Algorithms for verifying deep neural networks
Deep neural networks are widely used for nonlinear function approximation, with
applications ranging from computer vision to control. Although these networks involve the …
applications ranging from computer vision to control. Although these networks involve the …
Adversarial attacks and defenses in deep learning: From a perspective of cybersecurity
The outstanding performance of deep neural networks has promoted deep learning
applications in a broad set of domains. However, the potential risks caused by adversarial …
applications in a broad set of domains. However, the potential risks caused by adversarial …
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 …
Blockchain's coming to hospital to digitalize healthcare services: Designing a distributed electronic health record ecosystem
The technological revolution in blockchain achieved the healthcare sector and offered a
significant opportunity to lead this digital transformation. A significant problem is that various …
significant opportunity to lead this digital transformation. A significant problem is that various …
Beta-crown: Efficient bound propagation with per-neuron split constraints for neural network robustness verification
Bound propagation based incomplete neural network verifiers such as CROWN are very
efficient and can significantly accelerate branch-and-bound (BaB) based complete …
efficient and can significantly accelerate branch-and-bound (BaB) based complete …
Are formal methods applicable to machine learning and artificial intelligence?
Formal approaches can provide strict correctness guarantees for the development of both
hardware and software systems. In this work, we examine state-of-the-art formal methods for …
hardware and software systems. In this work, we examine state-of-the-art formal methods for …
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 …
Sok: Certified robustness for deep neural networks
Great advances in deep neural networks (DNNs) have led to state-of-the-art performance on
a wide range of tasks. However, recent studies have shown that DNNs are vulnerable to …
a wide range of tasks. However, recent studies have shown that DNNs are vulnerable to …
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 …
Beyond the single neuron convex barrier for neural network certification
We propose a new parametric framework, called k-ReLU, for computing precise and
scalable convex relaxations used to certify neural networks. The key idea is to approximate …
scalable convex relaxations used to certify neural networks. The key idea is to approximate …