Статті в журналах з теми "Fast Gradient Sign Method"
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Zou, 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 джерелаWibawa, Sigit. "Analysis of Adversarial Attacks on AI-based With Fast Gradient Sign Method." International Journal of Engineering Continuity 2, no. 2 (August 1, 2023): 72–79. http://dx.doi.org/10.58291/ijec.v2i2.120.
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 джерелаKim, Hoki, Woojin Lee, and Jaewook Lee. "Understanding Catastrophic Overfitting in Single-step Adversarial Training." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 9 (May 18, 2021): 8119–27. http://dx.doi.org/10.1609/aaai.v35i9.16989.
Повний текст джерелаSaxena, Rishabh, Amit Sanjay Adate, and Don Sasikumar. "A Comparative Study on Adversarial Noise Generation for Single Image Classification." International Journal of Intelligent Information Technologies 16, no. 1 (January 2020): 75–87. http://dx.doi.org/10.4018/ijiit.2020010105.
Повний текст джерелаYang, Bo, Kaiyong Xu, Hengjun Wang, and Hengwei Zhang. "Random Transformation of image brightness for adversarial attack." Journal of Intelligent & Fuzzy Systems 42, no. 3 (February 2, 2022): 1693–704. http://dx.doi.org/10.3233/jifs-211157.
Повний текст джерелаTrinh Quang Kien. "Improving the robustness of binarized neural network using the EFAT method." Journal of Military Science and Technology, CSCE5 (December 15, 2021): 14–23. http://dx.doi.org/10.54939/1859-1043.j.mst.csce5.2021.14-23.
Повний текст джерелаHirano, Hokuto, and Kazuhiro Takemoto. "Simple Iterative Method for Generating Targeted Universal Adversarial Perturbations." Algorithms 13, no. 11 (October 22, 2020): 268. http://dx.doi.org/10.3390/a13110268.
Повний текст джерелаAn, Tong, Tao Zhang, Yanzhang Geng, and Haiquan Jiao. "Normalized Combinations of Proportionate Affine Projection Sign Subband Adaptive Filter." Scientific Programming 2021 (August 26, 2021): 1–12. http://dx.doi.org/10.1155/2021/8826868.
Повний текст джерелаKadhim, Ansam, and Salah Al-Darraji. "Face Recognition System Against Adversarial Attack Using Convolutional Neural Network." Iraqi Journal for Electrical and Electronic Engineering 18, no. 1 (November 6, 2021): 1–8. http://dx.doi.org/10.37917/ijeee.18.1.1.
Повний текст джерелаZhang, Qikun, Yuzhi Zhang, Yanling Shao, Mengqi Liu, Jianyong Li, Junling Yuan, and Ruifang Wang. "Boosting Adversarial Attacks with Nadam Optimizer." Electronics 12, no. 6 (March 20, 2023): 1464. http://dx.doi.org/10.3390/electronics12061464.
Повний текст джерелаPal, Biprodip, Debashis Gupta, Md Rashed-Al-Mahfuz, Salem A. Alyami, and Mohammad Ali Moni. "Vulnerability in Deep Transfer Learning Models to Adversarial Fast Gradient Sign Attack for COVID-19 Prediction from Chest Radiography Images." Applied Sciences 11, no. 9 (May 7, 2021): 4233. http://dx.doi.org/10.3390/app11094233.
Повний текст джерелаZhao, Weimin, Sanaa Alwidian, and Qusay H. Mahmoud. "Adversarial Training Methods for Deep Learning: A Systematic Review." Algorithms 15, no. 8 (August 12, 2022): 283. http://dx.doi.org/10.3390/a15080283.
Повний текст джерелаZhang, Xingyu, Xiongwei Zhang, Xia Zou, Haibo Liu, and Meng Sun. "Towards Generating Adversarial Examples on Combined Systems of Automatic Speaker Verification and Spoofing Countermeasure." Security and Communication Networks 2022 (July 31, 2022): 1–12. http://dx.doi.org/10.1155/2022/2666534.
Повний текст джерелаRudd-Orthner, Richard N. M., and Lyudmila Mihaylova. "Deep ConvNet: Non-Random Weight Initialization for Repeatable Determinism, Examined with FSGM." Sensors 21, no. 14 (July 13, 2021): 4772. http://dx.doi.org/10.3390/s21144772.
Повний текст джерелаGuan, Dejian, and Wentao Zhao . "Adversarial Detection Based on Inner-Class Adjusted Cosine Similarity." Applied Sciences 12, no. 19 (September 20, 2022): 9406. http://dx.doi.org/10.3390/app12199406.
Повний текст джерелаA, Jayaprakash, and C. Kezi Selva Vijila. "Detection and Recognition of Traffic Sign using FCM with SVM." JOURNAL OF ADVANCES IN CHEMISTRY 13, no. 6 (February 25, 2017): 6285–89. http://dx.doi.org/10.24297/jac.v13i6.5773.
Повний текст джерелаZhu, Min-Ling, Liang-Liang Zhao, and Li Xiao. "Image Denoising Based on GAN with Optimization Algorithm." Electronics 11, no. 15 (August 5, 2022): 2445. http://dx.doi.org/10.3390/electronics11152445.
Повний текст джерелаXu, Wei, and Veerawat Sirivesmas. "Study on Network Virtual Printing Sculpture Design using Artificial Intelligence." International Journal of Communication Networks and Information Security (IJCNIS) 15, no. 1 (May 30, 2023): 132–45. http://dx.doi.org/10.17762/ijcnis.v15i1.5694.
Повний текст джерелаKurniawan S, Putu Widiarsa, Yosi Kristian, and Joan Santoso. "Pemanfaatan Deep Convulutional Auto-encoder untuk Mitigasi Serangan Adversarial Attack pada Citra Digital." J-INTECH 11, no. 1 (July 4, 2023): 50–59. http://dx.doi.org/10.32664/j-intech.v11i1.845.
Повний текст джерелаYang, Zhongguo, Irshad Ahmed Abbasi, Fahad Algarni, Sikandar Ali, and Mingzhu Zhang. "An IoT Time Series Data Security Model for Adversarial Attack Based on Thermometer Encoding." Security and Communication Networks 2021 (March 9, 2021): 1–11. http://dx.doi.org/10.1155/2021/5537041.
Повний текст джерелаPapadopoulos, Pavlos, Oliver Thornewill von Essen, Nikolaos Pitropakis, Christos Chrysoulas, Alexios Mylonas, and William J. Buchanan. "Launching Adversarial Attacks against Network Intrusion Detection Systems for IoT." Journal of Cybersecurity and Privacy 1, no. 2 (April 23, 2021): 252–73. http://dx.doi.org/10.3390/jcp1020014.
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 джерелаWu, Fei, Wenxue Yang, Limin Xiao, and Jinbin Zhu. "Adaptive Wiener Filter and Natural Noise to Eliminate Adversarial Perturbation." Electronics 9, no. 10 (October 3, 2020): 1634. http://dx.doi.org/10.3390/electronics9101634.
Повний текст джерелаSantana, Everton Jose, Ricardo Petri Silva, Bruno Bogaz Zarpelão, and Sylvio Barbon Junior. "Detecting and Mitigating Adversarial Examples in Regression Tasks: A Photovoltaic Power Generation Forecasting Case Study." Information 12, no. 10 (September 26, 2021): 394. http://dx.doi.org/10.3390/info12100394.
Повний текст джерелаKwon, Hyun. "MedicalGuard: U-Net Model Robust against Adversarially Perturbed Images." Security and Communication Networks 2021 (August 9, 2021): 1–8. http://dx.doi.org/10.1155/2021/5595026.
Повний текст джерелаLi, Xinyu, Shaogang Dai, and Zhijin Zhao. "Unsupervised Learning-Based Spectrum Sensing Algorithm with Defending Adversarial Attacks." Applied Sciences 13, no. 16 (August 9, 2023): 9101. http://dx.doi.org/10.3390/app13169101.
Повний текст джерелаPantiukhin, D. V. "Educational and methodological materials of the master class “Adversarial attacks on image recognition neural networks” for students and schoolchildren." Informatics and education 38, no. 1 (April 16, 2023): 55–63. http://dx.doi.org/10.32517/0234-0453-2023-38-1-55-63.
Повний текст джерелаLung, Rodica Ioana. "A game theoretic decision-making approach for fast gradient sign attacks." Procedia Computer Science 220 (2023): 1015–20. http://dx.doi.org/10.1016/j.procs.2023.03.141.
Повний текст джерелаPerne, Matija, Samo Gerkšič, and Boštjan Pregelj. "Soft inequality constraints in gradient method and fast gradient method for quadratic programming." Optimization and Engineering 20, no. 3 (December 18, 2018): 749–67. http://dx.doi.org/10.1007/s11081-018-9416-3.
Повний текст джерелаFlorea, Mihai I., and Sergiy A. Vorobyov. "A Generalized Accelerated Composite Gradient Method: Uniting Nesterov's Fast Gradient Method and FISTA." IEEE Transactions on Signal Processing 68 (2020): 3033–48. http://dx.doi.org/10.1109/tsp.2020.2988614.
Повний текст джерелаYao, Q., B. Tan, and Y. Huang. "FAST DRAWING OF TRAFFIC SIGN USING MOBILE MAPPING SYSTEM." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B3 (June 10, 2016): 937–44. http://dx.doi.org/10.5194/isprs-archives-xli-b3-937-2016.
Повний текст джерелаYao, Q., B. Tan, and Y. Huang. "FAST DRAWING OF TRAFFIC SIGN USING MOBILE MAPPING SYSTEM." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B3 (June 10, 2016): 937–44. http://dx.doi.org/10.5194/isprsarchives-xli-b3-937-2016.
Повний текст джерелаKenshimov, Chingiz, Zholdas Buribayev, Yedilkhan Amirgaliyev, Aisulyu Ataniyazova, and Askhat Aitimov. "Sign language dactyl recognition based on machine learning algorithms." Eastern-European Journal of Enterprise Technologies 4, no. 2(112) (August 31, 2021): 58–72. http://dx.doi.org/10.15587/1729-4061.2021.239253.
Повний текст джерелаGuo, Shaocui, and Xu Yang. "Fast recognition algorithm for static traffic sign information." Open Physics 16, no. 1 (December 31, 2018): 1149–56. http://dx.doi.org/10.1515/phys-2018-0135.
Повний текст джерелаTyurin, Alexander Igorevich. "Primal-dual fast gradient method with a model." Computer Research and Modeling 12, no. 2 (April 2020): 263–74. http://dx.doi.org/10.20537/2076-7633-2020-12-2-263-274.
Повний текст джерелаBloom, Veronica, Igor Griva, and Fabio Quijada. "Fast projected gradient method for support vector machines." Optimization and Engineering 17, no. 4 (August 11, 2016): 651–62. http://dx.doi.org/10.1007/s11081-016-9328-z.
Повний текст джерелаPolyak, Roman A., James Costa, and Saba Neyshabouri. "Dual fast projected gradient method for quadratic programming." Optimization Letters 7, no. 4 (April 21, 2012): 631–45. http://dx.doi.org/10.1007/s11590-012-0476-6.
Повний текст джерелаIyengar, Garud, and Alfred Ka Chun Ma. "Fast gradient descent method for Mean-CVaR optimization." Annals of Operations Research 205, no. 1 (February 7, 2013): 203–12. http://dx.doi.org/10.1007/s10479-012-1245-8.
Повний текст джерелаHan, Fang Fang, and Guo Qiang Xu. "Robust Memory Gradient Blind Equalization Algorithm Based on Error Sign Decision." Applied Mechanics and Materials 347-350 (August 2013): 1997–2000. http://dx.doi.org/10.4028/www.scientific.net/amm.347-350.1997.
Повний текст джерелаNishimura, Jun, and Shinji Shimasaki. "Unification of the complex Langevin method and the Lefschetzthimble method." EPJ Web of Conferences 175 (2018): 07018. http://dx.doi.org/10.1051/epjconf/201817507018.
Повний текст джерелаAnescu, George. "A Heuristic Fast Gradient Descent Method for Unimodal Optimization." Journal of Advances in Mathematics and Computer Science 26, no. 5 (February 28, 2018): 1–20. http://dx.doi.org/10.9734/jamcs/2018/39798.
Повний текст джерелаA.M, Raid, Khedr W.M, El-dosuky M.A, and Mona Aoud. "Fast NAS-RIF Algorithm Using Iterative Conjugate Gradient Method." Signal & Image Processing : An International Journal 5, no. 2 (April 30, 2014): 63–72. http://dx.doi.org/10.5121/sipij.2014.5206.
Повний текст джерелаKögel, Markus, and Rolf Findeisen. "A Fast Gradient method for embedded linear predictive control." IFAC Proceedings Volumes 44, no. 1 (January 2011): 1362–67. http://dx.doi.org/10.3182/20110828-6-it-1002.03322.
Повний текст джерелаZhang, Yue, Seong-Yoon Shin, Xujie Tan, and Bin Xiong. "A Self-Adaptive Approximated-Gradient-Simulation Method for Black-Box Adversarial Sample Generation." Applied Sciences 13, no. 3 (January 18, 2023): 1298. http://dx.doi.org/10.3390/app13031298.
Повний текст джерелаOgal’tsov, A. V., and A. I. Tyurin. "A Heuristic Adaptive Fast Gradient Method in Stochastic Optimization Problems." Computational Mathematics and Mathematical Physics 60, no. 7 (July 2020): 1108–15. http://dx.doi.org/10.1134/s0965542520070088.
Повний текст джерелаRen, Dongwei, Wangmeng Zuo, Xiaofei Zhao, Zhouchen Lin, and David Zhang. "Fast gradient vector flow computation based on augmented Lagrangian method." Pattern Recognition Letters 34, no. 2 (January 2013): 219–25. http://dx.doi.org/10.1016/j.patrec.2012.09.017.
Повний текст джерелаJirigalatu, Jörg, and Ebbing. "A fast equivalent source method for airborne gravity gradient data." GEOPHYSICS 84, no. 5 (September 1, 2019): G75—G82. http://dx.doi.org/10.1190/geo2018-0366.1.
Повний текст джерелаJin-bo, Zhang, Xu Jing-wen, and Li Yuan-xiang. "Gradient Gene Algorithm: a fast optimization method to MST problem." Wuhan University Journal of Natural Sciences 6, no. 1-2 (March 2001): 535–40. http://dx.doi.org/10.1007/bf03160298.
Повний текст джерелаChoi, Young-Jae, and In-Sik Choi. "A novel fast clean algorithm using the gradient descent method." Microwave and Optical Technology Letters 59, no. 5 (March 27, 2017): 1018–22. http://dx.doi.org/10.1002/mop.30448.
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