Artículos de revistas sobre el tema "Black-box attack"
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Chen, Jinghui, Dongruo Zhou, Jinfeng Yi y Quanquan Gu. "A Frank-Wolfe Framework for Efficient and Effective Adversarial Attacks". Proceedings of the AAAI Conference on Artificial Intelligence 34, n.º 04 (3 de abril de 2020): 3486–94. http://dx.doi.org/10.1609/aaai.v34i04.5753.
Texto completoJiang, Yi y Dengpan Ye. "Black-Box Adversarial Attacks against Audio Forensics Models". Security and Communication Networks 2022 (17 de enero de 2022): 1–8. http://dx.doi.org/10.1155/2022/6410478.
Texto completoPark, Hosung, Gwonsang Ryu y Daeseon Choi. "Partial Retraining Substitute Model for Query-Limited Black-Box Attacks". Applied Sciences 10, n.º 20 (14 de octubre de 2020): 7168. http://dx.doi.org/10.3390/app10207168.
Texto completoZhao, Pu, Pin-yu Chen, Siyue Wang y Xue Lin. "Towards Query-Efficient Black-Box Adversary with Zeroth-Order Natural Gradient Descent". Proceedings of the AAAI Conference on Artificial Intelligence 34, n.º 04 (3 de abril de 2020): 6909–16. http://dx.doi.org/10.1609/aaai.v34i04.6173.
Texto completoDuan, Mingxing, Kenli Li, Jiayan Deng, Bin Xiao y Qi Tian. "A Novel Multi-Sample Generation Method for Adversarial Attacks". ACM Transactions on Multimedia Computing, Communications, and Applications 18, n.º 4 (30 de noviembre de 2022): 1–21. http://dx.doi.org/10.1145/3506852.
Texto completoChen, Zhiyu, Jianyu Ding, Fei Wu, Chi Zhang, Yiming Sun, Jing Sun, Shangdong Liu y Yimu Ji. "An Optimized Black-Box Adversarial Simulator Attack Based on Meta-Learning". Entropy 24, n.º 10 (27 de septiembre de 2022): 1377. http://dx.doi.org/10.3390/e24101377.
Texto completoXiang, Fengtao, Jiahui Xu, Wanpeng Zhang y Weidong Wang. "A Distributed Biased Boundary Attack Method in Black-Box Attack". Applied Sciences 11, n.º 21 (8 de noviembre de 2021): 10479. http://dx.doi.org/10.3390/app112110479.
Texto completoWang, Qiuhua, Hui Yang, Guohua Wu, Kim-Kwang Raymond Choo, Zheng Zhang, Gongxun Miao y Yizhi Ren. "Black-box adversarial attacks on XSS attack detection model". Computers & Security 113 (febrero de 2022): 102554. http://dx.doi.org/10.1016/j.cose.2021.102554.
Texto completoWang, Lu, Huan Zhang, Jinfeng Yi, Cho-Jui Hsieh y Yuan Jiang. "Spanning attack: reinforce black-box attacks with unlabeled data". Machine Learning 109, n.º 12 (29 de octubre de 2020): 2349–68. http://dx.doi.org/10.1007/s10994-020-05916-1.
Texto completoGao, Xianfeng, Yu-an Tan, Hongwei Jiang, Quanxin Zhang y Xiaohui Kuang. "Boosting Targeted Black-Box Attacks via Ensemble Substitute Training and Linear Augmentation". Applied Sciences 9, n.º 11 (3 de junio de 2019): 2286. http://dx.doi.org/10.3390/app9112286.
Texto completoDionysiou, Antreas, Vassilis Vassiliades y Elias Athanasopoulos. "Exploring Model Inversion Attacks in the Black-box Setting". Proceedings on Privacy Enhancing Technologies 2023, n.º 1 (enero de 2023): 190–206. http://dx.doi.org/10.56553/popets-2023-0012.
Texto completoKhalel Ibrahem Al-Ubaidy, Mahmood. "BLACK-BOX ATTACK USING NEURO-IDENTIFIER". Cryptologia 28, n.º 4 (octubre de 2004): 358–72. http://dx.doi.org/10.1080/0161-110491892980.
Texto completoTu, Chun-Chen, Paishun Ting, Pin-Yu Chen, Sijia Liu, Huan Zhang, Jinfeng Yi, Cho-Jui Hsieh y Shin-Ming Cheng. "AutoZOOM: Autoencoder-Based Zeroth Order Optimization Method for Attacking Black-Box Neural Networks". Proceedings of the AAAI Conference on Artificial Intelligence 33 (17 de julio de 2019): 742–49. http://dx.doi.org/10.1609/aaai.v33i01.3301742.
Texto completoXu, Jiarong, Yizhou Sun, Xin Jiang, Yanhao Wang, Chunping Wang, Jiangang Lu y Yang Yang. "Blindfolded Attackers Still Threatening: Strict Black-Box Adversarial Attacks on Graphs". Proceedings of the AAAI Conference on Artificial Intelligence 36, n.º 4 (28 de junio de 2022): 4299–307. http://dx.doi.org/10.1609/aaai.v36i4.20350.
Texto completoYu, Mengran y Shiliang Sun. "Natural Black-Box Adversarial Examples against Deep Reinforcement Learning". Proceedings of the AAAI Conference on Artificial Intelligence 36, n.º 8 (28 de junio de 2022): 8936–44. http://dx.doi.org/10.1609/aaai.v36i8.20876.
Texto completoKong, Zixiao, Jingfeng Xue, Zhenyan Liu, Yong Wang y Weijie Han. "MalDBA: Detection for Query-Based Malware Black-Box Adversarial Attacks". Electronics 12, n.º 7 (6 de abril de 2023): 1751. http://dx.doi.org/10.3390/electronics12071751.
Texto completoChe, Zhaohui, Ali Borji, Guangtao Zhai, Suiyi Ling, Jing Li y Patrick Le Callet. "A New Ensemble Adversarial Attack Powered by Long-Term Gradient Memories". Proceedings of the AAAI Conference on Artificial Intelligence 34, n.º 04 (3 de abril de 2020): 3405–13. http://dx.doi.org/10.1609/aaai.v34i04.5743.
Texto completoWei, Zhipeng, Jingjing Chen, Zuxuan Wu y Yu-Gang Jiang. "Boosting the Transferability of Video Adversarial Examples via Temporal Translation". Proceedings of the AAAI Conference on Artificial Intelligence 36, n.º 3 (28 de junio de 2022): 2659–67. http://dx.doi.org/10.1609/aaai.v36i3.20168.
Texto completoZhang, Qikun, Yuzhi Zhang, Yanling Shao, Mengqi Liu, Jianyong Li, Junling Yuan y Ruifang Wang. "Boosting Adversarial Attacks with Nadam Optimizer". Electronics 12, n.º 6 (20 de marzo de 2023): 1464. http://dx.doi.org/10.3390/electronics12061464.
Texto completoKoga, Kazuki y Kazuhiro Takemoto. "Simple Black-Box Universal Adversarial Attacks on Deep Neural Networks for Medical Image Classification". Algorithms 15, n.º 5 (22 de abril de 2022): 144. http://dx.doi.org/10.3390/a15050144.
Texto completoDu, Xiaohu, Jie Yu, Zibo Yi, Shasha Li, Jun Ma, Yusong Tan y Qinbo Wu. "A Hybrid Adversarial Attack for Different Application Scenarios". Applied Sciences 10, n.º 10 (21 de mayo de 2020): 3559. http://dx.doi.org/10.3390/app10103559.
Texto completoChitic, Raluca, Ali Osman Topal y Franck Leprévost. "Empirical Perturbation Analysis of Two Adversarial Attacks: Black Box versus White Box". Applied Sciences 12, n.º 14 (21 de julio de 2022): 7339. http://dx.doi.org/10.3390/app12147339.
Texto completoDing, Daizong, Mi Zhang, Fuli Feng, Yuanmin Huang, Erling Jiang y Min Yang. "Black-Box Adversarial Attack on Time Series Classification". Proceedings of the AAAI Conference on Artificial Intelligence 37, n.º 6 (26 de junio de 2023): 7358–68. http://dx.doi.org/10.1609/aaai.v37i6.25896.
Texto completoPark, Sanglee y Jungmin So. "On the Effectiveness of Adversarial Training in Defending against Adversarial Example Attacks for Image Classification". Applied Sciences 10, n.º 22 (14 de noviembre de 2020): 8079. http://dx.doi.org/10.3390/app10228079.
Texto completoWei, Zhipeng, Jingjing Chen, Xingxing Wei, Linxi Jiang, Tat-Seng Chua, Fengfeng Zhou y Yu-Gang Jiang. "Heuristic Black-Box Adversarial Attacks on Video Recognition Models". Proceedings of the AAAI Conference on Artificial Intelligence 34, n.º 07 (3 de abril de 2020): 12338–45. http://dx.doi.org/10.1609/aaai.v34i07.6918.
Texto completoFu, Zhongwang y Xiaohui Cui. "ELAA: An Ensemble-Learning-Based Adversarial Attack Targeting Image-Classification Model". Entropy 25, n.º 2 (22 de enero de 2023): 215. http://dx.doi.org/10.3390/e25020215.
Texto completoLapid, Raz, Zvika Haramaty y Moshe Sipper. "An Evolutionary, Gradient-Free, Query-Efficient, Black-Box Algorithm for Generating Adversarial Instances in Deep Convolutional Neural Networks". Algorithms 15, n.º 11 (31 de octubre de 2022): 407. http://dx.doi.org/10.3390/a15110407.
Texto completoGuan, Yuting, Junjiang He, Tao Li, Hui Zhao y Baoqiang Ma. "SSQLi: A Black-Box Adversarial Attack Method for SQL Injection Based on Reinforcement Learning". Future Internet 15, n.º 4 (30 de marzo de 2023): 133. http://dx.doi.org/10.3390/fi15040133.
Texto completoChang, Heng, Yu Rong, Tingyang Xu, Wenbing Huang, Honglei Zhang, Peng Cui, Wenwu Zhu y Junzhou Huang. "A Restricted Black-Box Adversarial Framework Towards Attacking Graph Embedding Models". Proceedings of the AAAI Conference on Artificial Intelligence 34, n.º 04 (3 de abril de 2020): 3389–96. http://dx.doi.org/10.1609/aaai.v34i04.5741.
Texto completoYang, Bo, Kaiyong Xu, Hengjun Wang y Hengwei Zhang. "Random Transformation of image brightness for adversarial attack". Journal of Intelligent & Fuzzy Systems 42, n.º 3 (2 de febrero de 2022): 1693–704. http://dx.doi.org/10.3233/jifs-211157.
Texto completoSuryanto, Naufal, Hyoeun Kang, Yongsu Kim, Youngyeo Yun, Harashta Tatimma Larasati y Howon Kim. "A Distributed Black-Box Adversarial Attack Based on Multi-Group Particle Swarm Optimization". Sensors 20, n.º 24 (14 de diciembre de 2020): 7158. http://dx.doi.org/10.3390/s20247158.
Texto completoMaheshwary, Rishabh, Saket Maheshwary y Vikram Pudi. "Generating Natural Language Attacks in a Hard Label Black Box Setting". Proceedings of the AAAI Conference on Artificial Intelligence 35, n.º 15 (18 de mayo de 2021): 13525–33. http://dx.doi.org/10.1609/aaai.v35i15.17595.
Texto completoChen, Yiding y Xiaojin Zhu. "Optimal Attack against Autoregressive Models by Manipulating the Environment". Proceedings of the AAAI Conference on Artificial Intelligence 34, n.º 04 (3 de abril de 2020): 3545–52. http://dx.doi.org/10.1609/aaai.v34i04.5760.
Texto completoCao, Han, Chengxiang Si, Qindong Sun, Yanxiao Liu, Shancang Li y Prosanta Gope. "ABCAttack: A Gradient-Free Optimization Black-Box Attack for Fooling Deep Image Classifiers". Entropy 24, n.º 3 (15 de marzo de 2022): 412. http://dx.doi.org/10.3390/e24030412.
Texto completoLin, Bin, Jixin Chen, Zhihong Zhang, Yanlin Lai, Xinlong Wu, Lulu Tian y Wangchi Cheng. "An Adversarial Network-based Multi-model Black-box Attack". Intelligent Automation & Soft Computing 29, n.º 3 (2021): 641–49. http://dx.doi.org/10.32604/iasc.2021.016818.
Texto completoMun, Hyunjun, Sunggwan Seo, Baehoon Son y Joobeom Yun. "Black-Box Audio Adversarial Attack Using Particle Swarm Optimization". IEEE Access 10 (2022): 23532–44. http://dx.doi.org/10.1109/access.2022.3152526.
Texto completoHaq, Ijaz Ul, Zahid Younas Khan, Arshad Ahmad, Bashir Hayat, Asif Khan, Ye-Eun Lee y Ki-Il Kim. "Evaluating and Enhancing the Robustness of Sustainable Neural Relationship Classifiers Using Query-Efficient Black-Box Adversarial Attacks". Sustainability 13, n.º 11 (24 de mayo de 2021): 5892. http://dx.doi.org/10.3390/su13115892.
Texto completoCroce, Francesco, Maksym Andriushchenko, Naman D. Singh, Nicolas Flammarion y Matthias Hein. "Sparse-RS: A Versatile Framework for Query-Efficient Sparse Black-Box Adversarial Attacks". Proceedings of the AAAI Conference on Artificial Intelligence 36, n.º 6 (28 de junio de 2022): 6437–45. http://dx.doi.org/10.1609/aaai.v36i6.20595.
Texto completoWang, Fangwei, Yuanyuan Lu, Changguang Wang y Qingru Li. "Binary Black-Box Adversarial Attacks with Evolutionary Learning against IoT Malware Detection". Wireless Communications and Mobile Computing 2021 (30 de agosto de 2021): 1–9. http://dx.doi.org/10.1155/2021/8736946.
Texto completoFang, Yong, Cheng Huang, Yijia Xu y Yang Li. "RLXSS: Optimizing XSS Detection Model to Defend Against Adversarial Attacks Based on Reinforcement Learning". Future Internet 11, n.º 8 (14 de agosto de 2019): 177. http://dx.doi.org/10.3390/fi11080177.
Texto completoDu, Meng, Yuxin Sun, Bing Sun, Zilong Wu, Lan Luo, Daping Bi y Mingyang Du. "TAN: A Transferable Adversarial Network for DNN-Based UAV SAR Automatic Target Recognition Models". Drones 7, n.º 3 (16 de marzo de 2023): 205. http://dx.doi.org/10.3390/drones7030205.
Texto completoWang, Fangwei, Zerou Ma, Xiaohan Zhang, Qingru Li y Changguang Wang. "DDSG-GAN: Generative Adversarial Network with Dual Discriminators and Single Generator for Black-Box Attacks". Mathematics 11, n.º 4 (16 de febrero de 2023): 1016. http://dx.doi.org/10.3390/math11041016.
Texto completoSauka, Kudzai, Gun-Yoo Shin, Dong-Wook Kim y Myung-Mook Han. "Adversarial Robust and Explainable Network Intrusion Detection Systems Based on Deep Learning". Applied Sciences 12, n.º 13 (25 de junio de 2022): 6451. http://dx.doi.org/10.3390/app12136451.
Texto completoIlham Firdaus, Januar Al Amien y Soni Soni. "String Matching untuk Mendeteksi Serangan Sniffing (ARP Spoofing) pada IDS Snort". Jurnal CoSciTech (Computer Science and Information Technology) 1, n.º 2 (31 de octubre de 2020): 44–49. http://dx.doi.org/10.37859/coscitech.v1i2.2180.
Texto completoZhang, Yue, Seong-Yoon Shin, Xujie Tan y Bin Xiong. "A Self-Adaptive Approximated-Gradient-Simulation Method for Black-Box Adversarial Sample Generation". Applied Sciences 13, n.º 3 (18 de enero de 2023): 1298. http://dx.doi.org/10.3390/app13031298.
Texto completoBai, Yang, Yisen Wang, Yuyuan Zeng, Yong Jiang y Shu-Tao Xia. "Query efficient black-box adversarial attack on deep neural networks". Pattern Recognition 133 (enero de 2023): 109037. http://dx.doi.org/10.1016/j.patcog.2022.109037.
Texto completoLi, Siyuan, Guangji Huang, Xing Xu y Huimin Lu. "Query-based black-box attack against medical image segmentation model". Future Generation Computer Systems 133 (agosto de 2022): 331–37. http://dx.doi.org/10.1016/j.future.2022.03.008.
Texto completoDing, Kangyi, Xiaolei Liu, Weina Niu, Teng Hu, Yanping Wang y Xiaosong Zhang. "A low-query black-box adversarial attack based on transferability". Knowledge-Based Systems 226 (agosto de 2021): 107102. http://dx.doi.org/10.1016/j.knosys.2021.107102.
Texto completoZhu, Jiaqi, Feng Dai, Lingyun Yu, Hongtao Xie, Lidong Wang, Bo Wu y Yongdong Zhang. "Attention‐guided transformation‐invariant attack for black‐box adversarial examples". International Journal of Intelligent Systems 37, n.º 5 (11 de enero de 2022): 3142–65. http://dx.doi.org/10.1002/int.22808.
Texto completoWang, Yajie, Yu-an Tan, Wenjiao Zhang, Yuhang Zhao y Xiaohui Kuang. "An adversarial attack on DNN-based black-box object detectors". Journal of Network and Computer Applications 161 (julio de 2020): 102634. http://dx.doi.org/10.1016/j.jnca.2020.102634.
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