[HTML][HTML] Exploring misclassifications of robust neural networks to enhance adversarial attacks
Progress in making neural networks more robust against adversarial attacks is mostly
marginal, despite the great efforts of the research community. Moreover, the robustness …
marginal, despite the great efforts of the research community. Moreover, the robustness …
One less reason for filter pruning: Gaining free adversarial robustness with structured grouped kernel pruning
Densely structured pruning methods utilizing simple pruning heuristics can deliver
immediate compression and acceleration benefits with acceptable benign performances …
immediate compression and acceleration benefits with acceptable benign performances …
From Attack to Defense: Insights into Deep Learning Security Measures in Black-Box Settings
Deep Learning (DL) is rapidly maturing to the point that it can be used in safety-and security-
crucial applications. However, adversarial samples, which are undetectable to the human …
crucial applications. However, adversarial samples, which are undetectable to the human …
Detection, Quantification, and Mitigation of Robustness Vulnerabilities in Deep Neural Networks
L Schwinn - 2023 - search.proquest.com
Abstract Machine learning (ML) has made enormous progress in the last two decades.
Specifically, Deep Neural Networks (DNNs) have led to several breakthroughs. The …
Specifically, Deep Neural Networks (DNNs) have led to several breakthroughs. The …