From Pixels to Reality: Physical-Digital Patch Attacks on Real-World Camera

TL;DR

This paper introduces DiPA, a digital-only physical adversarial patch attack on camera-based authentication, demonstrating real-time dodging attacks and highlighting vulnerabilities in mobile and vision systems.

Contribution

It presents a novel digital-physical attack method that improves transferability and effectiveness against real-world face-recognition systems.

Findings

DiPA achieves higher success rates than existing physical attacks.

The attack reduces detection confidence and increases feature-space distortion.

Real-time demo shows effective dodging of deployed face-recognition cameras.

Abstract

This demonstration presents Digital-Physical Adversarial Attacks (DiPA), a new class of practical adversarial attacks against pervasive camera-based authentication systems, where an attacker displays an adversarial patch directly on a smartphone screen instead of relying on printed artifacts. This digital-only physical presentation enables rapid deployment, removes the need for total-variation regularization, and improves patch transferability in black-box conditions. DiPA leverages an ensemble of state-of-the-art face-recognition models (ArcFace, MagFace, CosFace) to enhance transfer across unseen commercial systems. Our interactive demo shows a real-time dodging attack against a deployed face-recognition camera, preventing authorized users from being recognized while participants dynamically adjust patch patterns and observe immediate effects on the sensing pipeline. We further demonstrate DiPA's superiority over existing physical attacks in terms of success rate, feature-space distortion, and reductions in detection confidence, highlighting critical vulnerabilities at the intersection of mobile devices, pervasive vision, and sensor-driven authentication infrastructures.