Статті в журналах з теми "Adversarial Attack and Defense"
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Park, Sanglee, and Jungmin So. "On the Effectiveness of Adversarial Training in Defending against Adversarial Example Attacks for Image Classification." Applied Sciences 10, no. 22 (November 14, 2020): 8079. http://dx.doi.org/10.3390/app10228079.
Повний текст джерелаTang, Renzhi, Guowei Shen, Chun Guo, and Yunhe Cui. "SAD: Website Fingerprinting Defense Based on Adversarial Examples." Security and Communication Networks 2022 (April 7, 2022): 1–12. http://dx.doi.org/10.1155/2022/7330465.
Повний текст джерелаZheng, Tianhang, Changyou Chen, and Kui Ren. "Distributionally Adversarial Attack." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 2253–60. http://dx.doi.org/10.1609/aaai.v33i01.33012253.
Повний текст джерелаLiang, Hongshuo, Erlu He, Yangyang Zhao, Zhe Jia, and Hao Li. "Adversarial Attack and Defense: A Survey." Electronics 11, no. 8 (April 18, 2022): 1283. http://dx.doi.org/10.3390/electronics11081283.
Повний текст джерелаZeng, Huimin, Chen Zhu, Tom Goldstein, and Furong Huang. "Are Adversarial Examples Created Equal? A Learnable Weighted Minimax Risk for Robustness under Non-uniform Attacks." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 12 (May 18, 2021): 10815–23. http://dx.doi.org/10.1609/aaai.v35i12.17292.
Повний текст джерелаRosenberg, Ishai, Asaf Shabtai, Yuval Elovici, and Lior Rokach. "Adversarial Machine Learning Attacks and Defense Methods in the Cyber Security Domain." ACM Computing Surveys 54, no. 5 (June 2021): 1–36. http://dx.doi.org/10.1145/3453158.
Повний текст джерелаQiu, Shilin, Qihe Liu, Shijie Zhou, and Chunjiang Wu. "Review of Artificial Intelligence Adversarial Attack and Defense Technologies." Applied Sciences 9, no. 5 (March 4, 2019): 909. http://dx.doi.org/10.3390/app9050909.
Повний текст джерелаYang, Kaichen, Tzungyu Tsai, Honggang Yu, Tsung-Yi Ho, and Yier Jin. "Beyond Digital Domain: Fooling Deep Learning Based Recognition System in Physical World." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 01 (April 3, 2020): 1088–95. http://dx.doi.org/10.1609/aaai.v34i01.5459.
Повний текст джерелаShi, Lin, Teyi Liao, and Jianfeng He. "Defending Adversarial Attacks against DNN Image Classification Models by a Noise-Fusion Method." Electronics 11, no. 12 (June 8, 2022): 1814. http://dx.doi.org/10.3390/electronics11121814.
Повний текст джерелаShieh, Chin-Shiuh, Thanh-Tuan Nguyen, Wan-Wei Lin, Wei Kuang Lai, Mong-Fong Horng, and Denis Miu. "Detection of Adversarial DDoS Attacks Using Symmetric Defense Generative Adversarial Networks." Electronics 11, no. 13 (June 24, 2022): 1977. http://dx.doi.org/10.3390/electronics11131977.
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 джерелаTan, Hao, Le Wang, Huan Zhang, Junjian Zhang, Muhammad Shafiq, and Zhaoquan Gu. "Adversarial Attack and Defense Strategies of Speaker Recognition Systems: A Survey." Electronics 11, no. 14 (July 12, 2022): 2183. http://dx.doi.org/10.3390/electronics11142183.
Повний текст джерелаLi, Feng, Xuehui Du, and Liu Zhang. "Adversarial Attacks Defense Method Based on Multiple Filtering and Image Rotation." Discrete Dynamics in Nature and Society 2022 (April 16, 2022): 1–11. http://dx.doi.org/10.1155/2022/6124895.
Повний текст джерелаDankwa, Stephen, and Lu Yang. "Securing IoT Devices: A Robust and Efficient Deep Learning with a Mixed Batch Adversarial Generation Process for CAPTCHA Security Verification." Electronics 10, no. 15 (July 27, 2021): 1798. http://dx.doi.org/10.3390/electronics10151798.
Повний текст джерелаJiang, Lingyun, Kai Qiao, Ruoxi Qin, Linyuan Wang, Wanting Yu, Jian Chen, Haibing Bu, and Bin Yan. "Cycle-Consistent Adversarial GAN: The Integration of Adversarial Attack and Defense." Security and Communication Networks 2020 (February 21, 2020): 1–9. http://dx.doi.org/10.1155/2020/3608173.
Повний текст джерелаJiang, Yi, and Dengpan Ye. "Black-Box Adversarial Attacks against Audio Forensics Models." Security and Communication Networks 2022 (January 17, 2022): 1–8. http://dx.doi.org/10.1155/2022/6410478.
Повний текст джерелаXu, Qiuling, Guanhong Tao, Siyuan Cheng, and Xiangyu Zhang. "Towards Feature Space Adversarial Attack by Style Perturbation." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 12 (May 18, 2021): 10523–31. http://dx.doi.org/10.1609/aaai.v35i12.17259.
Повний текст джерелаXu, Weizhen, Chenyi Zhang, Fangzhen Zhao, and Liangda Fang. "A Mask-Based Adversarial Defense Scheme." Algorithms 15, no. 12 (December 6, 2022): 461. http://dx.doi.org/10.3390/a15120461.
Повний текст джерелаWang, Tianfeng, Zhisong Pan, Guyu Hu, Yexin Duan, and Yu Pan. "Understanding Universal Adversarial Attack and Defense on Graph." International Journal on Semantic Web and Information Systems 18, no. 1 (January 1, 2022): 1–21. http://dx.doi.org/10.4018/ijswis.308812.
Повний текст джерелаQiao, Zhi, Zhenqiang Wu, Jiawang Chen, Ping’an Ren, and Zhiliang Yu. "A Lightweight Method for Defense Graph Neural Networks Adversarial Attacks." Entropy 25, no. 1 (December 25, 2022): 39. http://dx.doi.org/10.3390/e25010039.
Повний текст джерелаLi, Deqiang, Qianmu Li, Yanfang (Fanny) Ye, and Shouhuai Xu. "Arms Race in Adversarial Malware Detection: A Survey." ACM Computing Surveys 55, no. 1 (January 31, 2023): 1–35. http://dx.doi.org/10.1145/3484491.
Повний текст джерелаWang, Xiaosen, Yichen Yang, Yihe Deng, and Kun He. "Adversarial Training with Fast Gradient Projection Method against Synonym Substitution Based Text Attacks." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 16 (May 18, 2021): 13997–4005. http://dx.doi.org/10.1609/aaai.v35i16.17648.
Повний текст джерелаZou, Junhua, Yexin Duan, Boyu Li, Wu Zhang, Yu Pan, and Zhisong Pan. "Making Adversarial Examples More Transferable and Indistinguishable." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 3 (June 28, 2022): 3662–70. http://dx.doi.org/10.1609/aaai.v36i3.20279.
Повний текст джерелаFang, Yong, Cheng Huang, Yijia Xu, and Yang Li. "RLXSS: Optimizing XSS Detection Model to Defend Against Adversarial Attacks Based on Reinforcement Learning." Future Internet 11, no. 8 (August 14, 2019): 177. http://dx.doi.org/10.3390/fi11080177.
Повний текст джерелаLal, Sheeba, Saeed Ur Rehman, Jamal Hussain Shah, Talha Meraj, Hafiz Tayyab Rauf, Robertas Damaševičius, Mazin Abed Mohammed, and Karrar Hameed Abdulkareem. "Adversarial Attack and Defence through Adversarial Training and Feature Fusion for Diabetic Retinopathy Recognition." Sensors 21, no. 11 (June 7, 2021): 3922. http://dx.doi.org/10.3390/s21113922.
Повний текст джерелаButts, Jonathan, Mason Rice, and Sujeet Shenoi. "An Adversarial Model for Expressing Attacks on Control Protocols." Journal of Defense Modeling and Simulation: Applications, Methodology, Technology 9, no. 3 (July 2012): 243–55. http://dx.doi.org/10.1177/1548512911449409.
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.
Повний текст джерелаSutanto, Richard Evan, and Sukho Lee. "Real-Time Adversarial Attack Detection with Deep Image Prior Initialized as a High-Level Representation Based Blurring Network." Electronics 10, no. 1 (December 30, 2020): 52. http://dx.doi.org/10.3390/electronics10010052.
Повний текст джерелаYin, Heng, Hengwei Zhang, Jindong Wang, and Ruiyu Dou. "Boosting Adversarial Attacks on Neural Networks with Better Optimizer." Security and Communication Networks 2021 (June 7, 2021): 1–9. http://dx.doi.org/10.1155/2021/9983309.
Повний текст джерелаWalton, Claire, Isaac Kaminer, Qi Gong, Abram H. Clark, and Theodoros Tsatsanifos. "Defense against Adversarial Swarms with Parameter Uncertainty." Sensors 22, no. 13 (June 24, 2022): 4773. http://dx.doi.org/10.3390/s22134773.
Повний текст джерелаKong, Zixiao, Jingfeng Xue, Yong Wang, Lu Huang, Zequn Niu, and Feng Li. "A Survey on Adversarial Attack in the Age of Artificial Intelligence." Wireless Communications and Mobile Computing 2021 (June 21, 2021): 1–22. http://dx.doi.org/10.1155/2021/4907754.
Повний текст джерелаGomez-Alanis, Alejandro, Jose A. Gonzalez-Lopez, and Antonio M. Peinado. "GANBA: Generative Adversarial Network for Biometric Anti-Spoofing." Applied Sciences 12, no. 3 (January 29, 2022): 1454. http://dx.doi.org/10.3390/app12031454.
Повний текст джерелаSaha, Aniruddha, Akshayvarun Subramanya, and Hamed Pirsiavash. "Hidden Trigger Backdoor Attacks." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (April 3, 2020): 11957–65. http://dx.doi.org/10.1609/aaai.v34i07.6871.
Повний текст джерелаXue, Wei, Zhiming Chen, Weiwei Tian, Yunhua Wu, and Bing Hua. "A Cascade Defense Method for Multidomain Adversarial Attacks under Remote Sensing Detection." Remote Sensing 14, no. 15 (July 25, 2022): 3559. http://dx.doi.org/10.3390/rs14153559.
Повний текст джерелаZhang, Ziwei, and Dengpan Ye. "Defending against Deep-Learning-Based Flow Correlation Attacks with Adversarial Examples." Security and Communication Networks 2022 (March 27, 2022): 1–11. http://dx.doi.org/10.1155/2022/2962318.
Повний текст джерелаLiu, Ninghao, Mengnan Du, Ruocheng Guo, Huan Liu, and Xia Hu. "Adversarial Attacks and Defenses." ACM SIGKDD Explorations Newsletter 23, no. 1 (May 26, 2021): 86–99. http://dx.doi.org/10.1145/3468507.3468519.
Повний текст джерелаChen, Xiaojiao, Sheng Li, and Hao Huang. "Adversarial Attack and Defense on Deep Neural Network-Based Voice Processing Systems: An Overview." Applied Sciences 11, no. 18 (September 12, 2021): 8450. http://dx.doi.org/10.3390/app11188450.
Повний текст джерелаOzdayi, Mustafa Safa, Murat Kantarcioglu, and Yulia R. Gel. "Defending against Backdoors in Federated Learning with Robust Learning Rate." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 10 (May 18, 2021): 9268–76. http://dx.doi.org/10.1609/aaai.v35i10.17118.
Повний текст джерелаTaheri, Shayan, Aminollah Khormali, Milad Salem, and Jiann-Shiun Yuan. "Developing a Robust Defensive System against Adversarial Examples Using Generative Adversarial Networks." Big Data and Cognitive Computing 4, no. 2 (May 22, 2020): 11. http://dx.doi.org/10.3390/bdcc4020011.
Повний текст джерелаSong, Qun, Zhenyu Yan, and Rui Tan. "DeepMTD: Moving Target Defense for Deep Visual Sensing against Adversarial Examples." ACM Transactions on Sensor Networks 18, no. 1 (February 28, 2022): 1–32. http://dx.doi.org/10.1145/3469032.
Повний текст джерелаLiu, Shuqi, Mingwen Shao, and Xinping Liu. "GAN-based classifier protection against adversarial attacks." Journal of Intelligent & Fuzzy Systems 39, no. 5 (November 19, 2020): 7085–95. http://dx.doi.org/10.3233/jifs-200280.
Повний текст джерелаWang, Fangwei, Yuanyuan Lu, Changguang Wang, and Qingru Li. "Binary Black-Box Adversarial Attacks with Evolutionary Learning against IoT Malware Detection." Wireless Communications and Mobile Computing 2021 (August 30, 2021): 1–9. http://dx.doi.org/10.1155/2021/8736946.
Повний текст джерелаMao, Xiaofeng, Yuefeng Chen, Shuhui Wang, Hang Su, Yuan He, and Hui Xue. "Composite Adversarial Attacks." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 10 (May 18, 2021): 8884–92. http://dx.doi.org/10.1609/aaai.v35i10.17075.
Повний текст джерелаRasheed, Bader, Adil Khan, Muhammad Ahmad, Manuel Mazzara, and S. M. Ahsan Kazmi. "Multiple Adversarial Domains Adaptation Approach for Mitigating Adversarial Attacks Effects." International Transactions on Electrical Energy Systems 2022 (October 10, 2022): 1–11. http://dx.doi.org/10.1155/2022/2890761.
Повний текст джерелаLee , Jungeun, and Hoeseok Yang . "Performance Improvement of Image-Reconstruction-Based Defense against Adversarial Attack." Electronics 11, no. 15 (July 28, 2022): 2372. http://dx.doi.org/10.3390/electronics11152372.
Повний текст джерелаHaq, Ijaz Ul, Zahid Younas Khan, Arshad Ahmad, Bashir Hayat, Asif Khan, Ye-Eun Lee, and Ki-Il Kim. "Evaluating and Enhancing the Robustness of Sustainable Neural Relationship Classifiers Using Query-Efficient Black-Box Adversarial Attacks." Sustainability 13, no. 11 (May 24, 2021): 5892. http://dx.doi.org/10.3390/su13115892.
Повний текст джерелаZhao, Bingyin, and Yingjie Lao. "CLPA: Clean-Label Poisoning Availability Attacks Using Generative Adversarial Nets." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 8 (June 28, 2022): 9162–70. http://dx.doi.org/10.1609/aaai.v36i8.20902.
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 джерелаLi, Yaxin, Wei Jin, Han Xu, and Jiliang Tang. "DeepRobust: a Platform for Adversarial Attacks and Defenses." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 18 (May 18, 2021): 16078–80. http://dx.doi.org/10.1609/aaai.v35i18.18017.
Повний текст джерелаZhao, Jinxiong, Xun Zhang, Fuqiang Di, Sensen Guo, Xiaoyu Li, Xiao Jing, Panfei Huang, and Dejun Mu. "Exploring the Optimum Proactive Defense Strategy for the Power Systems from an Attack Perspective." Security and Communication Networks 2021 (February 12, 2021): 1–14. http://dx.doi.org/10.1155/2021/6699108.
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