Ensemble of Random Binary Output Encoding for Adversarial Robustness
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DOI: 10.1109/access.2019.2937604
Abstract: Despite the excellent classification performance, recent research has revealed that the Convolutional Neural Network (CNN) could be readily deceived by only the small adversarial perturbation. Its imperceptible to human eyes and transferability from one model… read more here.
Keywords: robustness; ensemble random; adversarial robustness; binary output ... See more keywords
Evaluating Adversarial Robustness of Secret Key-Based Defenses
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DOI: 10.1109/access.2022.3162874
Abstract: The vulnerability of neural networks to adversarial attacks has inspired the proposal of many defenses. Key-based input transformation techniques are the recently proposed methods that make use of gradient obfuscation to improve the adversarial robustness… read more here.
Keywords: key based; based defenses; adversarial robustness; transformation ... See more keywords
RoHNAS: A Neural Architecture Search Framework With Conjoint Optimization for Adversarial Robustness and Hardware Efficiency of Convolutional and Capsule Networks
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DOI: 10.1109/access.2022.3214312
Abstract: Neural Architecture Search (NAS) algorithms aim at finding efficient Deep Neural Network (DNN) architectures for a given application under given system constraints. DNNs are computationally-complex as well as vulnerable to adversarial attacks. In order to… read more here.
Keywords: hardware; robustness hardware; hardware efficiency; adversarial robustness ... See more keywords
Adversarial robustness assessment: Why in evaluation both L0 and L∞ attacks are necessary
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DOI: 10.1371/journal.pone.0265723
Abstract: There are different types of adversarial attacks and defences for machine learning algorithms which makes assessing the robustness of an algorithm a daunting task. Moreover, there is an intrinsic bias in these adversarial attacks and… read more here.
Keywords: adversarial robustness; robustness; robustness assessment; assessment evaluation ... See more keywords