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Adversarial patch camouflage against aerial detection [PDF]
Artificial Intelligence and Machine Learning in Defense Applications II, 2020 Detection of military assets on the ground can be performed by applying deep learning-based object detectors on drone surveillance footage. The traditional way of hiding military assets from sight is camouflage, for example by using camouflage nets. However, large assets like planes or vessels are difficult to conceal by means of traditional camouflage
Hollander, R.J.M. den +11 more
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Scale-Adaptive Adversarial Patch Attack for Remote Sensing Image Aircraft Detection
Remote Sensing, 2021 With the adversarial attack of convolutional neural networks (CNNs), we are able to generate adversarial patches to make an aircraft undetectable by object detectors instead of covering the aircraft with large camouflage nets. However, aircraft in remote
Mingming Lu, Qi Li, Li Chen, Haifeng Li
doaj +1 more source
Generative Adversarial Networks for Synthesizing InSAR Patches [PDF]
CoRR, 2020 accepted in preliminary version for EUSAR2020 ...
Sibler, Philipp +4 more
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Using Frequency Attention to Make Adversarial Patch Powerful Against Person Detector
IEEE Access, 2023 Deep neural networks (DNNs) are vulnerable to adversarial attacks. In particular, object detectors may be attacked by applying a particular adversarial patch to the image.
Xiaochun Lei +5 more
doaj +1 more source
CoRR, 2017 We present a method to create universal, robust, targeted adversarial image patches in the real world. The patches are universal because they can be used to attack any scene, robust because they work under a wide variety of transformations, and targeted because they can cause a classifier to output any target class.
Tom B. Brown +4 more
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ShuffleDetect: Detecting Adversarial Images against Convolutional Neural Networks
Applied Sciences, 2023 Recently, convolutional neural networks (CNNs) have become the main drivers in many image recognition applications. However, they are vulnerable to adversarial attacks, which can lead to disastrous consequences.
Raluca Chitic +2 more
doaj +1 more source
Brightness-Restricted Adversarial Attack Patch
CoRR, 2023 Adversarial attack patches have gained increasing attention due to their practical applicability in physical-world scenarios. However, the bright colors used in attack patches represent a significant drawback, as they can be easily identified by human observers.
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Eigenpatches—Adversarial Patches from Principal Components
, 2023 Adversarial patches are still a simple yet powerful white box attack that can be used to fool object detectors by suppressing possible detections. The patches of these so-called evasion attacks are computational expensive to produce and require full access to the attacked detector.
Jens Bayer +3 more
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Harnessing Perceptual Adversarial Patches for Crowd Counting
Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security, 2022 Crowd counting, which has been widely adopted for estimating the number of people in safety-critical scenes, is shown to be vulnerable to adversarial examples in the physical world (e.g., adversarial patches). Though harmful, adversarial examples are also valuable for evaluating and better understanding model robustness.
Shunchang Liu +6 more
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On Physical Adversarial Patches for Object Detection
CoRR, 2019 In this paper, we demonstrate a physical adversarial patch attack against object detectors, notably the YOLOv3 detector. Unlike previous work on physical object detection attacks, which required the patch to overlap with the objects being misclassified or avoiding detection, we show that a properly designed patch can suppress virtually all the detected
Mark Lee, J. Zico Kolter
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