Unified Adversarial Patch for Cross-modal Attacks in the Physical World
Xingxing Wei, Yao Huang, Yitong Sun, Jie Yu

TL;DR
This paper introduces a unified adversarial patch capable of simultaneously fooling both visible and infrared object detectors in physical environments, highlighting potential security risks in multi-modal sensor deployments.
Contribution
The work presents a novel shape optimization and score-aware evaluation method to create a single patch effective across different sensor modalities, a significant advancement over single-modal attacks.
Findings
Achieved over 70% attack success rate on YOLOv3 and Faster R-CNN.
Demonstrated physical-world effectiveness under various angles, distances, and scenes.
Proved the feasibility of cross-modal adversarial attacks in real-world scenarios.
Abstract
Recently, physical adversarial attacks have been presented to evade DNNs-based object detectors. To ensure the security, many scenarios are simultaneously deployed with visible sensors and infrared sensors, leading to the failures of these single-modal physical attacks. To show the potential risks under such scenes, we propose a unified adversarial patch to perform cross-modal physical attacks, i.e., fooling visible and infrared object detectors at the same time via a single patch. Considering different imaging mechanisms of visible and infrared sensors, our work focuses on modeling the shapes of adversarial patches, which can be captured in different modalities when they change. To this end, we design a novel boundary-limited shape optimization to achieve the compact and smooth shapes, and thus they can be easily implemented in the physical world. In addition, to balance the fooling…
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Taxonomy
TopicsAdversarial Robustness in Machine Learning · Bacillus and Francisella bacterial research · Anomaly Detection Techniques and Applications
MethodsBNB Customer Service Number +1-833-534-1729 · Average Pooling · Convolution · Batch Normalization · Global Average Pooling · 1x1 Convolution · Residual Connection · Softmax · k-Means Clustering · Logistic Regression
