119 citations · 468 across the 39 of their papers we have counts for
21 papers · 1 filter
Verifying Generalization in Deep Learning
Guy Amir, Osher Maayan, Tom Zelazny +2
Deep neural networks (DNNs) are the workhorses of deep learning, which constitutes the state of the art in numerous application domains. However, DNN-based decision rules are notor…
OccRob: Efficient SMT-Based Occlusion Robustness Verification of Deep Neural Networks
Xingwu Guo, Ziwei Zhou, Yueling Zhang +2
Occlusion is a prevalent and easily realizable semantic perturbation to deep neural networks (DNNs). It can fool a DNN into misclassifying an input image by occluding some segments…
gRoMA: a Tool for Measuring the Global Robustness of Deep Neural Networks
Natan Levy, Raz Yerushalmi, Guy Katz
Deep neural networks (DNNs) are at the forefront of cutting-edge technology, and have been achieving remarkable performance in a variety of complex tasks. Nevertheless, their integ…
Efficiently Finding Adversarial Examples with DNN Preprocessing
Avriti Chauhan, Mohammad Afzal, Hrishikesh Karmarkar +3
Deep Neural Networks (DNNs) are everywhere, frequently performing a fairly complex task that used to be unimaginable for machines to carry out. In doing so, they do a lot of decisi…
Taming Reachability Analysis of DNN-Controlled Systems via Abstraction-Based Training
Jiaxu Tian, Dapeng Zhi, Si Liu +3
The intrinsic complexity of deep neural networks (DNNs) makes it challenging to verify not only the networks themselves but also the hosting DNN-controlled systems. Reachability an…
Towards Formal XAI: Formally Approximate Minimal Explanations of Neural Networks
Shahaf Bassan, Guy Katz
With the rapid growth of machine learning, deep neural networks (DNNs) are now being used in numerous domains. Unfortunately, DNNs are "black-boxes", and cannot be interpreted by h…
Tighter Abstract Queries in Neural Network Verification
Elazar Cohen, Yizhak Yisrael Elboher, Clark Barrett +1
Neural networks have become critical components of reactive systems in various domains within computer science. Despite their excellent performance, using neural networks entails n…
On Optimizing Back-Substitution Methods for Neural Network Verification
Tom Zelazny, Haoze Wu, Clark Barrett +1
With the increasing application of deep learning in mission-critical systems, there is a growing need to obtain formal guarantees about the behaviors of neural networks. Indeed, ma…
Efficient Neural Network Analysis with Sum-of-Infeasibilities
Haoze Wu, Aleksandar Zeljić, Guy Katz +1
Inspired by sum-of-infeasibilities methods in convex optimization, we propose a novel procedure for analyzing verification queries on neural networks with piecewise-linear activati…
Scenario-Assisted Deep Reinforcement Learning
Raz Yerushalmi, Guy Amir, Achiya Elyasaf +3
Deep reinforcement learning has proven remarkably useful in training agents from unstructured data. However, the opacity of the produced agents makes it difficult to ensure that th…
Verification-Aided Deep Ensemble Selection
Guy Amir, Tom Zelazny, Guy Katz +1
Deep neural networks (DNNs) have become the technology of choice for realizing a variety of complex tasks. However, as highlighted by many recent studies, even an imperceptible per…
An Abstraction-Refinement Approach to Verifying Convolutional Neural Networks
Matan Ostrovsky, Clark Barrett, Guy Katz
Convolutional neural networks have gained vast popularity due to their excellent performance in the fields of computer vision, image processing, and others. Unfortunately, it is no…
Minimal Multi-Layer Modifications of Deep Neural Networks
Idan Refaeli, Guy Katz
Deep neural networks (DNNs) have become increasingly popular in recent years. However, despite their many successes, DNNs may also err and produce incorrect and potentially fatal o…
RoMA: a Method for Neural Network Robustness Measurement and Assessment
Natan Levy, Guy Katz
Neural network models have become the leading solution for a large variety of tasks, such as classification, language processing, protein folding, and others. However, their reliab…
Pruning and Slicing Neural Networks using Formal Verification
Ori Lahav, Guy Katz
Deep neural networks (DNNs) play an increasingly important role in various computer systems. In order to create these networks, engineers typically specify a desired topology, and…
Towards Scalable Verification of Deep Reinforcement Learning
Guy Amir, Michael Schapira, Guy Katz
Deep neural networks (DNNs) have gained significant popularity in recent years, becoming the state of the art in a variety of domains. In particular, deep reinforcement learning (D…
Neural Network Robustness as a Verification Property: A Principled Case Study
Marco Casadio, Ekaterina Komendantskaya, Matthew L. Daggitt +4
Neural networks are very successful at detecting patterns in noisy data, and have become the technology of choice in many fields. However, their usefulness is hampered by their sus…
An SMT-Based Approach for Verifying Binarized Neural Networks
Guy Amir, Haoze Wu, Clark Barrett +1
Deep learning has emerged as an effective approach for creating modern software systems, with neural networks often surpassing hand-crafted systems. Unfortunately, neural networks…
Global Optimization of Objective Functions Represented by ReLU Networks
Christopher A. Strong, Haoze Wu, Aleksandar Zeljić +4
Neural networks can learn complex, non-convex functions, and it is challenging to guarantee their correct behavior in safety-critical contexts. Many approaches exist to find failur…
Towards Proving the Adversarial Robustness of Deep Neural Networks
Guy Katz, Clark Barrett, David L. Dill +2
Autonomous vehicles are highly complex systems, required to function reliably in a wide variety of situations. Manually crafting software controllers for these vehicles is difficul…
Provably Minimally-Distorted Adversarial Examples
Nicholas Carlini, Guy Katz, Clark Barrett +1
The ability to deploy neural networks in real-world, safety-critical systems is severely limited by the presence of adversarial examples: slightly perturbed inputs that are misclas…