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Found 5 papers, 0 papers with code
Adversarial Profiles: Detecting Out-Distribution & Adversarial Samples in Pre-trained CNNs
Here, we propose a method to detect adversarial and out-distribution examples against a pre-trained CNN without needing to retrain the CNN or needing access to a wide variety of fooling examples.
Toward Adversarial Robustness by Diversity in an Ensemble of Specialized Deep Neural Networks
Using MNIST and CIFAR-10, we empirically verify the ability of our ensemble to detect a large portion of well-known black-box adversarial examples, which leads to a significant reduction in the risk rate of adversaries, at the expense of a small increase in the risk rate of clean samples.
Controlling Over-generalization and its Effect on Adversarial Examples Detection and Generation
As an appropriate training set for the extra class, we introduce two resources that are computationally efficient to obtain: a representative natural out-distribution set and interpolated in-distribution samples.
Controlling Over-generalization and its Effect on Adversarial Examples Generation and Detection
As an appropriate training set for the extra class, we introduce two resources that are computationally efficient to obtain: a representative natural out-distribution set and interpolated in-distribution samples.
Towards Dependable Deep Convolutional Neural Networks (CNNs) with Out-distribution Learning
Detection and rejection of adversarial examples in security sensitive and safety-critical systems using deep CNNs is essential.
