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Gradient Masking and the Underestimated Robustness Threats of Differential Privacy in Deep Learning.

, , and . CoRR, (2021)

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DA3G: Detecting Adversarial Attacks by Analysing Gradients., , and . ESORICS (1), volume 12972 of Lecture Notes in Computer Science, page 563-583. Springer, (2021)Protecting Publicly Available Data With Machine Learning Shortcuts., , , , , and . BMVC, page 685-698. BMVA Press, (2023)Physical Adversarial Examples for Multi-Camera Systems., , , and . CoRR, (2023)Double-Adversarial Activation Anomaly Detection: Adversarial Autoencoders are Anomaly Generators., , and . IJCNN, page 1-8. IEEE, (2022)DLA: Dense-Layer-Analysis for Adversarial Example Detection., , , , and . EuroS&P, page 198-215. IEEE, (2020)Defending Neural Networks with Activation Analysis.. Technical University of Munich, Germany, (2023)Side-Channel Aware Fuzzing., and . ESORICS (1), volume 11735 of Lecture Notes in Computer Science, page 259-278. Springer, (2019)Shortcut Detection with Variational Autoencoders., , , , and . CoRR, (2023)Activation Anomaly Analysis., , and . ECML/PKDD (2), volume 12458 of Lecture Notes in Computer Science, page 69-84. Springer, (2020)Assessing the Impact of Transformations on Physical Adversarial Attacks., , , and . AISec@CCS, page 79-90. ACM, (2022)