Efficient Adversarial Training without Attacking:Worst-Case-Aware Robust Reinforcement Learning
Efficient Adversarial Training without Attacking:Worst-Case-Aware Robust Reinforcement Learning
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Efficient Adversarial Training without Attacking:Worst-Case-Aware Robust Reinforcement Learning

Efficient Adversarial Training without Attacking:Worst-Case-Aware Robust Reinforcement Learning

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USENIX Security '21 - Adversarial Policy Training against Deep Reinforcement Learning

USENIX Security '21 - Adversarial Policy Training against Deep Reinforcement Learning

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ICASSP 20 - Enhanced Adversarial Strategically-Timed Attacks against Deep Reinforcement Learning

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Toward Learning Robust Detectors from Imbalanced Datasets Leveraging Weighted Adversarial Training

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Incorporating robust control guarantees within (deep) reinforcement learning

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Marek Petrik: Tight Bayesian Ambiguity Sets for Robust MDPs

Marek Petrik: Tight Bayesian Ambiguity Sets for Robust MDPs

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USENIX Security '20 - DeepHammer: Depleting the Intelligence of Deep Neural Networks through Target

USENIX Security '20 - DeepHammer: Depleting the Intelligence of Deep Neural Networks through Target

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Benchmarking Adversarial Robustness on Image Classification

Benchmarking Adversarial Robustness on Image Classification

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6. Noa Weiss - The Unspoken Problems With Machine Learning in Security

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Towards Deep Learning Models Resistant to Adversarial Attacks

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USENIX Security '22 - Inference Attacks Against Graph Neural Networks

USENIX Security '22 - Inference Attacks Against Graph Neural Networks

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Implementing Non-Trivial attack scenarios for evaluating AI based cyber security systems

Implementing Non-Trivial attack scenarios for evaluating AI based cyber security systems

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Backdooring and Poisoning Neural Networks with Image-Scaling Attacks

Backdooring and Poisoning Neural Networks with Image-Scaling Attacks

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Attack Vectors for IoT AI Models

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