Results 41 to 50 of about 3,240,231 (313)
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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Feature Separation and Recalibration for Adversarial Robustness [PDF]
Computer Vision and Pattern Recognition, 2023 Deep neural networks are susceptible to adversarial attacks due to the accumulation of perturbations in the feature level, and numerous works have boosted model robustness by deactivating the non-robust feature activations that cause model mispredictions.
W. Kim +3 more
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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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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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Language-Driven Anchors for Zero-Shot Adversarial Robustness [PDF]
Computer Vision and Pattern Recognition, 2023 Deep Neural Networks (DNNs) are known to be susceptible to adversarial attacks. Previous researches mainly fo-cus on improving adversarial robustness in the fully super-vised setting, leaving the challenging domain of zero-shot adversarial robustness an ...
Xiao Li +5 more
semanticscholar +1 more source
On the Importance of Backbone to the Adversarial Robustness of Object Detectors [PDF]
IEEE Transactions on Information Forensics and Security, 2023 Object detection is a critical component of various security-sensitive applications, such as autonomous driving and video surveillance. However, existing object detectors are vulnerable to adversarial attacks, which poses a significant challenge to their
Xiao Li, Hang Chen, Xiaolin Hu
semanticscholar +1 more source
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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Adversarial Robustness of Visual Dialog
CoRR, 2022 Adversarial robustness evaluates the worst-case performance scenario of a machine learning model to ensure its safety and reliability. This study is the first to investigate the robustness of visually grounded dialog models towards textual attacks. These attacks represent a worst-case scenario where the input question contains a synonym which causes ...
Lu Yu, Verena Rieser
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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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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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