Note on Attacking Object Detectors with Adversarial Stickers
Kevin Eykholt, Ivan Evtimov, Earlence Fernandes, Bo Li, Dawn Song,, Tadayoshi Kohno, Amir Rahmati, Atul Prakash, Florian Tramer

TL;DR
This paper demonstrates that physical adversarial stickers can effectively fool state-of-the-art object detectors like YOLO and Faster-RCNN, highlighting vulnerabilities in these systems through static and dynamic tests.
Contribution
It introduces an algorithm to generate physical adversarial stickers that can reliably deceive object detectors, extending adversarial attacks to physical objects.
Findings
Adversarial stickers can cause mislabeling or non-detection of objects.
The attack transfers effectively between different detectors.
Physical stickers are a practical attack vector against object detection systems.
Abstract
Deep learning has proven to be a powerful tool for computer vision and has seen widespread adoption for numerous tasks. However, deep learning algorithms are known to be vulnerable to adversarial examples. These adversarial inputs are created such that, when provided to a deep learning algorithm, they are very likely to be mislabeled. This can be problematic when deep learning is used to assist in safety critical decisions. Recent research has shown that classifiers can be attacked by physical adversarial examples under various physical conditions. Given the fact that state-of-the-art objection detection algorithms are harder to be fooled by the same set of adversarial examples, here we show that these detectors can also be attacked by physical adversarial examples. In this note, we briefly show both static and dynamic test results. We design an algorithm that produces physical…
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Taxonomy
TopicsAdversarial Robustness in Machine Learning · Integrated Circuits and Semiconductor Failure Analysis · Advanced Malware Detection Techniques
