KR102692100B1 - 훈련된 머신 러닝 모델에 적대적 견고성 추가 - Google Patents
훈련된 머신 러닝 모델에 적대적 견고성 추가 Download PDFInfo
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- KR102692100B1 KR102692100B1 KR1020227008142A KR20227008142A KR102692100B1 KR 102692100 B1 KR102692100 B1 KR 102692100B1 KR 1020227008142 A KR1020227008142 A KR 1020227008142A KR 20227008142 A KR20227008142 A KR 20227008142A KR 102692100 B1 KR102692100 B1 KR 102692100B1
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- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F21/50—Monitoring users, programs or devices to maintain the integrity of platforms, e.g. of processors, firmware or operating systems
- G06F21/55—Detecting local intrusion or implementing counter-measures
- G06F21/552—Detecting local intrusion or implementing counter-measures involving long-term monitoring or reporting
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/07—Responding to the occurrence of a fault, e.g. fault tolerance
- G06F11/14—Error detection or correction of the data by redundancy in operations
- G06F11/1471—Error detection or correction of the data by redundancy in operations involving logging of persistent data for recovery
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- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F21/50—Monitoring users, programs or devices to maintain the integrity of platforms, e.g. of processors, firmware or operating systems
- G06F21/57—Certifying or maintaining trusted computer platforms, e.g. secure boots or power-downs, version controls, system software checks, secure updates or assessing vulnerabilities
- G06F21/577—Assessing vulnerabilities and evaluating computer system security
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
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- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/0464—Convolutional networks [CNN, ConvNet]
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- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
- G06N3/082—Learning methods modifying the architecture, e.g. adding, deleting or silencing nodes or connections
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- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
- G06N3/088—Non-supervised learning, e.g. competitive learning
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- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
- G06N3/09—Supervised learning
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- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
- G06N3/094—Adversarial learning
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- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Software Systems (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Computing Systems (AREA)
- Artificial Intelligence (AREA)
- Mathematical Physics (AREA)
- Data Mining & Analysis (AREA)
- Evolutionary Computation (AREA)
- Computer Security & Cryptography (AREA)
- Biophysics (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Computational Linguistics (AREA)
- Biomedical Technology (AREA)
- Life Sciences & Earth Sciences (AREA)
- Molecular Biology (AREA)
- Computer Hardware Design (AREA)
- Medical Informatics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Quality & Reliability (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- User Interface Of Digital Computer (AREA)
- Debugging And Monitoring (AREA)
Applications Claiming Priority (3)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| US16/601,451 US11334671B2 (en) | 2019-10-14 | 2019-10-14 | Adding adversarial robustness to trained machine learning models |
| US16/601,451 | 2019-10-14 | ||
| PCT/IB2020/059559 WO2021074770A1 (en) | 2019-10-14 | 2020-10-12 | Adding adversarial robustness to trained machine learning models |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| KR20220054812A KR20220054812A (ko) | 2022-05-03 |
| KR102692100B1 true KR102692100B1 (ko) | 2024-08-05 |
Family
ID=75383118
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| KR1020227008142A Active KR102692100B1 (ko) | 2019-10-14 | 2020-10-12 | 훈련된 머신 러닝 모델에 적대적 견고성 추가 |
Country Status (7)
| Country | Link |
|---|---|
| US (1) | US11334671B2 (https=) |
| JP (1) | JP7537709B2 (https=) |
| KR (1) | KR102692100B1 (https=) |
| CN (1) | CN114503108B (https=) |
| AU (1) | AU2020368222B2 (https=) |
| GB (1) | GB2604791B (https=) |
| WO (1) | WO2021074770A1 (https=) |
Families Citing this family (26)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US12346432B2 (en) * | 2018-12-31 | 2025-07-01 | Intel Corporation | Securing systems employing artificial intelligence |
| JP7079502B2 (ja) * | 2019-11-14 | 2022-06-02 | 株式会社アクセル | 推論システム |
| WO2021176716A1 (ja) * | 2020-03-06 | 2021-09-10 | 日本電気株式会社 | 嗜好推定装置、嗜好推定方法および嗜好推定プログラム |
| US11675896B2 (en) * | 2020-04-09 | 2023-06-13 | International Business Machines Corporation | Using multimodal model consistency to detect adversarial attacks |
| EP4133346A1 (en) * | 2020-06-30 | 2023-02-15 | Siemens Aktiengesellschaft | Providing an alarm relating to anomaly scores assigned to input data method and system |
| US12242613B2 (en) * | 2020-09-30 | 2025-03-04 | International Business Machines Corporation | Automated evaluation of machine learning models |
| US12019747B2 (en) * | 2020-10-13 | 2024-06-25 | International Business Machines Corporation | Adversarial interpolation backdoor detection |
| KR20220103247A (ko) * | 2021-01-14 | 2022-07-22 | 성균관대학교산학협력단 | 학습 데이터 분류를 이용한 연합 학습 프레임워크의 로컬 모델 학습 방법 |
| US11785024B2 (en) * | 2021-03-22 | 2023-10-10 | University Of South Florida | Deploying neural-trojan-resistant convolutional neural networks |
| WO2022224246A1 (en) * | 2021-04-19 | 2022-10-27 | Deepkeep Ltd. | Device, system, and method for protecting machine learning, artificial intelligence, and deep learning units |
| EP4348508B1 (en) * | 2021-05-31 | 2025-06-25 | Microsoft Technology Licensing, LLC | Merging models on an edge server |
| US12536467B2 (en) | 2021-05-31 | 2026-01-27 | Microsoft Technology Licensing, Llc | Merging models on an edge server |
| CN113935949B (zh) * | 2021-09-10 | 2025-09-16 | 上海联影智能医疗科技有限公司 | 乳腺钼靶图像处理方法、装置及计算机可读存储介质 |
| US12368739B2 (en) | 2021-10-13 | 2025-07-22 | Oracle International Corporation | Adaptive network attack prediction system |
| US20230134546A1 (en) * | 2021-10-29 | 2023-05-04 | Oracle International Corporation | Network threat analysis system |
| CN114355936A (zh) * | 2021-12-31 | 2022-04-15 | 深兰人工智能(深圳)有限公司 | 智能体的控制方法、装置、智能体及计算机可读存储介质 |
| CN114358282B (zh) * | 2022-01-05 | 2024-10-29 | 深圳大学 | 深度网络对抗鲁棒性提升模型、构建方法、设备、介质 |
| CN114694222B (zh) * | 2022-03-28 | 2023-08-18 | 马上消费金融股份有限公司 | 图像处理方法、装置、计算机设备及存储介质 |
| US12541683B2 (en) * | 2022-04-06 | 2026-02-03 | Nomura Research Institute, Ltd. | Information processing apparatus for improving robustness of deep neural network by using adversarial training and formal method |
| WO2024013911A1 (ja) * | 2022-07-13 | 2024-01-18 | 日本電信電話株式会社 | 学習装置、学習方法、学習プログラム、推論装置、推論方法、及び推論プログラム |
| GB2621838A (en) * | 2022-08-23 | 2024-02-28 | Mindgard Ltd | Method and system |
| KR102753131B1 (ko) * | 2022-09-19 | 2025-01-14 | 호서대학교 산학협력단 | 악성코드 변종 분석을 위한 ai 모델의 견고성 측정 시스템 및 어플리케이션 |
| EP4425384A1 (en) * | 2023-02-28 | 2024-09-04 | Fujitsu Limited | Training deep belief networks |
| US12609949B2 (en) * | 2023-04-12 | 2026-04-21 | Taif University | System and method for DNN-based cyber-security using federated learning-based generative adversarial network |
| US20250156941A1 (en) * | 2023-11-14 | 2025-05-15 | The Pnc Financial Services Group, Inc. | Technologies for Prediction of Recurring Transactions |
| US12518025B1 (en) * | 2025-07-09 | 2026-01-06 | The Florida International University Board Of Trustees | Systems and methods for automatic vulnerability assessment of machine learning models |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2019014487A1 (en) * | 2017-07-12 | 2019-01-17 | The Regents Of The University Of California | DETECTION AND PREVENTION OF DEEP ANTAGONIST LEARNING |
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| JPH08339360A (ja) * | 1995-06-13 | 1996-12-24 | Hitachi Ltd | ニューラルネットを応用した学習システム |
| EP0974660A1 (en) | 1998-06-19 | 2000-01-26 | Stichting Instituut voor Dierhouderij en Diergezondheid (ID-DLO) | Newcastle disease virus infectious clones, vaccines and diagnostic assays |
| JP2000181893A (ja) | 1998-12-11 | 2000-06-30 | Toshiba Mach Co Ltd | ニューラルネットワークの構成方法 |
| US20150134966A1 (en) | 2013-11-10 | 2015-05-14 | Sypris Electronics, Llc | Authentication System |
| US9619749B2 (en) | 2014-03-06 | 2017-04-11 | Progress, Inc. | Neural network and method of neural network training |
| US20160321523A1 (en) | 2015-04-30 | 2016-11-03 | The Regents Of The University Of California | Using machine learning to filter monte carlo noise from images |
| EP3400419B1 (en) | 2016-01-05 | 2025-08-27 | Mobileye Vision Technologies Ltd. | Trained navigational system with imposed constraints |
| US20180005136A1 (en) | 2016-07-01 | 2018-01-04 | Yi Gai | Machine learning in adversarial environments |
| CN107273747A (zh) * | 2017-05-22 | 2017-10-20 | 中国人民公安大学 | 勒索软件检测的方法 |
| CN107463951A (zh) * | 2017-07-19 | 2017-12-12 | 清华大学 | 一种提高深度学习模型鲁棒性的方法及装置 |
| CN107390949B (zh) * | 2017-09-13 | 2020-08-07 | 广州视源电子科技股份有限公司 | 获取触摸屏基准资料的方法和装置、存储介质及触摸显示系统 |
| 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 |
| CN108304858B (zh) | 2017-12-28 | 2022-01-04 | 中国银联股份有限公司 | 对抗样本识别模型生成方法、验证方法及其系统 |
| US11315012B2 (en) | 2018-01-12 | 2022-04-26 | Intel Corporation | Neural network training using generated random unit vector |
| CN108099598A (zh) | 2018-01-29 | 2018-06-01 | 三汽车起重机械有限公司 | 用于起重机的驱动装置及起重机 |
| CA3033014A1 (en) * | 2018-02-07 | 2019-08-07 | Royal Bank Of Canada | Robust pruned neural networks via adversarial training |
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| US10347241B1 (en) * | 2018-03-23 | 2019-07-09 | Microsoft Technology Licensing, Llc | Speaker-invariant training via adversarial learning |
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| CN108615048B (zh) | 2018-04-04 | 2020-06-23 | 浙江工业大学 | 基于扰动进化对图像分类器对抗性攻击的防御方法 |
| CN108734276B (zh) * | 2018-04-28 | 2021-12-31 | 同济大学 | 一种基于对抗生成网络的模仿学习对话生成方法 |
| CA3043809A1 (en) * | 2018-05-17 | 2019-11-17 | Royal Bank Of Canada | System and method for machine learning architecture with adversarial attack defence |
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| CN109885389B (zh) * | 2019-02-19 | 2021-07-16 | 浪潮云信息技术股份公司 | 一种基于容器的并行深度学习调度训练方法及系统 |
| CN110008680B (zh) * | 2019-04-03 | 2020-11-13 | 华南师范大学 | 基于对抗样本的验证码生成系统及方法 |
| CN110310206B (zh) * | 2019-07-01 | 2023-09-29 | 创新先进技术有限公司 | 用于更新风险控制模型的方法和系统 |
| EP3944159A1 (en) * | 2020-07-17 | 2022-01-26 | Tata Consultancy Services Limited | Method and system for defending universal adversarial attacks on time-series data |
-
2019
- 2019-10-14 US US16/601,451 patent/US11334671B2/en active Active
-
2020
- 2020-10-12 WO PCT/IB2020/059559 patent/WO2021074770A1/en not_active Ceased
- 2020-10-12 KR KR1020227008142A patent/KR102692100B1/ko active Active
- 2020-10-12 GB GB2207000.7A patent/GB2604791B/en active Active
- 2020-10-12 AU AU2020368222A patent/AU2020368222B2/en active Active
- 2020-10-12 JP JP2022521116A patent/JP7537709B2/ja active Active
- 2020-10-12 CN CN202080070524.1A patent/CN114503108B/zh active Active
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2019014487A1 (en) * | 2017-07-12 | 2019-01-17 | The Regents Of The University Of California | DETECTION AND PREVENTION OF DEEP ANTAGONIST LEARNING |
Non-Patent Citations (1)
| Title |
|---|
| Jonathan Hui, "GAN - Unrolled GAN (How to reduce mode collapse)"(2018.06.)* |
Also Published As
| Publication number | Publication date |
|---|---|
| US20210110045A1 (en) | 2021-04-15 |
| CN114503108A (zh) | 2022-05-13 |
| GB2604791B (en) | 2024-03-13 |
| GB2604791A (en) | 2022-09-14 |
| US11334671B2 (en) | 2022-05-17 |
| GB202207000D0 (en) | 2022-06-29 |
| JP7537709B2 (ja) | 2024-08-21 |
| CN114503108B (zh) | 2025-03-14 |
| AU2020368222B2 (en) | 2023-11-23 |
| WO2021074770A1 (en) | 2021-04-22 |
| KR20220054812A (ko) | 2022-05-03 |
| JP2022552243A (ja) | 2022-12-15 |
| AU2020368222A1 (en) | 2022-03-31 |
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