Tag: attacks
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Admissibility of Stein Shrinkage for Batch Normalization in the Presence of Adversarial Attacks
Admissibility of Stein Shrinkage for Batch Normalization in the Presence of Adversarial Attacks arXiv:2507.08261v1 Announce Type: new Abstract: Batch normalization (BN) is a ubiquitous operation in deep neural networks used primarily to achieve stability and regularization during network training. BN involves feature map centering and scaling using sample means and variances, respectively. Since these statistics…
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On the existence of consistent adversarial attacks in high-dimensional linear classification
On the existence of consistent adversarial attacks in high-dimensional linear classification arXiv:2506.12454v1 Announce Type: new Abstract: What fundamentally distinguishes an adversarial attack from a misclassification due to limited model expressivity or finite data? In this work, we investigate this question in the setting of high-dimensional binary classification, where statistical effects due to limited data availability…
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Evasion Attacks Against Bayesian Predictive Models
Evasion Attacks Against Bayesian Predictive Models arXiv:2506.09640v1 Announce Type: new Abstract: There is an increasing interest in analyzing the behavior of machine learning systems against adversarial attacks. However, most of the research in adversarial machine learning has focused on studying weaknesses against evasion or poisoning attacks to predictive models in classical setups, with the susceptibility…
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Approaching the Harm of Gradient Attacks While Only Flipping Labels
Approaching the Harm of Gradient Attacks While Only Flipping Labels arXiv:2503.00140v1 Announce Type: new Abstract: Availability attacks are one of the strongest forms of training-phase attacks in machine learning, making the model unusable. While prior work in distributed ML has demonstrated such effect via gradient attacks and, more recently, data poisoning, we ask: can similar…