DeepAI AI Chat
Log In Sign Up

Robust Attacks on Deep Learning Face Recognition in the Physical World

by   Meng Shen, et al.

Deep neural networks (DNNs) have been increasingly used in face recognition (FR) systems. Recent studies, however, show that DNNs are vulnerable to adversarial examples, which can potentially mislead the FR systems using DNNs in the physical world. Existing attacks on these systems either generate perturbations working merely in the digital world, or rely on customized equipments to generate perturbations and are not robust in varying physical environments. In this paper, we propose FaceAdv, a physical-world attack that crafts adversarial stickers to deceive FR systems. It mainly consists of a sticker generator and a transformer, where the former can craft several stickers with different shapes and the latter transformer aims to digitally attach stickers to human faces and provide feedbacks to the generator to improve the effectiveness of stickers. We conduct extensive experiments to evaluate the effectiveness of FaceAdv on attacking 3 typical FR systems (i.e., ArcFace, CosFace and FaceNet). The results show that compared with a state-of-the-art attack, FaceAdv can significantly improve success rate of both dodging and impersonating attacks. We also conduct comprehensive evaluations to demonstrate the robustness of FaceAdv.


page 7

page 10


Robust Physical-World Attacks on Face Recognition

Face recognition has been greatly facilitated by the development of deep...

Real-World Adversarial Examples involving Makeup Application

Deep neural networks have developed rapidly and have achieved outstandin...

Dual Attention Suppression Attack: Generate Adversarial Camouflage in Physical World

Deep learning models are vulnerable to adversarial examples. As a more t...

Adversarial Generative Nets: Neural Network Attacks on State-of-the-Art Face Recognition

In this paper we show that misclassification attacks against face-recogn...

Towards Assessing and Characterizing the Semantic Robustness of Face Recognition

Deep Neural Networks (DNNs) lack robustness against imperceptible pertur...

Controllable Evaluation and Generation of Physical Adversarial Patch on Face Recognition

Recent studies have revealed the vulnerability of face recognition model...

Hardware Accelerator for Adversarial Attacks on Deep Learning Neural Networks

Recent studies identify that Deep learning Neural Networks (DNNs) are vu...