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Found 5 papers, 1 papers with code
MUC-driven Feature Importance Measurement and Adversarial Analysis for Random Forest
The broad adoption of Machine Learning (ML) in security-critical fields demands the explainability of the approach.
Adversarial Rain Attack and Defensive Deraining for DNN Perception
To defend the DNNs from the negative rain effect, we also present a defensive deraining strategy, for which we design an adversarial rain augmentation that uses mixed adversarial rain layers to enhance deraining models for downstream DNN perception.
Bias Field Poses a Threat to DNN-based X-Ray Recognition
Our method reveals the potential threat to the DNN-based X-ray automated diagnosis and can definitely benefit the development of bias-field-robust automated diagnosis system.
Toward `verifying' a Water Treatment System
As the system is too complicated to be manually modeled, we apply latest automatic model learning techniques to construct a set of Markov chains through abstraction and refinement, based on two long system execution logs (one for training and the other for testing).
Automatically 'Verifying' Discrete-Time Complex Systems through Learning, Abstraction and Refinement
The other is a probabilistic model based on which the given property is `verified'.
Software Engineering
