Invisible CMOS Camera Dazzling for Conducting Adversarial Attacks on Deep Neural Networks
Year: 2025
Type: article
Abstract: Despite the outstanding performance of deep neural networks, they remain vulnerable to adversarial attacks. While digital domain adversarial attacks are well-documented, most physical-world attacks are typically visible to the human eye. Here, we present a novel invisible optical-based physical adversarial attack via dazzling a CMOS camera. This attack involves using a designed light pulse sequence spatially transformed within the acquired image due to the camera's shutter mechanism. We provide a detailed analysis of the photopic conditions required to keep the attacking light source invisible to human observers while effectively disrupting the image, thereby deceiving the DNN. The results indicate that the light source duty cycle controls the tradeoff between the attack's success rate and the degree of concealment needed.
Source: Sensors
DOI: 10.3390/s25072301
Authors: Zvi Stein, Adir Hazan, Adrian Stern
Institution: Ben-Gurion University of the Negev
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,137
OpenAlex ID: W4409210824