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arXiv 2202.088922022-05-11cs.CV

Developing Imperceptible Adversarial Patches to Camouflage Military Assets From Computer Vision Enabled Technologies

University of New South Wales（新南威尔士大学）

Chris Wise, Jo Plested

备注：: 8 pages, 4 figures, 4 tables, submitted to WCCI 2022

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Convolutional neural networks (CNNs) have demonstrated rapid progress and a high level of success in object detection. However, recent evidence has highlighted their vulnerability to adversarial attacks. These attacks are calculated image perturbations or adversarial patches that result in object misclassification or detection suppression. Traditional camouflage methods are impractical when applied to disguise aircraft and other large mobile assets from autonomous detection in intelligence, surveillance and reconnaissance technologies and fifth generation missiles. In this paper we present a unique method that produces imperceptible patches capable of camouflaging large military assets from computer vision-enabled technologies. We developed these patches by maximising object detection loss whilst limiting the patch's colour perceptibility. This work also aims to further the understanding of adversarial examples and their effects on object detection algorithms.

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