Realism to Deception: Investigating Deepfake Detectors Against Face Enhancement
It addresses a vulnerability in deepfake detection for forensic applications, but the work is incremental as it systematically evaluates existing enhancement methods rather than proposing new solutions.
This study investigated how face enhancement techniques, which improve perceptual quality, can degrade deepfake detector accuracy by distorting biometric features, finding that basic filters reduce detection accuracy by up to 64.63% and GAN-based methods by up to 75.12%.
Face enhancement techniques are widely used to enhance facial appearance. However, they can inadvertently distort biometric features, leading to significant decrease in the accuracy of deepfake detectors. This study hypothesizes that these techniques, while improving perceptual quality, can degrade the performance of deepfake detectors. To investigate this, we systematically evaluate whether commonly used face enhancement methods can serve an anti-forensic role by reducing detection accuracy. We use both traditional image processing methods and advanced GAN-based enhancements to evaluate the robustness of deepfake detectors. We provide a comprehensive analysis of the effectiveness of these enhancement techniques, focusing on their impact on Naïve, Spatial, and Frequency-based detection methods. Furthermore, we conduct adversarial training experiments to assess whether exposure to face enhancement transformations improves model robustness. Experiments conducted on the FaceForensics++, DeepFakeDetection, and CelebDF-v2 datasets indicate that even basic enhancement filters can significantly reduce detection accuracy achieving ASR up to 64.63\%. In contrast, GAN-based techniques further exploit these vulnerabilities, achieving ASR up to 75.12\%. Our results demonstrate that face enhancement methods can effectively function as anti-forensic tools, emphasizing the need for more resilient and adaptive forensic methods.