Network transferability of adversarial patches in real-time object detection

• URL: http://arxiv.org/abs/2408.15833v1
• Date: Wed, 28 Aug 2024 14:47:34 GMT
• Title: Network transferability of adversarial patches in real-time object detection
• Authors: Jens Bayer, Stefan Becker, David Münch, Michael Arens,
• Abstract summary: Adversarial patches in computer vision can be used to fool deep neural networks and manipulate their decision-making process. This paper investigates the transferability across numerous object detector architectures.
• Score: 3.237380113935023
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Adversarial patches in computer vision can be used, to fool deep neural networks and manipulate their decision-making process. One of the most prominent examples of adversarial patches are evasion attacks for object detectors. By covering parts of objects of interest, these patches suppress the detections and thus make the target object 'invisible' to the object detector. Since these patches are usually optimized on a specific network with a specific train dataset, the transferability across multiple networks and datasets is not given. This paper addresses these issues and investigates the transferability across numerous object detector architectures. Our extensive evaluation across various models on two distinct datasets indicates that patches optimized with larger models provide better network transferability than patches that are optimized with smaller models.

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