Patch of Invisibility: Naturalistic Physical Black-Box Adversarial
Attacks on Object Detectors
- URL: http://arxiv.org/abs/2303.04238v4
- Date: Tue, 17 Oct 2023 09:16:06 GMT
- Title: Patch of Invisibility: Naturalistic Physical Black-Box Adversarial
Attacks on Object Detectors
- Authors: Raz Lapid, Eylon Mizrahi and Moshe Sipper
- Abstract summary: We propose a direct, black-box, gradient-free method to generate physical adversarial patches for object detectors.
To our knowledge this is the first and only method that performs black-box physical attacks directly on object-detection models.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Adversarial attacks on deep-learning models have been receiving increased
attention in recent years. Work in this area has mostly focused on
gradient-based techniques, so-called ``white-box'' attacks, wherein the
attacker has access to the targeted model's internal parameters; such an
assumption is usually unrealistic in the real world. Some attacks additionally
use the entire pixel space to fool a given model, which is neither practical
nor physical (i.e., real-world). On the contrary, we propose herein a direct,
black-box, gradient-free method that uses the learned image manifold of a
pretrained generative adversarial network (GAN) to generate naturalistic
physical adversarial patches for object detectors. To our knowledge this is the
first and only method that performs black-box physical attacks directly on
object-detection models, which results with a model-agnostic attack. We show
that our proposed method works both digitally and physically. We compared our
approach against four different black-box attacks with different
configurations. Our approach outperformed all other approaches that were tested
in our experiments by a large margin.
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