Статті в журналах з теми "Adversarial robustness"
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Doan, Bao Gia, Shuiqiao Yang, Paul Montague, Olivier De Vel, Tamas Abraham, Seyit Camtepe, Salil S. Kanhere, Ehsan Abbasnejad, and Damith C. Ranashinghe. "Feature-Space Bayesian Adversarial Learning Improved Malware Detector Robustness." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 12 (June 26, 2023): 14783–91. http://dx.doi.org/10.1609/aaai.v37i12.26727.
Повний текст джерелаZhou, Xiaoling, Nan Yang, and Ou Wu. "Combining Adversaries with Anti-adversaries in Training." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 9 (June 26, 2023): 11435–42. http://dx.doi.org/10.1609/aaai.v37i9.26352.
Повний текст джерелаGoldblum, Micah, Liam Fowl, Soheil Feizi, and Tom Goldstein. "Adversarially Robust Distillation." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 3996–4003. http://dx.doi.org/10.1609/aaai.v34i04.5816.
Повний текст джерелаTack, Jihoon, Sihyun Yu, Jongheon Jeong, Minseon Kim, Sung Ju Hwang, and Jinwoo Shin. "Consistency Regularization for Adversarial Robustness." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 8 (June 28, 2022): 8414–22. http://dx.doi.org/10.1609/aaai.v36i8.20817.
Повний текст джерелаLiang, Youwei, and Dong Huang. "Large Norms of CNN Layers Do Not Hurt Adversarial Robustness." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 10 (May 18, 2021): 8565–73. http://dx.doi.org/10.1609/aaai.v35i10.17039.
Повний текст джерелаWang, Desheng, Weidong Jin, and Yunpu Wu. "Between-Class Adversarial Training for Improving Adversarial Robustness of Image Classification." Sensors 23, no. 6 (March 20, 2023): 3252. http://dx.doi.org/10.3390/s23063252.
Повний текст джерелаBui, Anh Tuan, Trung Le, He Zhao, Paul Montague, Olivier DeVel, Tamas Abraham, and Dinh Phung. "Improving Ensemble Robustness by Collaboratively Promoting and Demoting Adversarial Robustness." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 8 (May 18, 2021): 6831–39. http://dx.doi.org/10.1609/aaai.v35i8.16843.
Повний текст джерелаLi, Xin, Xiangrui Li, Deng Pan, and Dongxiao Zhu. "Improving Adversarial Robustness via Probabilistically Compact Loss with Logit Constraints." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 10 (May 18, 2021): 8482–90. http://dx.doi.org/10.1609/aaai.v35i10.17030.
Повний текст джерелаYang, Shuo, Tianyu Guo, Yunhe Wang, and Chang Xu. "Adversarial Robustness through Disentangled Representations." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 4 (May 18, 2021): 3145–53. http://dx.doi.org/10.1609/aaai.v35i4.16424.
Повний текст джерелаLi, Zhuorong, Chao Feng, Minghui Wu, Hongchuan Yu, Jianwei Zheng, and Fanwei Zhu. "Adversarial robustness via attention transfer." Pattern Recognition Letters 146 (June 2021): 172–78. http://dx.doi.org/10.1016/j.patrec.2021.03.011.
Повний текст джерелаLiao, Ningyi, Shufan Wang, Liyao Xiang, Nanyang Ye, Shuo Shao, and Pengzhi Chu. "Achieving adversarial robustness via sparsity." Machine Learning 111, no. 2 (October 12, 2021): 685–711. http://dx.doi.org/10.1007/s10994-021-06049-9.
Повний текст джерелаPereira, Gean T., and André C. P. L. F. de Carvalho. "Bringing robustness against adversarial attacks." Nature Machine Intelligence 1, no. 11 (November 2019): 499–500. http://dx.doi.org/10.1038/s42256-019-0116-2.
Повний текст джерелаJiale Yan, Jiale Yan, Yang Xu Jiale Yan, Sicong Zhang Yang Xu, Kezi Li Sicong Zhang, and Xiaoyao Xie Kezi Li. "Improving Adversarial Robustness via Finding Flat Minimum of the Weight Loss Landscape." 電腦學刊 34, no. 1 (February 2023): 029–43. http://dx.doi.org/10.53106/199115992023023401003.
Повний текст джерелаZhang, Jie, Bo Li, Chen Chen, Lingjuan Lyu, Shuang Wu, Shouhong Ding, and Chao Wu. "Delving into the Adversarial Robustness of Federated Learning." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 9 (June 26, 2023): 11245–53. http://dx.doi.org/10.1609/aaai.v37i9.26331.
Повний текст джерелаCheng, Zhi, Yanxi Li, Minjing Dong, Xiu Su, Shan You, and Chang Xu. "Neural Architecture Search for Wide Spectrum Adversarial Robustness." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 1 (June 26, 2023): 442–51. http://dx.doi.org/10.1609/aaai.v37i1.25118.
Повний текст джерелаSun, Guangling, Yuying Su, Chuan Qin, Wenbo Xu, Xiaofeng Lu, and Andrzej Ceglowski. "Complete Defense Framework to Protect Deep Neural Networks against Adversarial Examples." Mathematical Problems in Engineering 2020 (May 11, 2020): 1–17. http://dx.doi.org/10.1155/2020/8319249.
Повний текст джерелаFatehi, Nina, Qutaiba Alasad, and Mohammed Alawad. "Towards Adversarial Attacks for Clinical Document Classification." Electronics 12, no. 1 (December 28, 2022): 129. http://dx.doi.org/10.3390/electronics12010129.
Повний текст джерелаGupta, Kartik, and Thalaiyasingam Ajanthan. "Improved Gradient-Based Adversarial Attacks for Quantized Networks." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 6 (June 28, 2022): 6810–18. http://dx.doi.org/10.1609/aaai.v36i6.20637.
Повний текст джерелаJiang, Guoteng, Zhuang Qian, Qiu-Feng Wang, Yan Wei, and Kaizhu Huang. "Adversarial Attack and Defence on Handwritten Chinese Character Recognition." Journal of Physics: Conference Series 2278, no. 1 (May 1, 2022): 012023. http://dx.doi.org/10.1088/1742-6596/2278/1/012023.
Повний текст джерелаHou, Pengyue, Jie Han, and Xingyu Li. "Improving Adversarial Robustness with Self-Paced Hard-Class Pair Reweighting." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 12 (June 26, 2023): 14883–91. http://dx.doi.org/10.1609/aaai.v37i12.26738.
Повний текст джерелаWu, Jiaping, Zhaoqiang Xia, and Xiaoyi Feng. "Improving Adversarial Robustness of CNNs via Maximum Margin." Applied Sciences 12, no. 15 (August 8, 2022): 7927. http://dx.doi.org/10.3390/app12157927.
Повний текст джерелаYin, Mingyong, Yixiao Xu, Teng Hu, and Xiaolei Liu. "A Robust Adversarial Example Attack Based on Video Augmentation." Applied Sciences 13, no. 3 (February 1, 2023): 1914. http://dx.doi.org/10.3390/app13031914.
Повний текст джерелаLee, Youngseok, and Jongweon Kim. "Robustness of Deep Learning Models for Vision Tasks." Applied Sciences 13, no. 7 (March 30, 2023): 4422. http://dx.doi.org/10.3390/app13074422.
Повний текст джерелаMygdalis, Vasileios, and Ioannis Pitas. "Hyperspherical class prototypes for adversarial robustness." Pattern Recognition 125 (May 2022): 108527. http://dx.doi.org/10.1016/j.patcog.2022.108527.
Повний текст джерелаRozsa, Andras, Manuel Günther, Ethan M. Rudd, and Terrance E. Boult. "Facial attributes: Accuracy and adversarial robustness." Pattern Recognition Letters 124 (June 2019): 100–108. http://dx.doi.org/10.1016/j.patrec.2017.10.024.
Повний текст джерелаLi, Zhuorong, Chao Feng, Jianwei Zheng, Minghui Wu, and Hongchuan Yu. "Towards Adversarial Robustness via Feature Matching." IEEE Access 8 (2020): 88594–603. http://dx.doi.org/10.1109/access.2020.2993304.
Повний текст джерелаKotyan, Shashank, and Danilo Vasconcellos Vargas. "Adversarial robustness assessment: Why in evaluation both L0 and L∞ attacks are necessary." PLOS ONE 17, no. 4 (April 14, 2022): e0265723. http://dx.doi.org/10.1371/journal.pone.0265723.
Повний текст джерелаKhan, Sarwar, Jun-Cheng Chen, Wen-Hung Liao, and Chu-Song Chen. "Towards Adversarial Robustness for Multi-Mode Data through Metric Learning." Sensors 23, no. 13 (July 5, 2023): 6173. http://dx.doi.org/10.3390/s23136173.
Повний текст джерелаHong, Junyuan, Haotao Wang, Zhangyang Wang, and Jiayu Zhou. "Federated Robustness Propagation: Sharing Adversarial Robustness in Heterogeneous Federated Learning." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 7 (June 26, 2023): 7893–901. http://dx.doi.org/10.1609/aaai.v37i7.25955.
Повний текст джерелаAgarwal, Akshay, Mayank Vatsa, and Richa Singh. "Role of Optimizer on Network Fine-tuning for Adversarial Robustness (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 18 (May 18, 2021): 15745–46. http://dx.doi.org/10.1609/aaai.v35i18.17869.
Повний текст джерелаImam, Niddal H., and Vassilios G. Vassilakis. "A Survey of Attacks Against Twitter Spam Detectors in an Adversarial Environment." Robotics 8, no. 3 (July 4, 2019): 50. http://dx.doi.org/10.3390/robotics8030050.
Повний текст джерелаChen, Hanjie, and Yangfeng Ji. "Adversarial Training for Improving Model Robustness? Look at Both Prediction and Interpretation." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 10 (June 28, 2022): 10463–72. http://dx.doi.org/10.1609/aaai.v36i10.21289.
Повний текст джерелаChen, Jinghui, Yu Cheng, Zhe Gan, Quanquan Gu, and Jingjing Liu. "Efficient Robust Training via Backward Smoothing." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 6 (June 28, 2022): 6222–30. http://dx.doi.org/10.1609/aaai.v36i6.20571.
Повний текст джерелаChen, Pin-Yu, and Sijia Liu. "Holistic Adversarial Robustness of Deep Learning Models." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 13 (June 26, 2023): 15411–20. http://dx.doi.org/10.1609/aaai.v37i13.26797.
Повний текст джерелаXu, Yanjie, Hao Sun, Jin Chen, Lin Lei, Kefeng Ji, and Gangyao Kuang. "Adversarial Self-Supervised Learning for Robust SAR Target Recognition." Remote Sensing 13, no. 20 (October 17, 2021): 4158. http://dx.doi.org/10.3390/rs13204158.
Повний текст джерелаWu, Hongqiu, Ruixue Ding, Hai Zhao, Pengjun Xie, Fei Huang, and Min Zhang. "Adversarial Self-Attention for Language Understanding." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 11 (June 26, 2023): 13727–35. http://dx.doi.org/10.1609/aaai.v37i11.26608.
Повний текст джерелаYing, Chao Ma &. Lexing. "Achieving Adversarial Robustness Requires An Active Teacher." Journal of Computational Mathematics 39, no. 6 (June 2021): 880–96. http://dx.doi.org/10.4208/jcm.2105-m2020-0310.
Повний текст джерелаHassanin, Mohammed, Nour Moustafa, Murat Tahtali, and Kim-Kwang Raymond Choo. "Rethinking maximum-margin softmax for adversarial robustness." Computers & Security 116 (May 2022): 102640. http://dx.doi.org/10.1016/j.cose.2022.102640.
Повний текст джерелаLai, Lifeng, and Erhan Bayraktar. "On the Adversarial Robustness of Robust Estimators." IEEE Transactions on Information Theory 66, no. 8 (August 2020): 5097–109. http://dx.doi.org/10.1109/tit.2020.2985966.
Повний текст джерелаShi, Yucheng, Yahong Han, Quanxin Zhang, and Xiaohui Kuang. "Adaptive iterative attack towards explainable adversarial robustness." Pattern Recognition 105 (September 2020): 107309. http://dx.doi.org/10.1016/j.patcog.2020.107309.
Повний текст джерелаFinlay, Chris, and Adam M. Oberman. "Scaleable input gradient regularization for adversarial robustness." Machine Learning with Applications 3 (March 2021): 100017. http://dx.doi.org/10.1016/j.mlwa.2020.100017.
Повний текст джерелаLi, Fuwei, Lifeng Lai, and Shuguang Cui. "On the Adversarial Robustness of Subspace Learning." IEEE Transactions on Signal Processing 68 (2020): 1470–83. http://dx.doi.org/10.1109/tsp.2020.2974676.
Повний текст джерелаLi, Tianlin, Aishan Liu, Xianglong Liu, Yitao Xu, Chongzhi Zhang, and Xiaofei Xie. "Understanding adversarial robustness via critical attacking route." Information Sciences 547 (February 2021): 568–78. http://dx.doi.org/10.1016/j.ins.2020.08.043.
Повний текст джерелаFawzi, Alhussein, Omar Fawzi, and Pascal Frossard. "Analysis of classifiers’ robustness to adversarial perturbations." Machine Learning 107, no. 3 (August 25, 2017): 481–508. http://dx.doi.org/10.1007/s10994-017-5663-3.
Повний текст джерелаDong, Junhao, Lingxiao Yang, Yuan Wang, Xiaohua Xie, and Jianhuang Lai. "Toward Intrinsic Adversarial Robustness Through Probabilistic Training." IEEE Transactions on Image Processing 32 (2023): 3862–72. http://dx.doi.org/10.1109/tip.2023.3290532.
Повний текст джерелаPhan, Huy, Yi Xie, Siyu Liao, Jie Chen, and Bo Yuan. "CAG: A Real-Time Low-Cost Enhanced-Robustness High-Transferability Content-Aware Adversarial Attack Generator." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 5412–19. http://dx.doi.org/10.1609/aaai.v34i04.5990.
Повний текст джерелаJeong, Jongheon, Seojin Kim, and Jinwoo Shin. "Confidence-Aware Training of Smoothed Classifiers for Certified Robustness." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 7 (June 26, 2023): 8005–13. http://dx.doi.org/10.1609/aaai.v37i7.25968.
Повний текст джерелаChen, Xu, Chuancai Liu, Yue Zhao, Zhiyang Jia, and Ge Jin. "Improving adversarial robustness of Bayesian neural networks via multi-task adversarial training." Information Sciences 592 (May 2022): 156–73. http://dx.doi.org/10.1016/j.ins.2022.01.051.
Повний текст джерелаMa, Linhai, and Liang Liang. "Increasing-Margin Adversarial (IMA) training to improve adversarial robustness of neural networks." Computer Methods and Programs in Biomedicine 240 (October 2023): 107687. http://dx.doi.org/10.1016/j.cmpb.2023.107687.
Повний текст джерелаSun, Liting, Da Ke, Xiang Wang, Zhitao Huang, and Kaizhu Huang. "Robustness of Deep Learning-Based Specific Emitter Identification under Adversarial Attacks." Remote Sensing 14, no. 19 (October 7, 2022): 4996. http://dx.doi.org/10.3390/rs14194996.
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