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Deep learning techniques have achieved superior performance in computer-aided medical image analysis, yet they are still vulnerable to imperceptible adversarial attacks, resulting in potential misdiagnosis in clinical practice.
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Jianing Zhu, Jiangchao Yao, Bo Han, Jingfeng Zhang, Tongliang Liu, Gang Niu, Jingren Zhou, Jianliang Xu, and Hongxia Yang. 2022 · 2022
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Query efficient black-box adversarial attack on deep neural networks
Yang Bai, Yisen Wang, Yuyuan Zeng, Yong Jiang, and Shu-Tao Xia. 2023 · 2023
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Frequency constraint-based adversarial attack on deep neural networks for medical image classification
Fang Chen, Jian Wang, Han Liu, Wentao Kong, Zhe Zhao, Longfei Ma, Hongen Liao, and Daoqiang Zhang. 2023 · 2023
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Improving adversarial robustness of medical imaging systems via adding global attention noise
Yinyao Dai, Yaguan Qian, Fang Lu, Bin Wang, Zhaoquan Gu, Wei Wang, Jian Wan, and Yanchun Zhang. 2023 · 2023
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ViTH-RFG: Vision Transformer Hashing with Residual Fuzzy Generation for Targeted Attack in Medical Image Retrieval
Weiping Ding, Chuansheng Liu, Jiashuang Huang, Chun Cheng, and Hengrong Ju. 2023 · 2023
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Restricted Black-Box Adversarial Attack Against DeepFake Face Swapping
Junhao Dong, Yuan Wang, Jianhuang Lai, and Xiaohua Xie. 2023b · 2023
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Robust evaluation of diffusion-based adversarial purification. In Proceedings of the IEEE/CVF International Conference on Computer Vision . 134–144
Minjong Lee and Dongwoo Kim. 2023 · 2023
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The threat of adversarial attack on a COVID-19 CT image-based deep learning system
Yang Li and Shaoying Liu. 2023 · 2023
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Understanding Zero-shot Adversarial Robustness for Large-Scale Models. In The Eleventh International Conference on Learning Representations,ICLR
Chengzhi Mao, Scott Geng, Junfeng Yang, Xin Wang, and Carl Vondrick. 2023 · 2023
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A critical revisit of adversarial robustness in 3D point cloud recognition with diffusion-driven purification. In ICML . 33100–33114
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Adversarial Attacks on Medical Image Classification
Min-Jen Tsai, Ping-Yi Lin, and Ming-En Lee. 2023 · 2023
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Reversing skin cancer adversarial examples by multiscale diffusive and denoising aggregation mechanism
Yongwei Wang, Yuan Li, Zhiqi Shen, and Yuhui Qiao. 2023a · 2023
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Toward robust diagnosis: A contour attention preserving adversarial defense for covid-19 detection. In AAAI
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Adversarial Medical Image with Hierarchical Feature Hiding
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RAE-VWP: A Reversible Adversarial Example-Based Privacy and Copyright Protection Method of Medical Images for Internet of Medical Things
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Content-based unrestricted adversarial attack
Zhaoyu Chen, Bo Li, Shuang Wu, Kaixun Jiang, Shouhong Ding, and Wenqiang Zhang. 2024b · 2024
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Protecting Prostate Cancer Classification from Rectal Artifacts via Targeted Adversarial Training
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Adversarial Attacks on Medical Segmentation Model via Transformation of Feature Statistics
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Dynamic Perturbation-Adaptive Adversarial Training on Medical Image Classification
Shuai Li, Xiaoguang Ma, Shancheng Jiang, and Lu Meng. 2024 · 2024
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Christian Schlarmann, Naman Deep Singh, Francesco Croce, and Matthias Hein. 2024 · 2024
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Pre-trained Model Guided Fine-Tuning for Zero-Shot Adversarial Robustness. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition
Sibo Wang, Jie Zhang, Zheng Yuan, and Shiguang Shan. 2024 · 2024
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Robust Medical Diagnosis: A Novel Two-Phase Deep Learning Framework for Adversarial Proof Disease Detection in Radiology Images
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Weakly-supervised localization of diabetic retinopathy lesions in retinal fundus images. In IEEE ICIP . 2069–2073
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Xiaosong Wang, Yifan Peng, Le Lu, Zhiyong Lu, Mohammadhadi Bagheri, and Ronald M Summers. 2017 · 2097
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