  Recent research has established the possibility of deducing soft-biometric
attributes such as age, gender and race from an individual's face image with
high accuracy. However, this raises privacy concerns, especially when face
images collected for biometric recognition purposes are used for attribute
analysis without the person's consent. To address this problem, we develop a
technique for imparting soft biometric privacy to face images via an image
perturbation methodology. The image perturbation is undertaken using a
GAN-based Semi-Adversarial Network (SAN) - referred to as PrivacyNet - that
modifies an input face image such that it can be used by a face matcher for
matching purposes but cannot be reliably used by an attribute classifier.
Further, PrivacyNet allows a person to choose specific attributes that have to
be obfuscated in the input face images (e.g., age and race), while allowing for
other types of attributes to be extracted (e.g., gender). Extensive experiments
using multiple face matchers, multiple age/gender/race classifiers, and
multiple face datasets demonstrate the generalizability of the proposed
multi-attribute privacy enhancing method across multiple face and attribute
classifiers.
