Input-relational verification of deep neural networks
D Banerjee, C Xu, G Singh - Proceedings of the ACM on Programming …, 2024 - dl.acm.org
We consider the verification of input-relational properties defined over deep neural networks
(DNNs) such as robustness against universal adversarial perturbations, monotonicity, etc …
(DNNs) such as robustness against universal adversarial perturbations, monotonicity, etc …
Incremental verification of neural networks
Complete verification of deep neural networks (DNNs) can exactly determine whether the
DNN satisfies a desired trustworthy property (eg, robustness, fairness) on an infinite set of …
DNN satisfies a desired trustworthy property (eg, robustness, fairness) on an infinite set of …
Deep learning robustness verification for few-pixel attacks
Y Shapira, E Avneri, D Drachsler-Cohen - Proceedings of the ACM on …, 2023 - dl.acm.org
While successful, neural networks have been shown to be vulnerable to adversarial
example attacks. In L 0 adversarial attacks, also known as few-pixel attacks, the attacker …
example attacks. In L 0 adversarial attacks, also known as few-pixel attacks, the attacker …
Boosting few-pixel robustness verification via covering verification designs
Y Shapira, N Wiesel, S Shabelman… - … on Computer Aided …, 2024 - Springer
Proving local robustness is crucial to increase the reliability of neural networks. While many
verifiers prove robustness in L∞ ϵ-balls, very little work deals with robustness verification in …
verifiers prove robustness in L∞ ϵ-balls, very little work deals with robustness verification in …
Verification of Neural Networks' Global Robustness
A Kabaha, DD Cohen - Proceedings of the ACM on Programming …, 2024 - dl.acm.org
Neural networks are successful in various applications but are also susceptible to
adversarial attacks. To show the safety of network classifiers, many verifiers have been …
adversarial attacks. To show the safety of network classifiers, many verifiers have been …
FAST: Feature arrangement for semantic transmission
Although existing semantic communication systems have achieved great success, they have
not considered that the channel is time-varying wherein deep fading occurs occasionally …
not considered that the channel is time-varying wherein deep fading occurs occasionally …
Robustness Verification of Multi-label Neural Network Classifiers
J Mour, D Drachsler-Cohen - International Static Analysis Symposium, 2024 - Springer
Multi-label neural networks are important in various tasks, including safety-critical tasks.
Several works show that these networks are susceptible to adversarial attacks, which can …
Several works show that these networks are susceptible to adversarial attacks, which can …
Building Trust and Safety in Artificial Intelligence with Abstract Interpretation
G Singh - International Static Analysis Symposium, 2023 - Springer
Building Trust and Safety in Artificial Intelligence with Abstract Interpretation | SpringerLink
Skip to main content Advertisement SpringerLink Account Menu Find a journal Publish with us …
Skip to main content Advertisement SpringerLink Account Menu Find a journal Publish with us …
Computer Aided Verification: 36th International Conference, CAV 2024, Montreal, QC, Canada, July 24–27, 2024, Proceedings, Part III
A Gurfinkel, V Ganesh - 2024 - library.oapen.org
This open access book constitutes the proceedings of the 36th International Conference on
Computer-Aided Verification, CAV 2024, which took place in Montreal, Canada, during July …
Computer-Aided Verification, CAV 2024, which took place in Montreal, Canada, during July …
Computer Aided Verification
A Gurfinkel, V Ganesh - Springer
It was our privilege to serve as the program chairs for CAV 2024, the 36th International
Conference on Computer-Aided Verification. CAV 2024 was held in Montreal, Canada, on …
Conference on Computer-Aided Verification. CAV 2024 was held in Montreal, Canada, on …