Search Results for author:
Found 8 papers, 4 papers with code
DeepSafe: A Data-driven Approach for Checking Adversarial Robustness in Neural Networks
We propose a novel approach for automatically identifying safe regions of the input space, within which the network is robust against adversarial perturbations.
Compositional Verification for Autonomous Systems with Deep Learning Components
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.
DifFuzz: Differential Fuzzing for Side-Channel Analysis
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
Property Inference for Deep Neural Networks
We present techniques for automatically inferring formal properties of feed-forward neural networks.
DeepCert: Verification of Contextually Relevant Robustness for Neural Network Image Classifiers
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.
AntidoteRT: Run-time Detection and Correction of Poison Attacks on Neural Networks
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.
An Overview of Structural Coverage Metrics for Testing Neural Networks
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.
Closed-loop Analysis of Vision-based Autonomous Systems: A Case Study
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.
