Results 11 to 20 of about 85,609 (269)
Optical Adversarial Attack [PDF]
2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), 2021 ICCV Workshop ...
Abhiram Gnanasambandam +2 more
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Adversarial attacks and adversarial robustness in computational pathology. [PDF]
Nat Commun, 2022 AbstractArtificial Intelligence (AI) can support diagnostic workflows in oncology by aiding diagnosis and providing biomarkers directly from routine pathology slides. However, AI applications are vulnerable to adversarial attacks. Hence, it is essential to quantify and mitigate this risk before widespread clinical use.
Ghaffari Laleh N +10 more
europepmc +6 more sources
On the Reversibility of Adversarial Attacks
2021 IEEE International Conference on Image Processing (ICIP), 2021 Adversarial attacks modify images with perturbations that change the prediction of classifiers. These modified images, known as adversarial examples, expose the vulnerabilities of deep neural network classifiers. In this paper, we investigate the predictability of the mapping between the classes predicted for original images and for their corresponding
Chau Yi Li +4 more
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Adversarial Attacks on Adversarial Bandits
CoRR, 2023 Accepted by ICLR ...
Yuzhe Ma, Zhijin Zhou
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A Survey on Universal Adversarial Attack [PDF]
Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2021 The intriguing phenomenon of adversarial examples has attracted significant attention in machine learning and what might be more surprising to the community is the existence of universal adversarial perturbations (UAPs), i.e. a single perturbation to fool the target DNN for most images.
Chaoning Zhang +5 more
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Attacking Adversarial Attacks as A Defense
CoRR, 2021 It is well known that adversarial attacks can fool deep neural networks with imperceptible perturbations. Although adversarial training significantly improves model robustness, failure cases of defense still broadly exist. In this work, we find that the adversarial attacks can also be vulnerable to small perturbations.
Boxi Wu +8 more
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Adversarial Attack with Raindrops
CoRR, 2023 10 pages, 7 figures, This manuscript was submitted to CVPR ...
Jiyuan Liu 0005 +4 more
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Stochastic sparse adversarial attacks [PDF]
2021 IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI), 2021 This paper introduces stochastic sparse adversarial attacks (SSAA), standing as simple, fast and purely noise-based targeted and untargeted attacks of neural network classifiers (NNC). SSAA offer new examples of sparse (or $L_0$) attacks for which only few methods have been proposed previously.
Hajri, Hatem +4 more
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CoRR, 2022 ICML Workshop 2022 on Adversarial Machine Learning ...
Soichiro Kumano +2 more
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CoRR, 2022 Recent advances in machine learning show that neural models are vulnerable to minimally perturbed inputs, or adversarial examples. Adversarial algorithms are optimization problems that minimize the accuracy of ML models by perturbing inputs, often using a model's loss function to craft such perturbations.
Thomas Cilloni +2 more
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