13 citations · 50 across the 13 of their papers we have counts for
6 papers · 1 filter
Latent SHAP: Toward Practical Human-Interpretable Explanations
Ron Bitton, Alon Malach, Amiel Meiseles +5
Model agnostic feature attribution algorithms (such as SHAP and LIME) are ubiquitous techniques for explaining the decisions of complex classification models, such as deep neural n…
Attacking Object Detector Using A Universal Targeted Label-Switch Patch
Avishag Shapira, Ron Bitton, Dan Avraham +3
Adversarial attacks against deep learning-based object detectors (ODs) have been studied extensively in the past few years. These attacks cause the model to make incorrect predicti…
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…
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…
GIM: Gaussian Isolation Machines
Guy Amit, Ishai Rosenberg, Moshe Levy +3
In many cases, neural network classifiers are likely to be exposed to input data that is outside of their training distribution data. Samples from outside the distribution may be c…
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…