Privacy Protection via Imperceptible Face Masking: A Dynamic Approach based on HyperNet

Abstract

The proliferation of video recording devices and facial recognition technology has led to significant privacy concerns, as surveillance systems can capture and identify individuals without their consent. Traditional facial obfuscation systems, which introduce pixel-level perturbations to images, aim to protect privacy by preventing unauthorized facial recognition. However, these systems are vulnerable to inversion attacks, where attackers can reverse the perturbations to restore original images, compromising privacy. This thesis addresses these vulnerabilities by proposing HyperObf, a novel approach utilizing HyperNet technology to generate unique obfuscation networks for each user. HyperObf ensures that each user’s images are distinctly protected, making it challenging for attackers to reverse-engineer the obfuscations. Our experiments demonstrate that inversion attacks can significantly degrade the protection offered by static obfuscation systems, with restored images achieving face recognition accuracy close to that of original images.
In contrast, HyperObf effectively mitigates these attacks, reducing the attack success rate to 30% compared to 60% for existing methods. Additionally, HyperObf can generate 100 personalized MaskNets in 0.2 seconds using high-performance computing resources. These findings highlight the potential of HyperObf to enhance privacy protection against unauthorized facial recognition and inversion attacks in the digital age.