  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.
