20 citations · 23 across the 3 of their papers we have counts for
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cs.LG2022★ 2 cited
Improving Interpretability via Regularization of Neural Activation Sensitivity
Ofir Moshe, Gil Fidel, Ron Bitton +1
State-of-the-art deep neural networks (DNNs) are highly effective at tackling many real-world tasks. However, their wide adoption in mission-critical contexts is hampered by two ma…
cs.LG2020★ 1 cited
Adversarial robustness via stochastic regularization of neural activation sensitivity
Gil Fidel, Ron Bitton, Ziv Katzir +1
Recent works have shown that the input domain of any machine learning classifier is bound to contain adversarial examples. Thus we can no longer hope to immune classifiers against…
cs.LG2019★ 20 cited
When Explainability Meets Adversarial Learning: Detecting Adversarial Examples using SHAP Signatures
Gil Fidel, Ron Bitton, Asaf Shabtai
State-of-the-art deep neural networks (DNNs) are highly effective in solving many complex real-world problems. However, these models are vulnerable to adversarial perturbation atta…