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Found 8 papers, 4 papers with code
Eidos: Efficient, Imperceptible Adversarial 3D Point Clouds
As with other ML tasks, classification models are notoriously brittle in the presence of adversarial attacks.
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.
Revisiting Transferable Adversarial Image Examples: Attack Categorization, Evaluation Guidelines, and New Insights
Transferable adversarial examples raise critical security concerns in real-world, black-box attack scenarios.
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.
Towards Good Practices in Evaluating Transfer Adversarial Attacks
In this work, we design good practices to address these limitations, and we present the first comprehensive evaluation of transfer attacks, covering 23 representative attacks against 9 defenses on ImageNet.
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.
