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Curse of Dimensionality in Adversarial Examples

2019 International Joint Conference on Neural Networks (IJCNN), 2019
While machine learning and deep neural networks in particular, have undergone massive progress in the past years, this ubiquitous paradigm faces a relatively newly discovered challenge, adversarial attacks. An adversary can leverage a plethora of attacking algorithms to severely reduce the performance of existing models, therefore threatening the use ...
Nandish Chattopadhyay +3 more
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On the Salience of Adversarial Examples

2019
Adversarial examples are beginning to evolve as rapidly as the deep learning models they are designed to attack. These intentionally-manipulated inputs attempt to mislead the targeted model while maintaining the appearance of innocuous input data. Countermeasures against these attacks that take a global approach tend to be lossy to the original data ...
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Adversarial Examples for Malware Detection

2017
Machine learning models are known to lack robustness against inputs crafted by an adversary. Such adversarial examples can, for instance, be derived from regular inputs by introducing minor—yet carefully selected—perturbations.
Kathrin Grosse +4 more
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Advops: Decoupling Adversarial Examples