Private‐encoder: Enforcing privacy in latent space for human face images

Abstract

The explosive growth of various computer vision technologies generates a tremendous amount of visual data online every day. In addition to bringing convenience and revolutionizing our daily life, image data also reveal a wide range of sensitive information and pose unprecedented privacy leakage risks. Particularly, in the case of photos contain human faces, people can easily access those face images on social media without any consent, and the misuse of personal information could cause serious privacy violation to individuals. Therefore, it is essential to consider sanitizing people's identity information when using images containing human faces. As a result, there has been rapid development in the area of facial anonymization, also called image de-identification. However, due to the emergence of numerous deep-learning based attacks, traditional anonymization methods such as blurring and mosaic are weak and ineffective to protect individual's privacy in face images. To respond to this challenge, this article proposes a novel de-identification method that utilizes a deep neural network. The proposed framework encompasses two modules: encoder network and generator network. The encoder transforms a face image into a high-semantic latent vector of codes, which will be de-identified according to the differential privacy criterion. The generator leverages the unconditional generative adversarial network to synthesize high-quality images based on the modified latent codes from the encoder.Extensive experimental results indicate that our proposed model can protect image privacy while keeping the processed image visual realistic.

Online New Media Oriented Privacy Data Recognition Mechanism Based on Deep Learning

“…Ref. [29] put forward a neural network model including encoder and generator, which utilizes the encoder pairs to convert face images into high-semantic potential code vectors and add differential privacy.…”

Face image de‐identification by feature space adversarial perturbation

“…Simple obfuscation has been shown to be ineffective against DNN‐based recognizers 27,28 . Therefore, some researchers have started to use GAN and AE to generate content to replace the sensitive information in the images 29‐34 . For example, Sun et al 29 proposed GAN‐based head in‐painting to remove the original identities.…”

“…27,28 Therefore, some researchers have started to use GAN and AE to generate content to replace the sensitive information in the images. [29][30][31][32][33][34] For example, Sun et al 29 proposed GAN-based head in-painting to remove the original identities. Hukkelås et al 11 proposed a CGAN-based architecture to anonymize faces without destroying the data distribution of the original image.…”