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Data Poisoning Won’t Save You From Facial Recognition
Evani Radiya-Dixit · Florian Tramer

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in A Blessing in Disguise: The Prospects and Perils of Adversarial Machine Learning »

Data poisoning has been proposed as a compelling defense against facial recognition models trained on Web-scraped pictures. By perturbing the images they post online, users can fool models into misclassifying future (unperturbed) pictures.

We demonstrate that this strategy provides a false sense of security, as it ignores an inherent asymmetry between the parties: users' pictures are perturbed once and for all before being published and scraped, and must thereafter fool all future models---including models trained adaptively against the users' past attacks, or models that use technologies discovered after the attack.

We evaluate two poisoning attacks against large-scale facial recognition, Fawkes 500,000+ downloads) and LowKey. We demonstrate how an ``oblivious'' model trainer can simply wait for future developments in computer vision to nullify the protection of pictures collected in the past. We further show that an adversary with black-box access to the attack can train a robust model that resists the perturbations of collected pictures.

We caution that facial recognition poisoning will not admit an ''arms race'' between attackers and defenders. Once perturbed pictures are scraped, the attack cannot be changed so any future defense irrevocably undermines users' privacy.

Author Information

Evani Radiya-Dixit (Stanford University)
Florian Tramer (Stanford University)

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