AdvFaces: Adversarial Face Synthesis
| Autor: | Anil K. Jain, Debayan Deb, Jianbang Zhang |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
FOS: Computer and information sciences
Computer science media_common.quotation_subject Speech recognition Computer Vision and Pattern Recognition (cs.CV) 05 social sciences Computer Science - Computer Vision and Pattern Recognition 010501 environmental sciences 01 natural sciences Facial recognition system Adversarial system Face synthesis Salient Face (geometry) 0502 economics and business Obfuscation Quality (business) 050207 economics 0105 earth and related environmental sciences media_common Hacker |
| Zdroj: | IJCB |
| Popis: | Face recognition systems have been shown to be vulnerable to adversarial faces resulting from adding small perturbations to probe images. Such adversarial images can lead state-of-the-art face matchers to falsely reject a genuine subject (obfuscation attack) or falsely match to an impostor (impersonation attack). Current approaches to crafting adversarial faces lack perceptual quality and take an unreasonable amount of time to generate them. We propose, AdvFaces, an automated adversarial face synthesis method that learns to generate minimal perturbations in the salient facial regions via Generative Adversarial Networks. Once AdvFaces is trained, a hacker can automatically generate imperceptible face perturbations that can evade four black-box state-of-the-art face matchers with attack success rates as high as 97.22% and 24.30% at 0.1 % False Accept Rate, for obfuscation and impersonation attacks, respectively. |
| Databáze: | OpenAIRE |
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