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

19 Oct 2022  ·  Julian Todt, Simon Hanisch, Thorsten Strufe ·

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.

PDF Abstract
No code implementations yet. Submit your code now


Results from the Paper

  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.


No methods listed for this paper. Add relevant methods here