# Invisible Mask: Practical Attacks on Face Recognition with Infrared

## TL;DR

This paper introduces a novel infrared-based adversarial attack on face recognition systems, demonstrating high success rates and highlighting a new security threat that is invisible to the naked eye.

## Contribution

We propose the first infrared adversarial attack method against face recognition, capable of bypassing or misleading systems without observable cues.

## Key findings

- Over 70% success rate in generating infrared adversarial examples
- Infrared attacks can impersonate victims and bypass surveillance
- Attacks are unobservable to humans and require small, discreet devices

## Abstract

Accurate face recognition techniques make a series of critical applications possible: policemen could employ it to retrieve criminals' faces from surveillance video streams; cross boarder travelers could pass a face authentication inspection line without the involvement of officers. Nonetheless, when public security heavily relies on such intelligent systems, the designers should deliberately consider the emerging attacks aiming at misleading those systems employing face recognition. We propose a kind of brand new attack against face recognition systems, which is realized by illuminating the subject using infrared according to the adversarial examples worked out by our algorithm, thus face recognition systems can be bypassed or misled while simultaneously the infrared perturbations cannot be observed by raw eyes. Through launching this kind of attack, an attacker not only can dodge surveillance cameras. More importantly, he can impersonate his target victim and pass the face authentication system, if only the victim's photo is acquired by the attacker. Again, the attack is totally unobservable by nearby people, because not only the light is invisible, but also the device we made to launch the attack is small enough. According to our study on a large dataset, attackers have a very high success rate with a over 70\% success rate for finding such an adversarial example that can be implemented by infrared. To the best of our knowledge, our work is the first one to shed light on the severity of threat resulted from infrared adversarial examples against face recognition.