With the applications of deep learning in safety-critical domains such as autonomous driving
systems gaining ground, it demands rigorous verification to guarantee the safety and …

There is a pressing need to verify robustness of classification deep neural networks
(CDNNs) as they are embedded in many safety-critical applications. Existing robustness …

Formal verification of neural networks is critical for their safe adoption in real-world
applications. However, designing a precise and scalable verifier which can handle different …

Deep neural networks (DNNs) have been applied in safety-critical domains such as self
driving cars, aircraft collision avoidance systems, malware detection, etc. In such scenarios …

Deep neural networks (DNNs) have been shown lack of robustness, as they are vulnerable
to small perturbations on the inputs. This has led to safety concerns on applying DNNs to …

The paper extends the recent star reachability method to verify the robustness of recurrent
neural networks (RNNs) for use in safety-critical applications. RNNs are a popular machine …

Deep neural networks have achieved impressive results in many complex applications,
including classification tasks for image and speech recognition, pattern analysis or …

Neural networks are increasingly deployed in real-world safety-critical domains such as
autonomous driving, aircraft collision avoidance, and malware detection. However, these …

The robustness of neural networks can be quantitatively indicated by a lower bound within
which any perturbation does not alter the original input's classification result. A certified …

Neural networks are increasingly applied to support decision making in safety-critical
applications (like autonomous cars, unmanned aerial vehicles and face recognition based …