Results 11 to 20 of about 16,308 (300)
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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Optical Adversarial Attack [PDF]
2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), 2021 ICCV Workshop ...
Gnanasambandam, Abhiram +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
Distributionally Adversarial Attack
Proceedings of the AAAI Conference on Artificial Intelligence, 2019 Recent work on adversarial attack has shown that Projected Gradient Descent (PGD) Adversary is a universal first-order adversary, and the classifier adversarially trained by PGD is robust against a wide range of first-order attacks. It is worth noting that the original objective of an attack/defense model relies on a data distribution p(x), typically ...
Tianhang Zheng, Changyou Chen, Kui Ren
openalex +4 more sources
Adversarial Attacks on Time Series [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021 Time series classification models have been garnering significant importance in the research community. However, not much research has been done on generating adversarial samples for these models. These adversarial samples can become a security concern. In this paper, we propose utilizing an adversarial transformation network (ATN) on a distilled model
Fazle Karim +2 more
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Discriminator-free Generative Adversarial Attack [PDF]
Proceedings of the 29th ACM International Conference on Multimedia, 2021 9 pages, 6 figures, 4 ...
Lu, Shaohao +7 more
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Probabilistic Categorical Adversarial Attack & Adversarial Training
, 2022 The existence of adversarial examples brings huge concern for people to apply Deep Neural Networks (DNNs) in safety-critical tasks. However, how to generate adversarial examples with categorical data is an important problem but lack of extensive exploration.
Xu, Han +6 more
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Adversarial Attacks on Adversarial Bandits
, 2023 Accepted by ICLR ...
Ma, Yuzhe, Zhou, Zhijin
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Proceedings of the AAAI Conference on Artificial Intelligence, 2021 Adversarial attack is a technique for deceiving Machine Learning (ML) models, which provides a way to evaluate the adversarial robustness. In practice, attack algorithms are artificially selected and tuned by human experts to break a ML system. However, manual selection of attackers tends to be sub-optimal, leading to a mistakenly assessment of model ...
Mao, Xiaofeng +5 more
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, 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.
Cilloni, Thomas +2 more
openaire +2 more sources