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Parametric Noise Injection: Trainable Randomness to Improve Deep Neural Network Robustness Against Adversarial Attack

机译：参数噪声注射：可训练随机性，以提高对抗对抗攻击的深度神经网络鲁棒性

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Zhezhi He; Adnan Siraj Rakin; Deliang Fan;
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年度 2019
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1. Integration of statistical detector and Gaussian noise injection detector for adversarial example detection in deep neural networks [J] . Fan Weiqi, Sun Guangling, Su Yuying, Multimedia Tools and Applications . 2019,第14期

机译：统计检测器和高斯噪声注入检测器的集成，用于深度神经网络中的对抗性示例检测
2. Interpreting and Improving Adversarial Robustness of Deep Neural Networks With Neuron Sensitivity [J] . Chongzhi Zhang, Aishan Liu, Xianglong Liu, IEEE Transactions on Image Processing . 2021,第1期

机译：用神经元敏感性解释和改善深层神经网络的对抗鲁棒性
3. Improving adversarial robustness of deep neural networks by using semantic information [J] . Wang Lina, Chen Xingshu, Tang Rui, Knowledge-Based Systems . 2021,第Auga17期

机译：使用语义信息改善深神经网络的对抗性鲁棒性
4. Efficacy of Defending Deep Neural Networks against Adversarial Attacks with Randomization [C] . Yan Zhou, Murat Kantarcioglu, Bowei Xi Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications Conference . 2020

机译：防御深度神经网络对抗随机攻击的功效
5. Adversarial Attacks Against Deep Neural Networks [D] . Koirala, Nirajan. 2021

机译：对深神经网络的对抗攻击
6. Automated Pulmonary Nodule Classification in Computed Tomography Images Using a Deep Convolutional Neural Network Trained by Generative Adversarial Networks [O] . Yuya Onishi, Atsushi Teramoto, Masakazu Tsujimoto, 2006

机译：使用由生成对抗网络训练的深卷积神经网络在计算机断层扫描图像中自动进行肺结节分类
7. On Robustness of Deep Neural Networks: A Comprehensive Study on the Effect of Architecture and Weight Initialization to Susceptibility and Transferability of Adversarial Attacks [O] . Ibrahim Ben Daya, Mohammad Javad Shaifee, Michelle Karg, 2018

机译：深度神经网络的鲁棒性：综合建筑与体重初始化对对抗敏感性和可转移性的综合研究

Parametric Noise Injection: Trainable Randomness to Improve Deep Neural Network Robustness Against Adversarial Attack

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