# A Novel Generative Model for Face Privacy Protection in Video Surveillance with Utility Maintenance
## 2 Related Work
The principle of Differential Privacy (DP) [36] is adopted by many face de-identification technologies [37]. [PERSON] [38] proposed the SDC-DP algorithm, a novel noise dynamic allocation algorithm based on differential privacy using the standard deviation circle radius, which effectively reduces the relative error and improves the accuracy. [PERSON] [39] introduced a neural network privacy analysis method based on the f-differential [40], which can boost forecast accuracy without going over the privacy budget by tweaking some model parameters. In [41], a Privacy-Preserving Adversarial Protector Network (PPAPNet) was developed as an improved technique for adding noise to the face. Experiments revealed that the PPAPNet performs remarkably in converting original images into high-quality de-identified images and resisting inversion attacks [42]. [PERSON] [43] directly processed face images in the pixel space to realize DP, regardless of the image's distribution characteristics. On this premise, the exponential mechanism proposed can provide superior visual quality for image confusion using the Laplacian mechanism, along with strong universality. On the contrary, [PERSON] [44] proposed Privacy using EigEnface Perturbation (PEEP), which utilizes local DP to add perturbations to image distribution features and stores these perturbed data on third-party servers to avoid privacy attacks such as model memorization attacks [45] or membership inference [46]. In [47], a Privacy-Preserving Semi-Generative Adversarial Network (PPSGAN) was proposed, which uses a self-attention mechanism [48] to add noise to the category-independent features of each image selectively, allowing the resulting image to preserve the original label. The PPSGAN is more practical than many common technologies, such as image filtering, random noise addition, and even, GAN generation. In addition to modifying faces directly, many studies solve the privacy protection problem in video surveillance using other angles. Reference [49] proposed a Sliding Window Publication (SWP) algorithm for face image publishing and sorting, innovatively converting the privacy security issue of images into that of the data stream, opening up a completely different perspective for image privacy protection. Reference [50] presented a secure video transmission strategy, which uses watermarking and video scrambling technology, making the multimedia stream low quality and unavailable in transmission, until it is decoded at the authorized endpoint. In [51], a cycle Vector-Quantized Variational Autoencoder was proposed to encode and decode the video and complete the task of privacy protection using multiple heterogeneous data sources in the video. This method uses a fusion mechanism to integrate the video and extracted audio and regards the extracted audio as random noise with a non-pattern distribution to realize visual information hiding. It has excellent performance in video compression, video reconstruction, and visual quality maintenance. The current research mainly focuses on a specific application scenario, and the evaluation standard of image quality was fixed in the process of the model design. Although these techniques have achieved extraordinary results, their application scope is strictly limited and their adaptability is weak to meet different service evaluation metrics. At the same time, the quality requirements of actual service applications are often varied and even abstract (difficult to judge by vision or a simple function). To overcome the restrictions of the approaches mentioned above, it is very valuable to design privacy protection and quality maintenance technology for different services. This paper proposes a novel de-identification and quality maintenance framework, QM-VAE. We treated the specific service scenario as a black box instead of defining specific calculation formulas, making our methods flexible, adaptability, and highly efficient.