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MX2022000163A - Sistemas y metodos de aprendizaje de maquina para localizacion mejorada de falsificacion de imagenes. - Google Patents

Sistemas y metodos de aprendizaje de maquina para localizacion mejorada de falsificacion de imagenes.

Info

Publication number
MX2022000163A
MX2022000163A MX2022000163A MX2022000163A MX2022000163A MX 2022000163 A MX2022000163 A MX 2022000163A MX 2022000163 A MX2022000163 A MX 2022000163A MX 2022000163 A MX2022000163 A MX 2022000163A MX 2022000163 A MX2022000163 A MX 2022000163A
Authority
MX
Mexico
Prior art keywords
input image
localization
methods
encoder
machine learning
Prior art date
Application number
MX2022000163A
Other languages
English (en)
Inventor
Terrance E Boult
Steve Cruz
Aurobrata Ghosh
Maneesh Kumar Singh
Venkata Subbarao Veeravarasapu
Zheng Zhong
Original Assignee
Insurance Services Office Inc
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Insurance Services Office Inc filed Critical Insurance Services Office Inc
Publication of MX2022000163A publication Critical patent/MX2022000163A/es

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR 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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR 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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR 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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G06T5/77—Retouching; Inpainting; Scratch removal
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/74—Image or video pattern matching; Proximity measures in feature spaces
    • G06V10/75—Organisation of the matching processes, e.g. simultaneous or sequential comparisons of image or video features; Coarse-fine approaches, e.g. multi-scale approaches; using context analysis; Selection of dictionaries
    • G06V10/755—Deformable models or variational models, e.g. snakes or active contours
    • G06V10/7557—Deformable models or variational models, e.g. snakes or active contours based on appearance, e.g. active appearance models [AAM]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/82—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Software Systems (AREA)
  • Evolutionary Computation (AREA)
  • Artificial Intelligence (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Computing Systems (AREA)
  • General Health & Medical Sciences (AREA)
  • Health & Medical Sciences (AREA)
  • Data Mining & Analysis (AREA)
  • General Engineering & Computer Science (AREA)
  • Mathematical Physics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Medical Informatics (AREA)
  • Biomedical Technology (AREA)
  • Molecular Biology (AREA)
  • Computational Linguistics (AREA)
  • Biophysics (AREA)
  • Databases & Information Systems (AREA)
  • Multimedia (AREA)
  • Quality & Reliability (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Evolutionary Biology (AREA)
  • Image Analysis (AREA)
  • Collating Specific Patterns (AREA)

Abstract

Un sistema para mejorar la localización de la falsificación de imágenes. El sistema genera una función objetivo de cuello de botella de información variacional y trabaja con parches de imagen de entrada para implementar una arquitectura de codificador-decodificador. La arquitectura de codificador-decodificador controla un flujo de información entre los parches de imagen de entrada y una capa de representación. El sistema utiliza el cuello de botella de información para aprender patrones de ruido residual útiles e ignorar el contenido semántico presente en cada parche de imagen de entrada. El sistema entrena una red neuronal para aprender una representación indicativa de una huella dactilar estadística de un modelo de cámara de origen de cada parche de imagen de entrada mientras excluye el contenido semántico del mismo. El sistema puede determinar una localización de manipulación de empalme por la red neuronal entrenada.
MX2022000163A 2019-06-24 2020-06-24 Sistemas y metodos de aprendizaje de maquina para localizacion mejorada de falsificacion de imagenes. MX2022000163A (es)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US201962865414P 2019-06-24 2019-06-24
PCT/US2020/039394 WO2020264006A1 (en) 2019-06-24 2020-06-24 Machine learning systems and methods for improved localization of image forgery

Publications (1)

Publication Number Publication Date
MX2022000163A true MX2022000163A (es) 2022-05-20

Family

ID=74039358

Family Applications (1)

Application Number Title Priority Date Filing Date
MX2022000163A MX2022000163A (es) 2019-06-24 2020-06-24 Sistemas y metodos de aprendizaje de maquina para localizacion mejorada de falsificacion de imagenes.

Country Status (6)

Country Link
US (1) US11663489B2 (es)
EP (1) EP3987483A4 (es)
AU (1) AU2020306013A1 (es)
CA (1) CA3145241A1 (es)
MX (1) MX2022000163A (es)
WO (1) WO2020264006A1 (es)

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US11967184B2 (en) 2021-05-21 2024-04-23 Ford Global Technologies, Llc Counterfeit image detection
US20220374641A1 (en) * 2021-05-21 2022-11-24 Ford Global Technologies, Llc Camera tampering detection
CN113627503B (zh) * 2021-07-30 2023-10-24 中国科学院计算技术研究所 生成图像溯源方法与装置、模型训练方法与装置、电子设备及存储介质
CN114331974B (zh) * 2021-12-09 2024-06-21 上海大学 一种基于特征融合的图像篡改检测方法
CN114758364B (zh) * 2022-02-09 2022-09-23 四川大学 基于深度学习的工业物联网场景融合定位方法及系统
CN114612693B (zh) * 2022-03-21 2024-08-02 华南理工大学 基于编解码网络的图像拼接伪造定位方法
WO2023196858A1 (en) * 2022-04-05 2023-10-12 North Carolina State University Diversity based deep learning system
CN114491135A (zh) * 2022-04-06 2022-05-13 成都考拉悠然科技有限公司 一种基于变分信息瓶颈的跨视角地理图像检索方法
CN114764858B (zh) * 2022-06-15 2022-11-01 深圳大学 一种复制粘贴图像识别方法、装置、计算机设备及存储介质
CN115063979B (zh) * 2022-08-19 2022-12-23 合肥工业大学 一种智能网联环境下的交通信息量化方法及其系统
CN117275068B (zh) * 2023-09-21 2024-05-17 北京中科闻歌科技股份有限公司 含不确定性引导的测试阶段训练人脸伪造检测方法及系统
CN118314449A (zh) * 2024-03-15 2024-07-09 深圳大学 一种基于强化学习的图像篡改定位方法、系统及终端

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FR3019350B1 (fr) * 2014-03-28 2017-07-21 Univ De Tech De Troyes Systeme d'identification d'un modele d'appareil photographique associe a une image compressee au format jpeg, procede, utilisations et applications associes
US9721559B2 (en) 2015-04-17 2017-08-01 International Business Machines Corporation Data augmentation method based on stochastic feature mapping for automatic speech recognition
US11080591B2 (en) * 2016-09-06 2021-08-03 Deepmind Technologies Limited Processing sequences using convolutional neural networks
KR102458808B1 (ko) * 2016-10-26 2022-10-25 딥마인드 테크놀로지스 리미티드 신경망을 이용한 텍스트 시퀀스 처리
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Also Published As

Publication number Publication date
WO2020264006A1 (en) 2020-12-30
CA3145241A1 (en) 2020-12-30
EP3987483A4 (en) 2023-03-01
EP3987483A1 (en) 2022-04-27
AU2020306013A1 (en) 2022-02-10
US11663489B2 (en) 2023-05-30
US20200402223A1 (en) 2020-12-24

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