no code implementations • 6 Feb 2023 • Corina S. Pasareanu, Ravi Mangal, Divya Gopinath, Sinem Getir Yaman, Calum Imrie, Radu Calinescu, Huafeng Yu
We address the above challenges by replacing the camera and the network with a compact probabilistic abstraction built from the confusion matrices computed for the DNN on a representative image data set.
1 code implementation • 5 Aug 2022 • Muhammad Usman, Youcheng Sun, Divya Gopinath, Rishi Dange, Luca Manolache, Corina S. Pasareanu
Deep neural network (DNN) models, including those used in safety-critical domains, need to be thoroughly tested to ensure that they can reliably perform well in different scenarios.
1 code implementation • 31 Jan 2022 • Muhammad Usman, Youcheng Sun, Divya Gopinath, Corina S. Pasareanu
For correction, we propose an input correction technique that uses a differential analysis to identify the trigger in the detected poisoned images, which is then reset to a neutral color.
no code implementations • 2 Mar 2021 • Colin Paterson, Haoze Wu, John Grese, Radu Calinescu, Corina S. Pasareanu, Clark Barrett
We introduce DeepCert, a tool-supported method for verifying the robustness of deep neural network (DNN) image classifiers to contextually relevant perturbations such as blur, haze, and changes in image contrast.
1 code implementation • 29 Apr 2019 • Divya Gopinath, Hayes Converse, Corina S. Pasareanu, Ankur Taly
We present techniques for automatically inferring formal properties of feed-forward neural networks.
2 code implementations • 16 Nov 2018 • Shirin Nilizadeh, Yannic Noller, Corina S. Pasareanu
For this paper, we present an implementation that targets analysis of Java programs, and uses and extends the Kelinci and AFL fuzzers.
Cryptography and Security Software Engineering
no code implementations • 18 Oct 2018 • Corina S. Pasareanu, Divya Gopinath, Huafeng Yu
As autonomy becomes prevalent in many applications, ranging from recommendation systems to fully autonomous vehicles, there is an increased need to provide safety guarantees for such systems.
no code implementations • 2 Oct 2017 • Divya Gopinath, Guy Katz, Corina S. Pasareanu, Clark Barrett
We propose a novel approach for automatically identifying safe regions of the input space, within which the network is robust against adversarial perturbations.