128 citations · 131 across the 8 of their papers we have counts for
8 papers
Tight Robustness Certification Through the Convex Hull of Attacks
Yuval Shapira, Dana Drachsler-Cohen
Few-pixel attacks mislead a classifier by modifying a few pixels of an image. Their perturbation space is an -ball, which is not convex, unlike -balls for .…
Mini-Batch Robustness Verification of Deep Neural Networks
Saar Tzour-Shaday, Dana Drachsler-Cohen
Neural network image classifiers are ubiquitous in many safety-critical applications. However, they are susceptible to adversarial attacks. To understand their robustness to attack…
Boosting Few-Pixel Robustness Verification via Covering Verification Designs
Yuval Shapira, Naor Wiesel, Shahar Shabelman +1
Proving local robustness is crucial to increase the reliability of neural networks. While many verifiers prove robustness in -balls, very little work deals with robus…
Verification of Neural Networks Local Differential Classification Privacy
Roie Reshef, Anan Kabaha, Olga Seleznova +1
Neural networks are susceptible to privacy attacks. To date, no verifier can reason about the privacy of individuals participating in the training set. We propose a new privacy pro…
Boosting Robustness Verification of Semantic Feature Neighborhoods
Anan Kabaha, Dana Drachsler-Cohen
Deep neural networks have been shown to be vulnerable to adversarial attacks that perturb inputs based on semantic features. Existing robustness analyzers can reason about semantic…
Securify: Practical Security Analysis of Smart Contracts
Petar Tsankov, Andrei Dan, Dana Drachsler Cohen +3
Permissionless blockchains allow the execution of arbitrary programs (called smart contracts), enabling mutually untrusted entities to interact without relying on trusted third par…
Learning Disjunctions of Predicates
Nader H. Bshouty, Dana Drachsler-Cohen, Martin Vechev +1
Let be a set of boolean functions. We present an algorithm for learning from membership queries. Our algorithm asks at most $…
Optimal Learning of Specifications from Examples
Dana Drachsler-Cohen, Martin Vechev, Eran Yahav
A fundamental challenge in synthesis from examples is designing a learning algorithm that poses the minimal number of questions to an end user while guaranteeing that the target hy…