Fantômas: Evaluating Reversibility of Face Anonymizations Using a General Deep Learning Attacker

Biometric data is a rich source of information that can be used to identify individuals and infer private information about them. To mitigate this privacy risk, anonymization techniques employ transformations on clear data to obfuscate sensitive information, all while retaining some utility of the data. Albeit published with impressive claims, they sometimes are not evaluated with convincing methodology. We hence are interested to which extent recently suggested anonymization techniques for obfuscating facial images are effective. More specifically, we test how easily they can be automatically reverted, to estimate the privacy they can provide. Our approach is agnostic to the anonymization technique as we learn a machine learning model on the clear and corresponding anonymized data. We find that 10 out of 14 tested face anonymization techniques are at least partially reversible, and six of them are at least highly reversible.

Understanding person identification via gait

Gait recognition is the process of identifying humans from their bipedal locomotion such as walking or running. As such gait data is privacy sensitive information and should be anonymized. With the rise of more and higher quality gait recording techniques, such as depth cameras or motion capture suits, an increasing amount of high-quality gait data becomes available which requires anonymization. As a first step towards developing anonymization techniques for high-quality gait data, we study different aspects of movement data to quantify their contribution to the gait recognition process. We first extract categories of features from the literature on human gait perception and then design computational experiments for each of the categories which we run against a gait recognition system. Our results show that gait anonymization is a challenging process as the data is highly redundant and interdependent.