文章基本信息

标题：Private Face Image Generation Method Based on Deidentification in Low Light
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作者：Beibei Dong ; Zhenyu Wang ; Zhihao Gu 等
期刊名称：Computational Intelligence and Neuroscience
印刷版ISSN：1687-5265
电子版ISSN：1687-5273
出版年度：2022
卷号：2022
DOI：10.1155/2022/5818180
语种：English
出版社：Hindawi Publishing Corporation
摘要：The existing face image recognition algorithm can accurately identify underexposed facial images, but the abuse of face image recognition technology can associate face features with personally identifiable information, resulting in privacy disclosure of the users. The paper puts forward a method for private face image generation based on deidentification under low light. First of all, the light enhancement and attenuation networks are pretrained using the training set, and low-light face images in the test set are input into the light enhancement network for photo enhancement. Then the facial area is captured by the face interception network, and corresponding latent code will be created through the latent code generation network and feature disentanglement will be done. Tiny noise will be added to the latent code by the face generation network to create deidentified face images which will be input in a light attenuation network to generate private facial images in a low-lighting style. At last, experiments show that, compared with other state-of-the-art algorithms, this method is more successful in generating low-light private face images with the most similar structure to original photos. It protects users’ privacy effectively by reducing the accuracy of the face recognition network, while also ensuring the practicability of the images.