On Brightness Agnostic Adversarial Examples Against Face Recognition Systems
This work addresses security vulnerabilities in face recognition systems for applications like surveillance and authentication, though it is incremental as it builds on existing adversarial attack methods.
The paper tackles the problem of generating adversarial examples that remain effective under varying brightness conditions against face recognition systems, demonstrating that their method outperforms conventional techniques in both digital and physical world experiments.
This paper introduces a novel adversarial example generation method against face recognition systems (FRSs). An adversarial example (AX) is an image with deliberately crafted noise to cause incorrect predictions by a target system. The AXs generated from our method remain robust under real-world brightness changes. Our method performs non-linear brightness transformations while leveraging the concept of curriculum learning during the attack generation procedure. We demonstrate that our method outperforms conventional techniques from comprehensive experimental investigations in the digital and physical world. Furthermore, this method enables practical risk assessment of FRSs against brightness agnostic AXs.