Journal articles on the topic '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.
Full textZhou, 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.
Full textGoldblum, 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.
Full textTack, 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.
Full textLiang, 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.
Full textWang, 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.
Full textBui, 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.
Full textLi, 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.
Full textYang, 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.
Full textLi, 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.
Full textLiao, 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.
Full textPereira, 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.
Full textJiale 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.
Full textZhang, 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.
Full textCheng, 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.
Full textSun, 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.
Full textFatehi, 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.
Full textGupta, 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.
Full textJiang, 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.
Full textHou, 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.
Full textWu, 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.
Full textYin, 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.
Full textLee, 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.
Full textMygdalis, 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.
Full textRozsa, 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.
Full textLi, 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.
Full textKotyan, 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.
Full textKhan, 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.
Full textHong, 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.
Full textAgarwal, 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.
Full textImam, 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.
Full textChen, 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.
Full textChen, 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.
Full textChen, 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.
Full textXu, 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.
Full textWu, 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.
Full textYing, 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.
Full textHassanin, 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.
Full textLai, 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.
Full textShi, 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.
Full textFinlay, 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.
Full textLi, 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.
Full textLi, 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.
Full textFawzi, 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.
Full textDong, 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.
Full textPhan, 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.
Full textJeong, 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.
Full textChen, 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.
Full textMa, 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.
Full textSun, 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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