Results 11 to 20 of about 85,147 (260)
Achieving adversarial robustness via sparsity [PDF]
Machine Learning, 2021 Network pruning has been known to produce compact models without much accuracy degradation. However, how the pruning process affects a network's robustness and the working mechanism behind remain unresolved. In this work, we theoretically prove that the sparsity of network weights is closely associated with model robustness.
Ningyi Liao +5 more
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Intelligent Systems with Applications, 2023 Deep neural networks, despite their remarkable success in various language understanding tasks, have been found vulnerable to adversarial attacks and subtle input perturbations, revealing a robustness shortfall.
Jingliang Fang +5 more
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Adversarial Robustness Curves [PDF]
, 2020 The existence of adversarial examples has led to considerable uncertainty regarding the trust one can justifiably put in predictions produced by automated systems. This uncertainty has, in turn, lead to considerable research effort in understanding adversarial robustness. In this work, we take first steps towards separating robustness analysis from the
Göpfert, Christina +2 more
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A Robust Adversarial Example Attack Based on Video Augmentation
Applied Sciences, 2023 Despite the success of learning-based systems, recent studies have highlighted video adversarial examples as a ubiquitous threat to state-of-the-art video classification systems.
Mingyong Yin +3 more
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Study on Adversarial Robustness of Deep Learning Models Based on SVD [PDF]
Jisuanji kexue, 2023 The emergence of adversarial attacks poses a substantial threat to the large-scale deployment of deep neural networks(DNNs) in real-world scenarios,especially in security-related domains.Most of the current defense methods are based on heuristic ...
ZHAO Zitian, ZHAN Wenhan, DUAN Hancong, WU Yue
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Towards Adversarial Robustness via Feature Matching
IEEE Access, 2020 Image classification systems are known to be vulnerable to adversarial attacks, which are imperceptibly perturbed but lead to spectacularly disgraceful classification.
Zhuorong Li +4 more
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网络与信息安全学报, 2022 Adversarial training is one of the commonly used defense methods against adversarial attacks, by incorporating adversarial samples into the training process.However, the effectiveness of adversarial training heavily relied on the size of the trained ...
Bin WANG, Simin LI, Yaguan QIAN, Jun ZHANG, Chaohao LI, Chenming ZHU, Hongfei ZHANG
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Are facial attributes adversarially robust? [PDF]
2016 23rd International Conference on Pattern Recognition (ICPR), 2016 Facial attributes are emerging soft biometrics that have the potential to reject non-matches, for example, based on mismatching gender. To be usable in stand-alone systems, facial attributes must be extracted from images automatically and reliably. In this paper, we propose a simple yet effective solution for automatic facial attribute extraction by ...
Rozsa, Andras +3 more
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Adversarial Robustness Via Fisher-Rao Regularization
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023 Adversarial robustness has become a topic of growing interest in machine learning since it was observed that neural networks tend to be brittle. We propose an information-geometric formulation of adversarial defense and introduce FIRE, a new Fisher-Rao regularization for the categorical cross-entropy loss, which is based on the geodesic distance ...
Picot, Marine +6 more
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Robust Tracking Against Adversarial Attacks [PDF]
, 2020 While deep convolutional neural networks (CNNs) are vulnerable to adversarial attacks, considerably few efforts have been paid to construct robust deep tracking algorithms against adversarial attacks. Current studies on adversarial attack and defense mainly reside in a single image. In this work, we first attempt to generate adversarial examples on top
Jia, Shuai +3 more
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