DBA: downsampling-based adversarial attack in medical image analysis

Zhaoxuan Wang; Shiyu Zhang; Yang Li; Quan Pan

doi:10.1117/12.2684368

1 August 2023 DBA: downsampling-based adversarial attack in medical image analysis

Zhaoxuan Wang, Shiyu Zhang, Yang Li, Quan Pan

Proceedings Volume 12754, Third International Conference on Computer Vision and Pattern Analysis (ICCPA 2023); 127540Y (2023) https://doi.org/10.1117/12.2684368
Event: 2023 3rd International Conference on Computer Vision and Pattern Analysis (ICCPA 2023), 2023, Hangzhou, China

Abstract

The next-generation of artificial intelligence technology has contributed significantly to the development of medical intelligence. However, the widespread use of deep neural networks (DNNs) has also brought about serious security threats. In this paper, we present an adversarial attack approach for deep learning-based image segmentation models in the field of medical image analysis. In our solutions, we propose a novel adversarial attack method, which is designed to exploit the DNNs’ generic down-sampling operation to ensure the effectiveness, stealthiness, and transferability of the attack. We perform the attack on two State-Of-The-Art (SOTA) models, DDANet and CaraNet in a general medical image dataset Kvasir-SEG, and a comprehensive evaluation shows that our attack is effective stealthy, and transferrable.

Citation Download Citation

Zhaoxuan Wang, Shiyu Zhang, Yang Li, and Quan Pan "DBA: downsampling-based adversarial attack in medical image analysis", Proc. SPIE 12754, Third International Conference on Computer Vision and Pattern Analysis (ICCPA 2023), 127540Y (1 August 2023); https://doi.org/10.1117/12.2684368

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KEYWORDS

Data modeling

Image segmentation

Medical imaging

Statistical modeling

Education and training

Process modeling

Polyps

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