Efficient Video Privacy Protection Against Malicious Face Recognition Models

Abstract

The proliferation of powerful facial recognition systems poses a serious threat to user privacy. Attackers could train highly accurate facial recognition models using public data on social platforms. Therefore, recent works have proposed image pre-processing techniques to protect user privacy. Without affecting people's normal viewing, these techniques add special noises into images, so that it would be difficult for attackers to train models with high accuracy. However, existing protection techniques are mainly designed for image data protection, and they cannot be directly applied for video data because of high computational overhead. In this paper, we propose an efficient protection method for video privacy that exploits unique features of video protection to eliminate computation redundancy for computational acceleration. The evaluation results under various benchmarks demonstrate that our method significantly outperforms the traditional methods by reducing computation overhead by 35.5%.

Keywords

• Computation reuse
• deep learning
• video privacy