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Found 4 papers, 2 papers with code
Unrestricted Adversarial Attacks on ImageNet Competition
Many works have investigated the adversarial attacks or defenses under the settings where a bounded and imperceptible perturbation can be added to the input.
Adversarial Attacks on ML Defense Models Competition
Due to the vulnerability of deep neural networks (DNNs) to adversarial examples, a large number of defense techniques have been proposed to alleviate this problem in recent years.
Automated Decision-based Adversarial Attacks
Deep learning models are vulnerable to adversarial examples, which can fool a target classifier by imposing imperceptible perturbations onto natural examples.
Benchmarking Adversarial Robustness
Deep neural networks are vulnerable to adversarial examples, which becomes one of the most important research problems in the development of deep learning.
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