DE112021005847T5 - Dynamische gradientenverschleierung gegen feindliche beispiele bei maschinenlernmodellen - Google Patents

Dynamische gradientenverschleierung gegen feindliche beispiele bei maschinenlernmodellen Download PDF

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DE112021005847T5
DE112021005847T5 DE112021005847.9T DE112021005847T DE112021005847T5 DE 112021005847 T5 DE112021005847 T5 DE 112021005847T5 DE 112021005847 T DE112021005847 T DE 112021005847T DE 112021005847 T5 DE112021005847 T5 DE 112021005847T5
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Taesung Lee
Ian Michael Molloy
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International Business Machines Corp
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DE112021005847.9T 2020-12-08 2021-11-22 Dynamische gradientenverschleierung gegen feindliche beispiele bei maschinenlernmodellen Pending DE112021005847T5 (de)

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US17/114,819 US12050993B2 (en) 2020-12-08 2020-12-08 Dynamic gradient deception against adversarial examples in machine learning models
US17/114,819 2020-12-08
PCT/IB2021/060808 WO2022123372A1 (en) 2020-12-08 2021-11-22 Dynamic gradient deception against adversarial examples in machine learning models

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JP (1) JP7754599B2 (https=)
CN (1) CN116670693A (https=)
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Families Citing this family (16)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US12493666B2 (en) * 2021-01-14 2025-12-09 Origin Research Wireless, Inc. Wireless sensing using classifier probing and refinement
US20220405531A1 (en) * 2021-06-15 2022-12-22 Etsy, Inc. Blackbox optimization via model ensembling
US20230071450A1 (en) * 2021-09-09 2023-03-09 Siemens Aktiengesellschaft System and method for controlling large scale power distribution systems using reinforcement learning
CN115278757B (zh) * 2022-07-25 2025-05-20 绿盟科技集团股份有限公司 一种检测异常数据的方法、装置及电子设备
CN114998707B (zh) * 2022-08-05 2022-11-04 深圳中集智能科技有限公司 评估目标检测模型鲁棒性的攻击方法和装置
US11947902B1 (en) * 2023-03-03 2024-04-02 Microsoft Technology Licensing, Llc Efficient multi-turn generative AI model suggested message generation
US11962546B1 (en) 2023-03-03 2024-04-16 Microsoft Technology Licensing, Llc Leveraging inferred context to improve suggested messages
US12282731B2 (en) 2023-03-03 2025-04-22 Microsoft Technology Licensing, Llc Guardrails for efficient processing and error prevention in generating suggested messages
US20240378726A1 (en) * 2023-05-12 2024-11-14 GE Precision Healthcare LLC Deep learning based medical imaging system and method
US12580929B2 (en) * 2023-07-25 2026-03-17 Crowdstrike, Inc. Techniques for assessing malware classification
CN116680727B (zh) * 2023-08-01 2023-11-03 北京航空航天大学 一种面向图像分类模型的功能窃取防御方法
US12587564B2 (en) * 2023-08-15 2026-03-24 Cisco Technology, Inc. Adversarial training of language models to prevent hijacking of conversational agents
US20250217255A1 (en) * 2024-01-03 2025-07-03 Samsung Electronics Co., Ltd. Method and apparatus with ai model performance measuring using perturbation
CN118747837B (zh) * 2024-08-12 2024-11-15 北京小蝇科技有限责任公司 基于机器学习的样本数据处理方法和装置
CN119150031B (zh) * 2024-11-13 2025-10-10 阿里云飞天(杭州)云计算技术有限公司 模型训练方法和数据处理方法
CN119202258B (zh) * 2024-11-25 2025-02-28 西安融军通用标准化研究院有限责任公司 一种基于机器学习的标准文本分类方法

Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20110125734A1 (en) 2009-11-23 2011-05-26 International Business Machines Corporation Questions and answers generation

Family Cites Families (31)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5359699A (en) 1991-12-02 1994-10-25 General Electric Company Method for using a feed forward neural network to perform classification with highly biased data
US5371809A (en) 1992-03-30 1994-12-06 Desieno; Duane D. Neural network for improved classification of patterns which adds a best performing trial branch node to the network
US7409372B2 (en) 2003-06-20 2008-08-05 Hewlett-Packard Development Company, L.P. Neural network trained with spatial errors
US8275803B2 (en) 2008-05-14 2012-09-25 International Business Machines Corporation System and method for providing answers to questions
US8280838B2 (en) 2009-09-17 2012-10-02 International Business Machines Corporation Evidence evaluation system and method based on question answering
WO2012047557A1 (en) 2010-09-28 2012-04-12 International Business Machines Corporation Evidence diffusion among candidate answers during question answering
US8738617B2 (en) 2010-09-28 2014-05-27 International Business Machines Corporation Providing answers to questions using multiple models to score candidate answers
US8601030B2 (en) 2011-09-09 2013-12-03 International Business Machines Corporation Method for a natural language question-answering system to complement decision-support in a real-time command center
US9390370B2 (en) 2012-08-28 2016-07-12 International Business Machines Corporation Training deep neural network acoustic models using distributed hessian-free optimization
US20150170027A1 (en) 2013-12-13 2015-06-18 Qualcomm Incorporated Neuronal diversity in spiking neural networks and pattern classification
US10621487B2 (en) 2014-09-17 2020-04-14 Hewlett Packard Enterprise Development Lp Neural network verification
US9786270B2 (en) 2015-07-09 2017-10-10 Google Inc. Generating acoustic models
US10044751B2 (en) 2015-12-28 2018-08-07 Arbor Networks, Inc. Using recurrent neural networks to defeat DNS denial of service attacks
CN105718945B (zh) 2016-01-20 2020-07-31 江苏大学 基于分水岭和神经网络的苹果采摘机器人夜间图像识别方法
US9948666B2 (en) 2016-02-09 2018-04-17 International Business Machines Corporation Forecasting and classifying cyber-attacks using analytical data based neural embeddings
CN106127729A (zh) 2016-06-08 2016-11-16 浙江传媒学院 一种基于梯度的图像噪声水平估计方法
CN106296692A (zh) 2016-08-11 2017-01-04 深圳市未来媒体技术研究院 基于对抗网络的图像显著性检测方法
US10915817B2 (en) 2017-01-23 2021-02-09 Fotonation Limited Method of training a neural network
CN106845471A (zh) 2017-02-20 2017-06-13 深圳市唯特视科技有限公司 一种基于生成对抗网络的视觉显著性预测方法
EP3602316A4 (en) 2017-03-24 2020-12-30 D5A1 Llc LEARNING COACH FOR AUTOMATIC LEARNING SYSTEM
CN107025284B (zh) 2017-04-06 2020-10-27 中南大学 网络评论文本情感倾向的识别方法及卷积神经网络模型
CN107147603B (zh) 2017-05-05 2019-10-08 西安电子科技大学 基于多神经网络的dbpsk解调方法
CN107240085A (zh) 2017-05-08 2017-10-10 广州智慧城市发展研究院 一种基于卷积神经网络模型的图像融合方法及系统
WO2018231708A2 (en) 2017-06-12 2018-12-20 D5Ai Llc Robust anti-adversarial machine learning
US11023593B2 (en) 2017-09-25 2021-06-01 International Business Machines Corporation Protecting cognitive systems from model stealing attacks
US10642846B2 (en) 2017-10-13 2020-05-05 Microsoft Technology Licensing, Llc Using a generative adversarial network for query-keyword matching
US10657259B2 (en) 2017-11-01 2020-05-19 International Business Machines Corporation Protecting cognitive systems from gradient based attacks through the use of deceiving gradients
EP3770777A4 (en) 2018-03-20 2021-05-05 Sony Corporation INFORMATION PROCESSING DEVICE AND INFORMATION PROCESSING METHOD
US10733292B2 (en) 2018-07-10 2020-08-04 International Business Machines Corporation Defending against model inversion attacks on neural networks
US11227215B2 (en) 2019-03-08 2022-01-18 International Business Machines Corporation Quantifying vulnerabilities of deep learning computing systems to adversarial perturbations
US11017319B1 (en) * 2020-06-23 2021-05-25 Deeping Source Inc. Method for training obfuscation network which conceals original data to be used for machine learning and training surrogate network which uses obfuscated data generated by obfuscation network and method for testing trained obfuscation network and learning device and testing device using the same

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20110125734A1 (en) 2009-11-23 2011-05-26 International Business Machines Corporation Questions and answers generation

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US12050993B2 (en) 2024-07-30
CN116670693A (zh) 2023-08-29
GB2617735A (en) 2023-10-18
JP7754599B2 (ja) 2025-10-15
GB202310212D0 (en) 2023-08-16
JP2023551976A (ja) 2023-12-13
US20220180242A1 (en) 2022-06-09

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