(PI-Net): Facial Image Obfuscation with Manipulable Semantics
Perceptual Indistinguishability-Net (PI-Net): Facial Image Obfuscation with Manipulable Semantics Jia-Wei Chen1,3 Li-Ju Chen3 Chia-Mu Yu2 Chun-Shien Lu1,3 1Institute of Information Science, Academia Sinica 2National Yang Ming Chiao Tung University 3Research Center for Information Technology Innovation, Academia Sinica Abstract Deepfakes [45], if used to replace sensitive semantics, can also mitigate privacy risks for identity disclosure [3, 15]. With the growing use of camera devices, the industry However, all of the above methods share a common has many image datasets that provide more opportunities weakness of syntactic anonymity, or say, lack of formal for collaboration between the machine learning commu- privacy guarantee. Recent studies report that obfuscated nity and industry. However, the sensitive information in faces can be re-identified through machine learning tech- the datasets discourages data owners from releasing these niques [33, 19, 35]. Even worse, the above methods are datasets. Despite recent research devoted to removing sen- not guaranteed to reach the analytical conclusions consis- sitive information from images, they provide neither mean- tent with the one derived from original images, after manip- ingful privacy-utility trade-off nor provable privacy guar- ulating semantics. To overcome the above two weaknesses, antees. In this study, with the consideration of the percep- one might resort to differential privacy (DP) [9], a rigorous tual similarity, we propose perceptual indistinguishability privacy notion with utility preservation. In particular, DP- (PI) as a formal privacy notion particularly for images. We GANs [1, 6, 23, 46] shows a promising solution for both also propose PI-Net, a privacy-preserving mechanism that the provable privacy and perceptual similarity of synthetic achieves image obfuscation with PI guarantee.
[Show full text]