Physics-Based Adversarial Attack on Near-Infrared Human Detector for Nighttime Surveillance Camera Systems

TL;DR

This paper reveals vulnerabilities in NIR-based human detection for nighttime surveillance, demonstrating how physical adversarial patterns can deceive systems by manipulating NIR images, raising security concerns.

Contribution

It introduces the first physical adversarial attack on NIR human detectors, exploiting spectral sensitivity and reflectance properties to deceive AI systems in real-world scenarios.

Findings

NIR-based AI is vulnerable to physical adversarial patterns.

Retro-reflective tapes can manipulate NIR image intensity.

Attacks successfully deceive YOLO-based human detectors.

Abstract

Many surveillance cameras switch between daytime and nighttime modes based on illuminance levels. During the day, the camera records ordinary RGB images through an enabled IR-cut filter. At night, the filter is disabled to capture near-infrared (NIR) light emitted from NIR LEDs typically mounted around the lens. While RGB-based AI algorithm vulnerabilities have been widely reported, the vulnerabilities of NIR-based AI have rarely been investigated. In this paper, we identify fundamental vulnerabilities in NIR-based image understanding caused by color and texture loss due to the intrinsic characteristics of clothes' reflectance and cameras' spectral sensitivity in the NIR range. We further show that the nearly co-located configuration of illuminants and cameras in existing surveillance systems facilitates concealing and fully passive attacks in the physical world. Specifically, we demonstrate how retro-reflective and insulation plastic tapes can manipulate the intensity distribution of NIR images. We showcase an attack on the YOLO-based human detector using binary patterns designed in the digital space (via black-box query and searching) and then physically realized using tapes pasted onto clothes. Our attack highlights significant reliability concerns for nighttime surveillance systems, which are intended to enhance security. Codes Available: https://github.com/MyNiuuu/AdvNIR