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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
Christina Göpfert +2 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
doaj +1 more source
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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Adversarially Robust Distillation
Proceedings of the AAAI Conference on Artificial Intelligence, 2020 Knowledge distillation is effective for producing small, high-performance neural networks for classification, but these small networks are vulnerable to adversarial attacks. This paper studies how adversarial robustness transfers from teacher to student during knowledge distillation.
Micah Goldblum +3 more
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Evaluating Membership Inference Through Adversarial Robustness
, 2022 The usage of deep learning is being escalated in many applications. Due to its outstanding performance, it is being used in a variety of security and privacy-sensitive areas in addition to conventional applications.
Hu, Shengshan +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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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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Adversarial Robustness for Code
CoRR, 2020 Proceedings of the 37th International Conference on Machine ...
Bielik, Pavol, Vechev, Martin
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Adversarially Robust Learning with Tolerance
CoRR, 2022 We initiate the study of tolerant adversarial PAC-learning with respect to metric perturbation sets. In adversarial PAC-learning, an adversary is allowed to replace a test point $x$ with an arbitrary point in a closed ball of radius $r$ centered at $x$.
Hassan Ashtiani +2 more
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Deep Architecture Enhancing Robustness to Noise, Adversarial Attacks, and Cross-Corpus Setting for Speech Emotion Recognition [PDF]
, 2020 Speech emotion recognition systems (SER) can achieve high accuracy when the training and test data are identically distributed, but this assumption is frequently violated in practice and the performance of SER systems plummet against unforeseen data ...
Raja Jurdak +9 more
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