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Code relative to "Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks"
A list of papers in NeurIPS 2022 related to adversarial attack and defense / AI security.
Adaptive evaluation reveals that most examined adversarial defenses for GNNs show no or only marginal improvement in robustness. (NeurIPS 2022)
Official implementation of Segmentation and Complete (SAC) defense.
Official code for "PubDef: Defending Against Transfer Attacks From Public Models" (ICLR 2024)
DeepDefend is an open-source Python library for adversarial attacks and defenses in deep learning models, enhancing the security and robustness of AI systems.
Simple code related to adversarial examples, attacks, and defenses.