-based Verification Strategy (WVS) that combines steganography with GODWGM, allowing simultaneous transmission of the benchmark watermark representing facial features within the image. Experimental results demonstrate that our proposed method maintains exceptional detection performance and exhibits high practicality on images altered by various deepfake techniques.