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Found 7 papers, 3 papers with code
ADVREPAIR:Provable Repair of Adversarial Attack
Existing neuron-level methods using limited data lack efficacy in fixing adversaries due to the inherent complexity of adversarial attack mechanisms, while adversarial training, leveraging a large number of adversarial samples to enhance robustness, lacks provability.
TrajPAC: Towards Robustness Verification of Pedestrian Trajectory Prediction Models
Firstly, the previous definitions of robustness in trajectory prediction are ambiguous.
Incremental Satisfiability Modulo Theory for Verification of Deep Neural Networks
Moreover, based on the framework, we propose the multi-objective DNN repair problem and give an algorithm based on our incremental SMT solving algorithm.
Safety Analysis of Autonomous Driving Systems Based on Model Learning
The safety properties proved in the resulting surrogate model apply to the original ADS with a probabilistic guarantee.
Ensemble Defense with Data Diversity: Weak Correlation Implies Strong Robustness
In this paper, we propose a framework of filter-based ensemble of deep neuralnetworks (DNNs) to defend against adversarial attacks.
Towards Practical Robustness Analysis for DNNs based on PAC-Model Learning
It is shown that DeepPAC outperforms the state-of-the-art statistical method PROVERO, and it achieves more practical robustness analysis than the formal verification tool ERAN.
Improving Neural Network Verification through Spurious Region Guided Refinement
The core idea is to make use of the obtained constraints of the abstraction to infer new bounds for the neurons.
