V-Phanton:Voltage-Based Physically-Triggered Backdoor Attack Against Facial Recognition

Physical backdoor attacks are under increasing scrutiny, yet current methods often necessitate directly applying adversarial perturbations to target objects, like the attacker’s face. These approaches often pose practical challenges and compromise concealment. In this paper, we propose a stealthy, physically-triggered backdoor attack, V-Phanton,enabling attackers to engage in face spoofing and bypass facial recognition without the need for physical alterations to the attacker or model modifications. Specifically, V-Phanton manipulates the power supply voltage of the webcam to introduce adversarial perturbations into the captured image, which undermines the recognition process. Our experiments across three facial recognition models (ArcFace-50, MagFace-18/50) and one commercial facial recognition system (Face++) illustrate that V-Phanton achieves attack and victim success rates of up to 100% and 100% in simulations, and 100% and 99.93% in real-world experiments.

Authors

Institutions

Publication Details

Published
2025-03-12
DOI
https://doi.org/10.1109/icassp49660.2025.10888148
Primary Topic
Adversarial Robustness in Machine Learning
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

V-Phanton:Voltage-Based Physically-Triggered Backdoor Attack Against Facial Recognition

Xiaoyu Ji, Wenyuan Xu, Yushi Cheng, Yan Jiang et al.
Adversarial Robustness in Machine Learning
article

V-Phanton:Voltage-Based Physically-Triggered Backdoor Attack Against Facial Recognition

Xiaoyu Ji, Wenyuan Xu, Yushi Cheng, Yan Jiang, Ruishan Li
article en

Abstract

Physical backdoor attacks are under increasing scrutiny, yet current methods often necessitate directly applying adversarial perturbations to target objects, like the attacker’s face. These approaches often pose practical challenges and compromise concealment. In this paper, we propose a stealthy, physically-triggered backdoor attack, V-Phanton,enabling attackers to engage in face spoofing and bypass facial recognition without the need for physical alterations to the attacker or model modifications. Specifically, V-Phanton manipulates the power supply voltage of the webcam to introduce adversarial perturbations into the captured image, which undermines the recognition process. Our experiments across three facial recognition models (ArcFace-50, MagFace-18/50) and one commercial facial recognition system (Face++) illustrate that V-Phanton achieves attack and victim success rates of up to 100% and 100% in simulations, and 100% and 99.93% in real-world experiments.

Zhejiang University (CN)
Gender equality
Openalex Percentile: Top 98%
Adversarial Robustness in Machine Learning
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.