General cutting planes for bound-propagation-based neural network verification
Bound propagation methods, when combined with branch and bound, are among the most
effective methods to formally verify properties of deep neural networks such as correctness …
effective methods to formally verify properties of deep neural networks such as correctness …
First three years of the international verification of neural networks competition (VNN-COMP)
This paper presents a summary and meta-analysis of the first three iterations of the annual
International Verification of Neural Networks Competition (VNN-COMP), held in 2020, 2021 …
International Verification of Neural Networks Competition (VNN-COMP), held in 2020, 2021 …
The second international verification of neural networks competition (vnn-comp 2021): Summary and results
This report summarizes the second International Verification of Neural Networks
Competition (VNN-COMP 2021), held as a part of the 4th Workshop on Formal Methods for …
Competition (VNN-COMP 2021), held as a part of the 4th Workshop on Formal Methods for …
Adversarial robustness of deep neural networks: A survey from a formal verification perspective
Neural networks have been widely applied in security applications such as spam and
phishing detection, intrusion prevention, and malware detection. This black-box method …
phishing detection, intrusion prevention, and malware detection. This black-box method …
Neuro-symbolic verification of deep neural networks
Formal verification has emerged as a powerful approach to ensure the safety and reliability
of deep neural networks. However, current verification tools are limited to only a handful of …
of deep neural networks. However, current verification tools are limited to only a handful of …
Formal verification of neural agents in non-deterministic environments
We introduce a model for agent-environment systems where the agents are implemented via
feed-forward ReLU neural networks and the environment is non-deterministic. We study the …
feed-forward ReLU neural networks and the environment is non-deterministic. We study the …
Tight Verification of Probabilistic Robustness in Bayesian Neural Networks
B Batten, M Hosseini… - … Conference on Artificial …, 2024 - proceedings.mlr.press
We introduce two algorithms for computing tight guarantees on the probabilistic robustness
of Bayesian Neural Networks (BNNs). Computing robustness guarantees for BNNs is a …
of Bayesian Neural Networks (BNNs). Computing robustness guarantees for BNNs is a …
Robust training of neural networks against bias field perturbations
P Henriksen, A Lomuscio - Proceedings of the AAAI Conference on …, 2023 - ojs.aaai.org
We introduce the problem of training neural networks such that they are robust against a
class of smooth intensity perturbations modelled by bias fields. We first develop an approach …
class of smooth intensity perturbations modelled by bias fields. We first develop an approach …
Formal verification for neural networks with general nonlinearities via branch-and-bound
Bound propagation with branch-and-bound (BaB) is so far among the most effective
methods for neural network (NN) verification. However, existing works with BaB have mostly …
methods for neural network (NN) verification. However, existing works with BaB have mostly …
Repairing misclassifications in neural networks using limited data
We present a novel and computationally efficient method for repairing a feed-forward neural
network with respect to a finite set of inputs that are misclassified. The method assumes no …
network with respect to a finite set of inputs that are misclassified. The method assumes no …