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Topology Attack and Defense for Graph Neural Networks: An Optimization Perspective.

, , , , , , and . IJCAI, page 3961-3967. ijcai.org, (2019)

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Evaluating the Robustness of Neural Networks: An Extreme Value Theory Approach., , , , , , , and . ICLR (Poster), OpenReview.net, (2018)Characterizing Adversarial Subspaces by Mutual Information., , and . AsiaCCS, page 667-669. ACM, (2019)Incremental Eigenpair Computation for Graph Laplacian Matrices: Theory and Applications., , and . CoRR, (2018)Neural Clamping: Joint Input Perturbation and Temperature Scaling for Neural Network Calibration., , and . CoRR, (2022)Certified Interpretability Robustness for Class Activation Mapping., , , , and . CoRR, (2023)Exploring the Benefits of Visual Prompting in Differential Privacy., , , , and . ICCV, page 5135-5144. IEEE, (2023)ZOO: Zeroth Order Optimization Based Black-box Attacks to Deep Neural Networks without Training Substitute Models., , , , and . AISec@CCS, page 15-26. ACM, (2017)Distributed adversarial training to robustify deep neural networks at scale., , , , , , , , , and . UAI, volume 180 of Proceedings of Machine Learning Research, page 2353-2363. PMLR, (2022)Non-Singular Adversarial Robustness of Neural Networks., , , and . ICASSP, page 3840-3844. IEEE, (2021)Real-World Adversarial Examples Via Makeup., , , and . ICASSP, page 2854-2858. IEEE, (2022)