PrivacyProber: Assessment and Detection of Soft-Biometric Privacy-Enhancing Techniques
This addresses privacy concerns for users of face recognition technology by revealing vulnerabilities in existing privacy methods, though it is incremental as it builds on prior techniques without introducing a new paradigm.
The paper tackles the problem of assessing the robustness of soft-biometric privacy-enhancing techniques in face recognition by investigating how much suppressed attribute information can be recovered, and finds that their proposed PrivacyProber framework can restore a considerable amount of information across different techniques and datasets.
Soft-biometric privacy-enhancing techniques represent machine learning methods that aim to: (i) mitigate privacy concerns associated with face recognition technology by suppressing selected soft-biometric attributes in facial images (e.g., gender, age, ethnicity) and (ii) make unsolicited extraction of sensitive personal information infeasible. Because such techniques are increasingly used in real-world applications, it is imperative to understand to what extent the privacy enhancement can be inverted and how much attribute information can be recovered from privacy-enhanced images. While these aspects are critical, they have not been investigated in the literature. We, therefore, study the robustness of several state-of-the-art soft-biometric privacy-enhancing techniques to attribute recovery attempts. We propose PrivacyProber, a high-level framework for restoring soft-biometric information from privacy-enhanced facial images, and apply it for attribute recovery in comprehensive experiments on three public face datasets, i.e., LFW, MUCT and Adience. Our experiments show that the proposed framework is able to restore a considerable amount of suppressed information, regardless of the privacy-enhancing technique used, but also that there are significant differences between the considered privacy models. These results point to the need for novel mechanisms that can improve the robustness of existing privacy-enhancing techniques and secure them against potential adversaries trying to restore suppressed information.