Zeitschriftenartikel zum Thema „Fast Gradient Sign Method (FGSM)“
Geben Sie eine Quelle nach APA, MLA, Chicago, Harvard und anderen Zitierweisen an
Machen Sie sich mit Top-50 Zeitschriftenartikel für die Forschung zum Thema "Fast Gradient Sign Method (FGSM)" bekannt.
Neben jedem Werk im Literaturverzeichnis ist die Option "Zur Bibliographie hinzufügen" verfügbar. Nutzen Sie sie, wird Ihre bibliographische Angabe des gewählten Werkes nach der nötigen Zitierweise (APA, MLA, Harvard, Chicago, Vancouver usw.) automatisch gestaltet.
Sie können auch den vollen Text der wissenschaftlichen Publikation im PDF-Format herunterladen und eine Online-Annotation der Arbeit lesen, wenn die relevanten Parameter in den Metadaten verfügbar sind.
Sehen Sie die Zeitschriftenartikel für verschiedene Spezialgebieten durch und erstellen Sie Ihre Bibliographie auf korrekte Weise.
Hong, Dian, Deng Chen, Yanduo Zhang, Huabing Zhou und Liang Xie. „Attacking Robot Vision Models Efficiently Based on Improved Fast Gradient Sign Method“. Applied Sciences 14, Nr. 3 (02.02.2024): 1257. http://dx.doi.org/10.3390/app14031257.
Der volle Inhalt der QuelleLong, Sheng, Wei Tao, Shuohao LI, Jun Lei und Jun Zhang. „On the Convergence of an Adaptive Momentum Method for Adversarial Attacks“. Proceedings of the AAAI Conference on Artificial Intelligence 38, Nr. 13 (24.03.2024): 14132–40. http://dx.doi.org/10.1609/aaai.v38i13.29323.
Der volle Inhalt der QuellePan, Chao, Qing Li und Xin Yao. „Adversarial Initialization with Universal Adversarial Perturbation: A New Approach to Fast Adversarial Training“. Proceedings of the AAAI Conference on Artificial Intelligence 38, Nr. 19 (24.03.2024): 21501–9. http://dx.doi.org/10.1609/aaai.v38i19.30147.
Der volle Inhalt der QuelleWibawa, Sigit. „Analysis of Adversarial Attacks on AI-based With Fast Gradient Sign Method“. International Journal of Engineering Continuity 2, Nr. 2 (01.08.2023): 72–79. http://dx.doi.org/10.58291/ijec.v2i2.120.
Der volle Inhalt der QuelleKadhim, Ansam, und Salah Al-Darraji. „Face Recognition System Against Adversarial Attack Using Convolutional Neural Network“. Iraqi Journal for Electrical and Electronic Engineering 18, Nr. 1 (06.11.2021): 1–8. http://dx.doi.org/10.37917/ijeee.18.1.1.
Der volle Inhalt der QuellePervin, Mst Tasnim, Linmi Tao und Aminul Huq. „Adversarial attack driven data augmentation for medical images“. International Journal of Electrical and Computer Engineering (IJECE) 13, Nr. 6 (01.12.2023): 6285. http://dx.doi.org/10.11591/ijece.v13i6.pp6285-6292.
Der volle Inhalt der QuelleVillegas-Ch, William, Angel Jaramillo-Alcázar und Sergio Luján-Mora. „Evaluating the Robustness of Deep Learning Models against Adversarial Attacks: An Analysis with FGSM, PGD and CW“. Big Data and Cognitive Computing 8, Nr. 1 (16.01.2024): 8. http://dx.doi.org/10.3390/bdcc8010008.
Der volle Inhalt der QuelleKurniawan S, Putu Widiarsa, Yosi Kristian und Joan Santoso. „Pemanfaatan Deep Convulutional Auto-encoder untuk Mitigasi Serangan Adversarial Attack pada Citra Digital“. J-INTECH 11, Nr. 1 (04.07.2023): 50–59. http://dx.doi.org/10.32664/j-intech.v11i1.845.
Der volle Inhalt der QuelleKumari, Rekha, Tushar Bhatia, Peeyush Kumar Singh und Kanishk Vikram Singh. „Dissecting Adversarial Attacks: A Comparative Analysis of Adversarial Perturbation Effects on Pre-Trained Deep Learning Models“. INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, Nr. 11 (01.11.2023): 1–11. http://dx.doi.org/10.55041/ijsrem27337.
Der volle Inhalt der QuellePal, Biprodip, Debashis Gupta, Md Rashed-Al-Mahfuz, Salem A. Alyami und 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, Nr. 9 (07.05.2021): 4233. http://dx.doi.org/10.3390/app11094233.
Der volle Inhalt der QuelleKim, Hoki, Woojin Lee und Jaewook Lee. „Understanding Catastrophic Overfitting in Single-step Adversarial Training“. Proceedings of the AAAI Conference on Artificial Intelligence 35, Nr. 9 (18.05.2021): 8119–27. http://dx.doi.org/10.1609/aaai.v35i9.16989.
Der volle Inhalt der QuelleLu, Fan. „Adversarial attack against deep learning algorithms for gun category detection“. Applied and Computational Engineering 53, Nr. 1 (28.03.2024): 190–96. http://dx.doi.org/10.54254/2755-2721/53/20241368.
Der volle Inhalt der QuelleCui, Chenrui. „Adversarial attack study on VGG16 for cat and dog image classification task“. Applied and Computational Engineering 50, Nr. 1 (25.03.2024): 170–75. http://dx.doi.org/10.54254/2755-2721/50/20241438.
Der volle Inhalt der QuelleMohamed, Mahmoud, und Mohamed Bilal. „Comparing the Performance of Deep Denoising Sparse Autoencoder with Other Defense Methods Against Adversarial Attacks for Arabic letters“. Jordan Journal of Electrical Engineering 10, Nr. 1 (2024): 122. http://dx.doi.org/10.5455/jjee.204-1687363297.
Der volle Inhalt der QuelleNavjot Kaur. „Robustness and Security in Deep Learning: Adversarial Attacks and Countermeasures“. Journal of Electrical Systems 20, Nr. 3s (04.04.2024): 1250–57. http://dx.doi.org/10.52783/jes.1436.
Der volle Inhalt der QuelleZhang, Qikun, Yuzhi Zhang, Yanling Shao, Mengqi Liu, Jianyong Li, Junling Yuan und Ruifang Wang. „Boosting Adversarial Attacks with Nadam Optimizer“. Electronics 12, Nr. 6 (20.03.2023): 1464. http://dx.doi.org/10.3390/electronics12061464.
Der volle Inhalt der QuelleYang, Bo, Kaiyong Xu, Hengjun Wang und Hengwei Zhang. „Random Transformation of image brightness for adversarial attack“. Journal of Intelligent & Fuzzy Systems 42, Nr. 3 (02.02.2022): 1693–704. http://dx.doi.org/10.3233/jifs-211157.
Der volle Inhalt der QuelleVyas, Dhairya, und Viral V. Kapadia. „Designing defensive techniques to handle adversarial attack on deep learning based model“. PeerJ Computer Science 10 (08.03.2024): e1868. http://dx.doi.org/10.7717/peerj-cs.1868.
Der volle Inhalt der QuelleZou, Junhua, Yexin Duan, Boyu Li, Wu Zhang, Yu Pan und Zhisong Pan. „Making Adversarial Examples More Transferable and Indistinguishable“. Proceedings of the AAAI Conference on Artificial Intelligence 36, Nr. 3 (28.06.2022): 3662–70. http://dx.doi.org/10.1609/aaai.v36i3.20279.
Der volle Inhalt der QuelleUtomo, Sapdo, Adarsh Rouniyar, Hsiu-Chun Hsu und Pao-Ann Hsiung. „Federated Adversarial Training Strategies for Achieving Privacy and Security in Sustainable Smart City Applications“. Future Internet 15, Nr. 11 (20.11.2023): 371. http://dx.doi.org/10.3390/fi15110371.
Der volle Inhalt der QuelleHan, Dong, Reza Babaei, Shangqing Zhao und Samuel Cheng. „Exploring the Efficacy of Learning Techniques in Model Extraction Attacks on Image Classifiers: A Comparative Study“. Applied Sciences 14, Nr. 9 (29.04.2024): 3785. http://dx.doi.org/10.3390/app14093785.
Der volle Inhalt der QuelleTrinh Quang Kien. „Improving the robustness of binarized neural network using the EFAT method“. Journal of Military Science and Technology, CSCE5 (15.12.2021): 14–23. http://dx.doi.org/10.54939/1859-1043.j.mst.csce5.2021.14-23.
Der volle Inhalt der QuelleRudd-Orthner, Richard N. M., und Lyudmila Mihaylova. „Deep ConvNet: Non-Random Weight Initialization for Repeatable Determinism, Examined with FSGM“. Sensors 21, Nr. 14 (13.07.2021): 4772. http://dx.doi.org/10.3390/s21144772.
Der volle Inhalt der QuelleXu, Wei, und Veerawat Sirivesmas. „Study on Network Virtual Printing Sculpture Design using Artificial Intelligence“. International Journal of Communication Networks and Information Security (IJCNIS) 15, Nr. 1 (30.05.2023): 132–45. http://dx.doi.org/10.17762/ijcnis.v15i1.5694.
Der volle Inhalt der QuelleGuan, Dejian, und Wentao Zhao . „Adversarial Detection Based on Inner-Class Adjusted Cosine Similarity“. Applied Sciences 12, Nr. 19 (20.09.2022): 9406. http://dx.doi.org/10.3390/app12199406.
Der volle Inhalt der QuelleZhao, Weimin, Sanaa Alwidian und Qusay H. Mahmoud. „Adversarial Training Methods for Deep Learning: A Systematic Review“. Algorithms 15, Nr. 8 (12.08.2022): 283. http://dx.doi.org/10.3390/a15080283.
Der volle Inhalt der QuelleLi, Xinyu, Shaogang Dai und Zhijin Zhao. „Unsupervised Learning-Based Spectrum Sensing Algorithm with Defending Adversarial Attacks“. Applied Sciences 13, Nr. 16 (09.08.2023): 9101. http://dx.doi.org/10.3390/app13169101.
Der volle Inhalt der QuelleZhu, Min-Ling, Liang-Liang Zhao und Li Xiao. „Image Denoising Based on GAN with Optimization Algorithm“. Electronics 11, Nr. 15 (05.08.2022): 2445. http://dx.doi.org/10.3390/electronics11152445.
Der volle Inhalt der QuelleLee , Jungeun, und Hoeseok Yang . „Performance Improvement of Image-Reconstruction-Based Defense against Adversarial Attack“. Electronics 11, Nr. 15 (28.07.2022): 2372. http://dx.doi.org/10.3390/electronics11152372.
Der volle Inhalt der QuelleWu, Fei, Wenxue Yang, Limin Xiao und Jinbin Zhu. „Adaptive Wiener Filter and Natural Noise to Eliminate Adversarial Perturbation“. Electronics 9, Nr. 10 (03.10.2020): 1634. http://dx.doi.org/10.3390/electronics9101634.
Der volle Inhalt der QuelleBhandari, Mohan, Tej Bahadur Shahi und Arjun Neupane. „Evaluating Retinal Disease Diagnosis with an Interpretable Lightweight CNN Model Resistant to Adversarial Attacks“. Journal of Imaging 9, Nr. 10 (11.10.2023): 219. http://dx.doi.org/10.3390/jimaging9100219.
Der volle Inhalt der QuelleSu, Guanpeng. „Analysis of the attack effect of adversarial attacks on machine learning models“. Applied and Computational Engineering 6, Nr. 1 (14.06.2023): 1212–18. http://dx.doi.org/10.54254/2755-2721/6/20230607.
Der volle Inhalt der QuelleHuang, Bowen, Ruoheng Feng und Jiahao Yuan. „Exploiting ensembled neural network model for social platform rumor detection“. Applied and Computational Engineering 20, Nr. 1 (23.10.2023): 231–39. http://dx.doi.org/10.54254/2755-2721/20/20231103.
Der volle Inhalt der QuelleKwon, Hyun. „MedicalGuard: U-Net Model Robust against Adversarially Perturbed Images“. Security and Communication Networks 2021 (09.08.2021): 1–8. http://dx.doi.org/10.1155/2021/5595026.
Der volle Inhalt der QuelleHaroon, Muhammad Shahzad, und Husnain Mansoor Ali. „Ensemble adversarial training based defense against adversarial attacks for machine learning-based intrusion detection system“. Neural Network World 33, Nr. 5 (2023): 317–36. http://dx.doi.org/10.14311/nnw.2023.33.018.
Der volle Inhalt der QuelleShi, Lin, Teyi Liao und Jianfeng He. „Defending Adversarial Attacks against DNN Image Classification Models by a Noise-Fusion Method“. Electronics 11, Nr. 12 (08.06.2022): 1814. http://dx.doi.org/10.3390/electronics11121814.
Der volle Inhalt der QuelleSun, Guangling, Yuying Su, Chuan Qin, Wenbo Xu, Xiaofeng Lu und Andrzej Ceglowski. „Complete Defense Framework to Protect Deep Neural Networks against Adversarial Examples“. Mathematical Problems in Engineering 2020 (11.05.2020): 1–17. http://dx.doi.org/10.1155/2020/8319249.
Der volle Inhalt der QuelleSaxena, Rishabh, Amit Sanjay Adate und Don Sasikumar. „A Comparative Study on Adversarial Noise Generation for Single Image Classification“. International Journal of Intelligent Information Technologies 16, Nr. 1 (Januar 2020): 75–87. http://dx.doi.org/10.4018/ijiit.2020010105.
Der volle Inhalt der QuelleAn, Tong, Tao Zhang, Yanzhang Geng und Haiquan Jiao. „Normalized Combinations of Proportionate Affine Projection Sign Subband Adaptive Filter“. Scientific Programming 2021 (26.08.2021): 1–12. http://dx.doi.org/10.1155/2021/8826868.
Der volle Inhalt der QuelleHirano, Hokuto, und Kazuhiro Takemoto. „Simple Iterative Method for Generating Targeted Universal Adversarial Perturbations“. Algorithms 13, Nr. 11 (22.10.2020): 268. http://dx.doi.org/10.3390/a13110268.
Der volle Inhalt der QuelleZhang, Xingyu, Xiongwei Zhang, Xia Zou, Haibo Liu und Meng Sun. „Towards Generating Adversarial Examples on Combined Systems of Automatic Speaker Verification and Spoofing Countermeasure“. Security and Communication Networks 2022 (31.07.2022): 1–12. http://dx.doi.org/10.1155/2022/2666534.
Der volle Inhalt der QuellePapadopoulos, Pavlos, Oliver Thornewill von Essen, Nikolaos Pitropakis, Christos Chrysoulas, Alexios Mylonas und William J. Buchanan. „Launching Adversarial Attacks against Network Intrusion Detection Systems for IoT“. Journal of Cybersecurity and Privacy 1, Nr. 2 (23.04.2021): 252–73. http://dx.doi.org/10.3390/jcp1020014.
Der volle Inhalt der QuelleDing, Ning, und Knut Möller. „Using adaptive learning rate to generate adversarial images“. Current Directions in Biomedical Engineering 9, Nr. 1 (01.09.2023): 359–62. http://dx.doi.org/10.1515/cdbme-2023-1090.
Der volle Inhalt der QuelleYang, Zhongguo, Irshad Ahmed Abbasi, Fahad Algarni, Sikandar Ali und Mingzhu Zhang. „An IoT Time Series Data Security Model for Adversarial Attack Based on Thermometer Encoding“. Security and Communication Networks 2021 (09.03.2021): 1–11. http://dx.doi.org/10.1155/2021/5537041.
Der volle Inhalt der QuelleSantana, Everton Jose, Ricardo Petri Silva, Bruno Bogaz Zarpelão und Sylvio Barbon Junior. „Detecting and Mitigating Adversarial Examples in Regression Tasks: A Photovoltaic Power Generation Forecasting Case Study“. Information 12, Nr. 10 (26.09.2021): 394. http://dx.doi.org/10.3390/info12100394.
Der volle Inhalt der QuellePantiukhin, 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, Nr. 1 (16.04.2023): 55–63. http://dx.doi.org/10.32517/0234-0453-2023-38-1-55-63.
Der volle Inhalt der QuelleKumar, P. Sathish, und K. V. D. Kiran. „Momentum Iterative Fast Gradient Sign Algorithm for Adversarial Attacks and Defenses“. Research Journal of Engineering and Technology, 30.06.2023, 7–24. http://dx.doi.org/10.52711/2321-581x.2023.00002.
Der volle Inhalt der QuelleNaseem, Muhammad Luqman. „Trans-IFFT-FGSM: a novel fast gradient sign method for adversarial attacks“. Multimedia Tools and Applications, 09.02.2024. http://dx.doi.org/10.1007/s11042-024-18475-7.
Der volle Inhalt der QuelleXie, Pengfei, Shuhao Shi, Shuai Yang, Kai Qiao, Ningning Liang, Linyuan Wang, Jian Chen, Guoen Hu und Bin Yan. „Improving the Transferability of Adversarial Examples With a Noise Data Enhancement Framework and Random Erasing“. Frontiers in Neurorobotics 15 (09.12.2021). http://dx.doi.org/10.3389/fnbot.2021.784053.
Der volle Inhalt der QuelleZhang, Junjian, Hao Tan, Le Wang, Yaguan Qian und Zhaoquan Gu. „Rethinking multi‐spatial information for transferable adversarial attacks on speaker recognition systems“. CAAI Transactions on Intelligence Technology, 29.03.2024. http://dx.doi.org/10.1049/cit2.12295.
Der volle Inhalt der Quelle