Patent application EP21182951A (EP), filed 2021-06-30
Publication number: EP4102399A1
Title: OBJECT AUTHENTICATION USING DIGITAL BLUEPRINTS AND PHYSICAL FINGERPRINTS
Abstract: [0001]    A method of object authentication based on digital blueprints and physical fingerprints comprising the steps of acquiring a set of training blueprints and fingerprints, training, object enrollment and object authentication. The method uses a pair of a mapper preferably realized as an encoder and a decoder and a set of multi-metric scores originating from the decomposition of mutual information and applied to both the output of the encoder and decoder and producing a feature vector for a one-class classifier. The method is trained only on the original physical objects and does not use any fakes for reliable authentication. The invention also concerns a method for generating synthetic fingerprints from given blueprints with controllable parameters of proximity to a manifold of original data that simulates the entire chain of production and acquisition as well as a method for training on these synthetically generated fingerprints. The method offers a solution how different synthetically generated fingerprints can be used to enhance the authentication performance under the variability of manufacturing at acquisition conditions while minimizing the probability of miss and allows to compensate the lack of priors about fakes while minimizing the probability of false acceptance. The invention is also directed to corresponding computer program means adapted to implement the proposed method, devices adapted for implementation of the proposed method, as well as applications which are particularly adapted for use of the proposed method.
IPC codes: G06K   9/    00            A I
G06K   9/    62            A I
G06F  18/    21            A I
G06F  18/  2135            A I
G06F  18/    22            A I
G06F  18/  2413            A I
G06F  18/  2433            A I
G06V  10/    82            A I
G06V  20/    80            A I
Claims: number: 1
text: Method for authentication of physical objects, in particular of physical batch objects, adapted to be produced based on a set of blueprints  t i i = 1 N
number: 2
text: Method according to the preceding claim 1, characterized by the fact that- said fingerprints  x i i = 1 N and blueprints  t i i = 1 N represent paired data,
number: 3
text: Method according to the preceding claim 1, characterized by the fact that the method further comprises, at said training stage, the step of
number: 4
text: Method according to the preceding claim 1, characterized by the fact that said mapper is realized by an encoder (200) adapted to produce an estimate t̃i
number: 5
text: Method according to the preceding claim 1, characterized by the fact that said mapper is realized by an encoder (200) adapted to produce an estimate t̂i
number: 6
text: Method according to the preceding claim 1, characterized by the fact that said mapper is realized by an encoder (200) adapted to produce an estimate t̃i
number: 7
text: Method according to one of the preceding claims, characterized by the fact that said authentication is performed only from the blueprint ti or only from the fingerprint xi or jointly from the blueprint-fingerprint pair (ti,xi) representing an object with the index i.
number: 8
text: Method according to one of the preceding claims, characterized by the fact that said sets of similarity scores for said fingerprints and/or for said blueprints (220, 230) are each sets of multi-metric similarity scores measuring similarity and comprising each at least two metrics chosen from the group comprising Euclidean l2
number: 9
text: Method according to the preceding claim, characterized by the fact that output of said multi-metric similarity scores is concatenated into a feature vector and serves as an input to said one-class classifier (250) which is implemented as a kernel one-class SVM, deep classifier or as a hand-crafted decision rule bounding said set of multi-metric similarity scores from those of hypothetical fakes.
number: 10
text: Method according to one of the preceding claims in combination with one of claims 4 to 6, characterized by the fact that said discriminators (221, 222, 231, 232) are realized as criteria implementing density ration estimation, MMD, Wasserstein or generalized divergences, U-net with a latent and output space used as a global and local discrimination, the discriminators being applied to the entire data or only to its parts having further aggregation of scores of local discriminators.
number: 11
text: Method according to one of the preceding claims in combination with one of claims 4 to 6, characterized by the fact that said encoder (200) and decoder (210) are represented as deep neural networks and implemented based on U-net architecture, several down sampling convolutional layers followed by several ResNet layers and several up sampling convolutional layers, invertable networks such as normalizing FLOWS or similar ones and also by injecting the randomization.
number: 12
text: Method according to one of the preceding claims 4 to 11, characterized by the fact that said encoder (200), decoder (210), discriminators (230), (222), (252), (254) are trained from target samples x1
number: 13
text: Method according to one of the preceding claims 4 to 11, characterized by the fact that said encoder (200), decoder (210), discriminators (230), (222), (252), (254) are trained from parameters defining printer settings (264) encoded by the encoder (260) and phone settings (265) encoded by the encoder (262).
number: 14
text: Method according to one of the preceding claims 4 to 11, characterized by the fact that said encoder (200), decoder (210) are trained on paired data of blueprints-fingerprint  t i x i i = 1 N
number: 15
text: Method according to one of the preceding claims 4 to 11, characterized by the fact that said learned encoder (200) and decoder (210) are used to produce synthetic samples  x ˜ i i = 1 N
number: 16
text: Method according to the preceding claim, characterized by the fact that said synthetic samples  x ˜ i i = 1 N
number: 17
text: Method according to one of the preceding claims 15 to 16, characterized by the fact that said synthetic samples  x ˜ i i = 1 N
number: 18
text: Method according to one of the preceding claims, characterized by the fact that said blueprints  t i i = 1 N
number: 20
text: Use of a method according to one of the preceding claims 1 to 19 for an application chosen from the group comprising protection of documents, certificates, contracts, identity documents, packaging, secure labels, stickers, banknotes, checks, luxury goods such as watches, precious stones and metals, diamonds, electronics, chips, holograms, medicaments, cosmetics, food, spare and medical parts and components and providing the related object tracking and tracing, circulation and delivery monitoring and connectivity to the corresponding database records including blockchain.
number: 21
text: Use of a method according to one of the preceding claims 1 to 19 for an application chosen from the group comprising generation of synthetic samples of printed objects acquired under various imaging conditions and simulation of various distortions in imaging systems that include the noise in RAW images under various settings of imaging device, geometric aberrations and defocusing.
number: 22
text: Use of a method according to one of the preceding claims 1 to 19 for authentication of a person from a probe signal y with respect to a reference blueprint ti reproduced on an identification document of said person or stored in some electronic form in private or public domain and a fingerprint xi representing reference biometric data of said person.
number: 23
text: Use of a method according to one of the preceding claims 1 to 19 for an application chosen from the group comprising automatic quality estimation of newly produced objects and anomaly detection during manufacturing.
number: 24
text: A device adapted for the implementation of a method according to one of the preceding claims 1 to 19, characterized by the fact that the device is chosen from the group comprising a mobile phone, a smart phone equipped with a camera, a digital photo apparatus, a barcode reader equipped with a camera, a scanning device, a portable microscope connected to any portable device equipped with communication means.
number: 25
text: Computer program means stored in a computer readable medium adapted to implement a method according to one of the preceding claims 1 to 19.
Office action: search_report, dated 2022-12-14
Rejection reasons: inventive_step_epc_56
Prior art cited: ref_id: US8705873B2
ref_type: patent
severity: background
ref_id: WO2007028799A1
ref_type: patent
severity: background
ref_id: WO9724699A1
ref_type: patent
severity: background
ref_id: WO2005088517A1
ref_type: patent
severity: background
ref_id: US8387889B2
ref_type: patent
severity: background
ref_id: US9594993B2
ref_type: patent
severity: background
ref_id: US9436852B2
ref_type: patent
severity: background
ref_id: US8736910B2
ref_type: patent
severity: background
ref_id: US10019646B2
ref_type: patent
severity: background
ref_id: US9947163B2
ref_type: patent
severity: background
ref_id: US9552543B2
ref_type: patent
severity: background
ref_id: US7644281B2
ref_type: patent
severity: background
ref_id: US7965862B2
ref_type: patent
severity: background
ref_id: US9794067B2
ref_type: patent
severity: background
ref_id: US201715302866A
ref_type: patent
severity: background
ref_id: - CHAU-WAI WONGMIN WU: "Counterfeit detection based on unclonable feature of paper using mobile camera", IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, vol. 12, no. 8, 2017, pages 1885 - 1899, XP011648200, DOI: 10.1109/TIFS.2017.2694404
ref_type: npl
severity: background
ref_id: - RUDOLF SCHRAMLLUCA DEBIASIANDREAS UHL: "Proceedings of the 6th ACM Workshop on Information Hiding and Multimedia Security, New York, NY, USA", 2018, ASSOCIATION FOR COMPUTING MACHINERY, article "Real or fake: Mobile device drug packaging authentication", pages: 121 - 126
ref_type: npl
severity: background
ref_id: - RIIKKA ARPPETHOMAS JUST SRENSEN: "Physical unclonable functions generated through chemical methods for anti-counterfeiting", NATURE REVIEWS CHEMISTRY, vol. 1, no. 4, 2017, XP055382550, DOI: 10.1038/s41570-017-0031
ref_type: npl
severity: background
ref_id: - MIGUEL R. CARRO-TEMBOURYRIIKKAARPPETOM VOSCHTHOMAS JUST SORENSEN: "An optical authentication system based on imaging of excitation-selected lanthanide luminescence", SCIENCE ADVANCES, vol. 4, no. 1, 2018, XP055516382, DOI: 10.1126/sciadv.1701384
ref_type: npl
severity: background
ref_id: - ALI VALEHIABOLFAZL RAZIBERTRAND CAMBOUWEIJIE YUMICHAEL KOZICKI: "A graph matching algorithm for user authentication in data networks using image-based physical unclonable functions", COMPUTING CONFERENCE, 2017, pages 863 - 870, XP033294615, DOI: 10.1109/SAI.2017.8252196
ref_type: npl
severity: background
ref_id: - SVIATOSLAV VOLOSHYNOVSKIYMAURITS DIEPHUISTARAS HOLOTYAK: "Mobile visual object identifcation: from sift-bof-ransac to sketchprint", PROCEEDINGS OF SPIE PHOTONICS WEST, ELECTRONIC IMAGING, MEDIA FORENSICS AND SECURITY V, SAN FRANCISCO, USA, 13 January 2015 (2015-01-13)
ref_type: npl
severity: background
ref_id: - MAURITS DIEPHUIS: "Ph.D. thesis", 2017, UNIVERSITY OF GENEVA, article "Micro-structure based physical object identifcation on mobile platforms"
ref_type: npl
severity: background
ref_id: - F. BEEKHOFS. VOLOSHYNOVSKIYF. FARHADZADEH: "Content authentication and identifcation under informed attacks", PROCEEDINGS OF IEEE INTERNATIONAL WORKSHOP ON INFORMATION FORENSICS AND SECURITY, TENERIFE, SPAIN, 2 December 2012 (2012-12-02)
ref_type: npl
severity: background
ref_id: - MAURITS DIEPHUIS: "M.S. thesis", 2010, UNIVERSITY OF TWENTE, article "A framework for robust forensic image identifcation"
ref_type: npl
severity: background
ref_id: - MAURITS DIEPHUISSVYATOSLAV VOLOSHYNOVSKIYTARAS HOLOTYAKNABIL STENDARDOBRUNO KEEL: "Media Watermarking, Security, and Forensics", vol. 9409, March 2015, SOCIETY OF PHOTO-OPTICAL INSTRUMENTATION ENGINEERS (SPIE) CONFERENCE SERIES, article "A framework for fast and secure packaging identifcation on mobile phones", pages: 940 - 906
ref_type: npl
severity: background
ref_id: - JUSTIN PICARD: "Optical Security and Counterfeit Deterrence Techniques V", vol. 5310, 2004, INTERNATIONAL SOCIETY FOR OPTICS AND PHOTONICS, article "Digital authentication with copy-detection patterns", pages: 176 - 183
ref_type: npl
severity: background
ref_id: - LULIIA TKACHENKOCHRISTOPHE DESTRUEL: "Exploitation of redundancy for pattern estimation of copy-sensitive two level qr code", 2018 IEEE INTERNATIONAL WORKSHOP ON INFORMATION FORENSICS AND SECURITY (WIFS, 2018, pages 1 - 6, XP033511107, DOI: 10.1109/WIFS.2018.8630792
ref_type: npl
severity: background
ref_id: - THOMAS DEWAELEMAURITS DIEPHUISTARAS HOLOTYAKSVIATOSLAV VOLOSHYNOVSKIY: "Forensic authentication of banknotes on mobile phones", PROCEEDINGS OF SPIE PHOTONICS WEST, ELECTRONIC IMAGING, MEDIA FORENSICS AND SECURITY V, SAN FRANCISCO, USA, 14 January 2016 (2016-01-14)
ref_type: npl
severity: background
ref_id: - GUY ADAMSSTEPHEN POLLARDSTEVEN SIMSKE: "Proceedings of the 11th ACM Symposium on Document Engineering, New York, NY, USA", 2011, ASSOCIATION FOR COMPUTING MACHINERY, article "A study of the interaction of paper substrates on printed forensic imaging", pages: 263266
ref_type: npl
severity: background
ref_id: - STEPHEN B. POLLARDSTEVEN J. SIMSKEGUY B. ADAMS: "Model based print signature profle extraction for forensic analysis of individual text glyphs", 2010 IEEE INTERNATIONAL WORKSHOP ON INFORMATION FORENSICS AND SECURITY, 2010, pages 1 - 6, XP031913514, DOI: 10.1109/WIFS.2010.5711442
ref_type: npl
severity: background
ref_id: - YANLING JUDHRUV SAXENATAMAR KASHTIDROR KELLADORON SHAKEDMANI FISCHERROBERT ULICHNEYJAN P. ALLEBACH: "Color Imaging XVII: Displaying, Processing, Hardcopy, and Applications", vol. 8292, 2012, INTERNATIONAL SOCIETY FOR OPTICS AND PHOTONICS, article "Modeling large-area infuence in digital halftoning for electrophotographic printers", pages: 259 - 267
ref_type: npl
severity: background
ref_id: - RENATO VILLANSVIATOSLAV VOLOSHYNOVSKIYOLEKSIY KOVALTHIERRY PUN: "Multilevel 2-d bar codes: Toward high-capacity storage modules for multimedia security and management", IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, vol. 1, no. 4, 2006, pages 405 - 420, XP011150462, DOI: 10.1109/TIFS.2006.885022
ref_type: npl
severity: background
ref_id: - SARTID VONGPRADHIPSUPPAT RUNGRAUNGSILP: "Qr code using invisible watermarking in frequency domain", 2011 NINTH INTERNATIONAL CONFERENCE ON ICT AND KNOWLEDGE ENGINEERING, 2012, pages 47 - 52, XP032116171, DOI: 10.1109/ICTKE.2012.6152412
ref_type: npl
severity: background
ref_id: - OLGA TARANSLAVI BONEVSLAVA VOLOSHYNOVSKIY: "Clonability of anti-counterfeiting printable graphical codes: a machine learning approach", IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), BRIGHTON, UNITED KINGDOM, May 2019 (2019-05-01)
ref_type: npl
severity: background
ref_id: - ROHIT YADAVLULIIA TKACHENKOALAIN TR'EMEAUTHIERRY FOURNEL: "Estimation of copy-sensitive codes using a neural approach", 7TH ACM WORKSHOP ON INFORMATION HIDING AND MULTIMEDIA SECURITY, PARIS, FRANCE, July 2019 (2019-07-01)
ref_type: npl
severity: background
ref_id: - BURT PERRYSCOTT CARRPHIL PATTERSON: "Optical Security and Counterfeit Deterrence Techniques III", vol. 3973, 2000, INTERNATIONAL SOCIETY FOR OPTICS AND PHOTONICS, article "Digital watermarks as a security feature for identity documents", pages: 80 - 87
ref_type: npl
severity: background
ref_id: - PILLAI PRAVEEN THULASIDHARANMADHU S. NAIR: "Qr code based blind digital image watermarking with attack detection code", AEU -INTERNATIONAL JOURNAL OF ELECTRONICS AND COMMUNICATIONS, vol. 69, no. 7, 2015, pages 1074 - 1084, XP029157500, DOI: 10.1016/j.aeue.2015.03.007
ref_type: npl
severity: background
ref_id: - GUANGMIN SUNRUI WANGSHU WANGXIAOMENG WANGDEQUN ZHAOANDI ZHANG: "High-defnition digital color image watermark algorithm based on qr code and dwt", 2015 IEEE 10TH CONFERENCE ON INDUSTRIAL ELECTRONICS AND APPLICATIONS (ICIEA, 2015, pages 220 - 223, XP032815873, DOI: 10.1109/ICIEA.2015.7334114
ref_type: npl
severity: background
ref_id: - YANG-WAI CHOWWILLY SUSILOJOSEPH TONIENWEI ZONG: "Information Security and Privacy", 2017, SPRINGER INTERNATIONAL PUBLISHING, article "A qr code watermarking approach based on the dwt-dct technique", pages: 314 - 331
ref_type: npl
severity: background
ref_id: - XIAOFEI FENGXINGZHONG JI: "A blind watermarking method with strong robust based on 2d-barcode", 2009 INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY AND COMPUTER SCIENCE, vol. 2, 2009, pages 452 - 456, XP031499969
ref_type: npl
severity: background
ref_id: - WEIJUN ZHANGXUETIAN MENG: "An improved digital watermarking technology based on qr code", 2015 4TH INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND NETWORK TECHNOLOGY (ICCSNT, vol. 01, 2015, pages 1004 - 1007, XP032912040, DOI: 10.1109/ICCSNT.2015.7490906
ref_type: npl
severity: background
ref_id: - LI LIRUILING WANGCHINCHEN CHANG: "A digital watermark algorithm for qr code", INTERNATIONAL JOURNAL OF INTELLIGENT INFORMATION PROCESSING, vol. 2, no. 2, 2011, pages 29 - 36
ref_type: npl
severity: background
ref_id: - JANTANA PANYAVARAPORNPARAMATE HORKAEWWANNAREE WONGTRAIRAT: "Qr code watermarking algorithm based on wavelet transform", 2013 13TH INTERNATIONAL SYMPOSIUM ON COMMUNICATIONS AND INFORMATION TECHNOLOGIES (ISCIT, 2013, pages 791 - 796, XP032514046, DOI: 10.1109/ISCIT.2013.6645969
ref_type: npl
severity: background
ref_id: - MING SUNJIBO SISHUHUAI ZHANG: "Research on embedding and extracting methods for digital watermarks applied to qr code images", NEW ZEALAND JOURNAL OF AGRICULTURAL RESEARCH, vol. 50, no. 5, 2007, pages 861 - 867
ref_type: npl
severity: background
ref_id: - RONGSHENG XIECHAOQUN HONGSHUNZHI ZHUDAPENG TAO: "Anti-counterfeiting digital watermarking algorithm for printed qr barcode", NEUROCOMPUTING, vol. 167, 2015, pages 625 - 635
ref_type: npl
severity: background
ref_id: - PEI-YU LINYI-HUI CHENERIC JUI-LIN LUPING-JUNG CHEN: "Secret hiding mechanism using qr barcode", 2013 INTERNATIONAL CONFERENCE ON SIGNAL-IMAGE TECHNOLOGY INTERNET-BASED SYSTEMS, 2013, pages 22 - 25, XP032561402, DOI: 10.1109/SITIS.2013.15
ref_type: npl
severity: background
ref_id: - ARI MOESRIAMI BARMAWIFAZMAH ARIF YULIANTO: "Watermarking qr code", 2015 2ND INTERNATIONAL CONFERENCE ON INFORMATION SCIENCE AND SECURITY (ICISS, 2015, pages 1 - 4, XP032845389, DOI: 10.1109/ICISSEC.2015.7371041
ref_type: npl
severity: background
ref_id: - THACH V. BUINGUYEN K. VUTHONG T.P. NGUYENISAO ECHIZENTHUC D. NGUYEN: "Robust message hiding for qr code", 2014 TENTH INTERNATIONAL CONFERENCE ON INTELLIGENT INFORMATION HIDING AND MULTIMEDIA SIGNAL PROCESSING, 2014, pages 520 - 523, XP032715183, DOI: 10.1109/IIH-MSP.2014.135
ref_type: npl
severity: background
ref_id: - LULIIA TKACHENKOWILLIAM PUECHCHRISTOPHE DESTRUELOLIVIER STRAUSSJEAN-MARC GAUDINCHRISTIAN GUICHARD: "Two-level qr code for private message sharing and document authentication", IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, vol. 11, no. 3, 2016, pages 571 - 583, XP011594898, DOI: 10.1109/TIFS.2015.2506546
ref_type: npl
severity: background
ref_id: - LU. TKACHENKOW. PUECHO. STRAUSSC. DESTRUELJ.-M. GAUDIN: "Printed document authentication using two level or code", 2016 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP, 2016, pages 2149 - 2153, XP032900983, DOI: 10.1109/ICASSP.2016.7472057
ref_type: npl
severity: background
ref_id: - YUQIAO CHENGZHENGXIN FUBIN YUGANG SHEN: "A new two-level qr code with visual cryptography scheme", MULTIMEDIA TOOLS AND APPLICATIONS, vol. 77, no. 16, 2018, pages 2062920649
ref_type: npl
severity: background
ref_id: - H. PHUONG NGUYENAGNS DELAHAIESFLORENT RETRAINTD. HUY NGUYENMARC PICFREDERIC MORAIN-NICOLIER: "A watermarking technique to secure printed qr codes using a statistical test", 2017 IEEE GLOBAL CONFERENCE ON SIGNAL AND INFORMATION PROCESSING (GLOBALSIP, 2017, pages 288 - 292, XP033327576, DOI: 10.1109/GlobalSIP.2017.8308650
ref_type: npl
severity: background
ref_id: - HOAI PHUONG NGUYENFLORENT RETRAINTFRDRIC MORAIN-NICOLIERANGS DELAHAIES: "A watermarking technique to secure printed matrix barcodeapplication for anti-counterfeit packaging", IEEE ACCESS, vol. 7, 2019, pages 131839 - 131850, XP011747349, DOI: 10.1109/ACCESS.2019.2937465
ref_type: npl
severity: background
ref_id: - TAILING YUANYILI WANGKUN XURALPH R. MARTINSHI-MIN HU: "Two-layer qr codes", IEEE TRANSACTIONS ON IMAGE PROCESSING, vol. 28, no. 9, 2019, pages 4413 - 4428, XP011732989, DOI: 10.1109/TIP.2019.2908490
ref_type: npl
severity: background
ref_id: - MARTIN KUTTERSVIATOSLAV V. VOLOSHYNOVSKIYALEXANDER HERRIGEL: "Security and Watermarking of Multimedia Contents II", vol. 3971, 2000, INTERNATIONAL SOCIETY FOR OPTICS AND PHOTONICS, article "Watermark copy attack", pages: 371 - 380
ref_type: npl
severity: background
ref_id: - SANDY HUANGNICOLAS PAPERNOTIAN GOODFELLOWYAN DUANPIETER ABBEEL, ADVERSARIAL ATTACKS ON NEURAL NETWORK POLICIES, 2017
ref_type: npl
severity: background
ref_id: - NICHOLAS CARLINIDAVID WAGNER: "Towards evaluating the robustness of neural networks", 2017 IEEE SYMPOSIUM ON SECURITY AND PRIVACY (SP, 2017, pages 39 - 57, XP033108086, DOI: 10.1109/SP.2017.49
ref_type: npl
severity: background
ref_id: - ANH THU PHAN HOBAO AN HOANG MAIWADIH SAWAYAPATRICK BAS: "Document Authentication Using Graphical Codes: Impacts of the Channel Model", ACM WORKSHOP ON INFORMATION HIDING AND MULTIMEDIA SECURITY, MONTPELLIER, FRANCE, June 2013 (2013-06-01)
ref_type: npl
severity: background
ref_id: - KILIAN Q WEINBERGERJOHN BLITZERLAWRENCE SAUL: "Pattern Recognition and Machine Learning (Information Science and Statistics", vol. 18, 2006, SPRINGER-VERLAG, article "Distance metric learning for large margin nearest neighbor classifcation"
ref_type: npl
severity: background
ref_id: - H. VINCENT POOR: "An Introduction to Signal Detection and Estimation", 2013, SPRINGER-VERLAG
ref_type: npl
severity: background
ref_id: - ALEXEY KURAKINIAN J. GOODFELLOWSAMY BENGIO: "Adversarial examples in the physical world", CORR, vol. abs/1607.02533, 2016
ref_type: npl
severity: background
ref_id: - OLGA TARANSLAVI BONEVTARAS HOLOTYAKSLAVA VOLOSHYNOVSKIY: "Adversarial detection of counterfeited printable graphical codes: Towards ''adversarial games'' in physical world", ICASSP 2020 -2020 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP, 2020, pages 2812 - 2816, XP033794258, DOI: 10.1109/ICASSP40776.2020.9054604
ref_type: npl
severity: background
ref_id: - NEIL. A. MACMILLANC. DOUGLAS. CREELMAN: "Detection Theory -A user's guide", 2005, LAWRENCE ERLBAUM ASSOCIATES
ref_type: npl
severity: background
ref_id: - DIEDERIK P. KINGMAMAX WELLING: "Auto-encoding variational bayes", 2ND INTERNATIONAL CONFERENCE ON LEARNING REPRESENTATIONS, ICLR 2014, BAN , AB, CANADA, APRIL 14-16, 2014, CONFERENCE TRACK PROCEEDINGS, 14 April 2014 (2014-04-14)
ref_type: npl
severity: background
ref_id: - ALIREZA MAKHZANIJONATHON SHLENSNAVDEEP JAITLYIAN J. GOODFELLOW: "Adversarial autoencoders", CORR, vol. a bs/1511.05644, 2015
ref_type: npl
severity: background
ref_id: - LUKAS RUROBERT VANDERMEULENNICO GOERNITZLUCAS DEECKESHOAIB AHMED SIDDIQUIALEXANDER BINDEREMMANUEL M'IILLERMARIUS KLOFT: "Proceedings of the 35th International Conference on Machine Learning", vol. 80, 10 July 2018, PMLR, article "Mutual information neural estimation", pages: 4393 - 4402
ref_type: npl
severity: background
ref_id: - MOHAMMAD SABOKROUMOHAMMAD KHALOOEIMAHMOOD FATHYEHSAN ADELI: "CVPR", 2018, IEEE COMPUTER SOCIETY, article "Adversarially learned one-class classifer for novelty detection.", pages: 3379 - 3388
ref_type: npl
severity: background
ref_id: - MOHAMMADREZA SALEHIAINAZ EFTEKHARNIOUSHA SADJADIMOHAMMAD HOSSEIN ROHBANHAMID R. RABIEE: "Puzzle-ae: Novelty detection in images through solving puzzles", CORR, vol. abs/2008.12959, 2020
ref_type: npl
severity: background
ref_id: - KAIMING HEXIANGYU ZHANGSHAOQING RENJIAN SUN: "Deep residual learning for image recognition", 2016 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR, 2016, pages 770 - 778, XP055536240, DOI: 10.1109/CVPR.2016.90
ref_type: npl
severity: background
ref_id: - OLAF RONNEBERGERPHILIPP FISCHERTHOMAS BROX: "U-net: Convolutional networks for biomedical image segmentation", MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION MICCAI 2015, May 2015 (2015-05-01)
ref_type: npl
severity: background
ref_id: - PAUL BERGMANNKILIAN BATZNERMICHAEL FAUSERDAVID SATTLEGGERCARSTEN STEGER: "The mvtec anomaly detection dataset: A comprehensive real-world dataset for unsupervised anomaly detection", INTERNATIONAL JOURNAL OF COMPUTER VISION, vol. 129, no. 4, 2021, pages 1038 - 1059, XP037408359, DOI: 10.1007/s11263-020-01400-4
ref_type: npl
severity: background
ref_id: - NOBUYUKI OTSU: "A threshold selection method from gray-level histograms", IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS, vol. 9, no. 1, 1979, pages 62 - 66, XP002386459
ref_type: npl
severity: background
ref_id: - YUNQIANG CHENXIANG SEAN ZHOUT.S. HUANG: "One-class svm for learning in image retrieval", PROCEEDINGS 2001 INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (CAT. NO.01CH37205, vol. 1, 2001, pages 34 - 37, XP010564789, DOI: 10.1109/ICIP.2001.958946
ref_type: npl
severity: background
ref_id: - SLAVA VOLOSHYNOVSKIYMOUAD KONDAHSHIDEH REZAEIFAROLGA TARANTARAS HOTOLYAKDANILO JIMENEZ REZENDE: "Information bottleneck through variational glasses", NEURLPS WORKSHOP ON BAYESIAN DEEP LEARNING, VANCOUVER, CANADA, December 2019 (2019-12-01)
ref_type: npl
severity: background
ref_id: - IAN J. GOODFELLOWJEAN POUGET-ABADIEMEHDI MIRZABING XUDAVID WARDE-FARLEYSHERJIL OZAIRAARON COURVILLEYOSHUA BENGIO: "Proceedings of the 27th International Conference on Neural Information Processing Systems", vol. 2, 2014, MIT PRESS, article "Generative adversarial nets", pages: 26722680
ref_type: npl
severity: background
ref_id: - MASASHI SUGIYAMATAIJI SUZUKITAKAFUMI KANAMORI: "Density Ratio Estimation in Machine Learning", 2012, CAMBRIDGE UNIVERSITY PRESS
ref_type: npl
severity: background
ref_id: - SEBASTIAN NOWOZINBOTOND CSEKERYOTA TOMIOKA, F-GAN: TRAINING GENERATIVE NEURAL SAMPLERS USING VARIATIONAL DIVERGENCE MINIMIZATION, 2016
ref_type: npl
severity: background
ref_id: - MASASHI SUGIYAMATAIJI SUZUKITAKAFUMI KANAMORI: "Density-ratio matching under the Bregman divergence: a unifed framework of density-ratio estimation", ANNALS OF THE INSTITUTE OF STATISTICAL MATHEMATICS, vol. 64, no. 5, October 2012 (2012-10-01), pages 1009 - 1044, XP035091359, DOI: 10.1007/s10463-011-0343-8
ref_type: npl
severity: background
ref_id: - MARTIN ARJOVSKYSOUMITH CHINTALALON BOTTOU: "Wasserstein gan", ARXIV:1701.07875, 2017
ref_type: npl
severity: background
ref_id: - KACPER CHWIALKOWSKIAADITYA RAMDASDINO SEJDINOVICARTHUR GRETTON: "Proceedings of the 28th International Conference on Neural Information Processing Systems", vol. 2, 2015, MIT PRESS, article "Fast two-sample testing with analytic representations of probability measures", pages: 19811989
ref_type: npl
severity: background
ref_id: - PRANNAY KHOSLAPIOTR TETERWAKCHEN WANGAARON SARNAYONGLONG TIANPHILLIP ISOLAAARON MASCHINOTCE LIUDILIP KRISHNAN: "Advances in Neural Information Processing Systems", vol. 32, 2019, CURRAN ASSOCIATES, INC, article "Improved deep metric learning with multi-class n-pair loss objective", pages: 18661 - 18673
ref_type: npl
severity: background
ref_id: - TING CHENSIMON KORNBLITHMOHAMMAD NOROUZIGEO REY HINTON: "Proceedings of the 37th International Conference on Machine Learning", vol. 119, 13 July 2020, PMLR, article "A simple framework for contrastive learning of visual representations", pages: 1597 - 1607
ref_type: npl
severity: background
ref_id: - ALESSANDRO FOIMEJDI TRIMECHEVLADIMIR KATKOVNIKKAREN EGIAZARIAN: "Practical poissonian-gaussian noise modeling and ftting for single-image raw- data", IEEE TRANSACTIONS ON IMAGE PROCESSING, vol. 17, no. 10, 2008, pages 1737 - 1754
ref_type: npl
severity: background
ref_id: - ARTHUR GRETTONKARSTEN M. BORGWARDTMALTE J. RASCHBERNHARD SCHOLKOPFALEXANDER SMOLA: "A kernel two-sample test", J. MACH. LEARN. RES., vol. 13, no. null, March 2012 (2012-03-01), pages 723773
ref_type: npl
severity: background
ref_id: - GEORGE PAPAMAKARIOSERIC T. NALISNICKDANILO JIMENEZ REZENDESHAKIR MOHAMEDBALAJI LAKSHMINARAYANAN: "Normalizing flows for probabilistic modeling and inference", CORR, vol. abs/1912.02762, 2019
ref_type: npl
severity: background
ref_id: - OZA POOJAN ET AL: "Active Authentication using an Autoencoder regularized CNN-based One-Class Classifier", 2019 14TH IEEE INTERNATIONAL CONFERENCE ON AUTOMATIC FACE & GESTURE RECOGNITION (FG 2019), IEEE, 14 May 2019 (2019-05-14), pages 1 - 8, XP033575955, DOI: 10.1109/FG.2019.8756525
ref_type: npl
severity: background
ref_id: - BAWEJA YASHASVI ET AL: "Anomaly Detection-Based Unknown Face Presentation Attack Detection", 2020 IEEE INTERNATIONAL JOINT CONFERENCE ON BIOMETRICS (IJCB), IEEE, 28 September 2020 (2020-09-28), pages 1 - 9, XP033871342, DOI: 10.1109/IJCB48548.2020.9304935
ref_type: npl
severity: background
ref_id: - PRAMUDITHA PERERA ET AL: "One-Class Classification: A Survey", ARXIV.ORG, CORNELL UNIVERSITY LIBRARY, 201 OLIN LIBRARY CORNELL UNIVERSITY ITHACA, NY 14853, 8 January 2021 (2021-01-08), XP081855203
ref_type: npl
severity: background
ref_id: - OZA POOJAN ET AL, "Active Authentication using an Autoencoder regularized CNN-based One-Class Classifier", 2019 14TH IEEE INTERNATIONAL CONFERENCE ON AUTOMATIC FACE & GESTURE RECOGNITION (FG 2019), IEEE, (20190514), doi:10.1109/FG.2019.8756525, pages 1 - 8, XP033575955 [I] 1-3,7-11,20-25 * the whole document * [Y] 15-17
ref_type: npl
severity: obviousness
ref_id: - BAWEJA YASHASVI ET AL, "Anomaly Detection-Based Unknown Face Presentation Attack Detection", 2020 IEEE INTERNATIONAL JOINT CONFERENCE ON BIOMETRICS (IJCB), IEEE, (20200928), doi:10.1109/IJCB48548.2020.9304935, pages 1 - 9, XP033871342 [Y] 15-17 * the whole document *
ref_type: npl
severity: obviousness
ref_id: - PRAMUDITHA PERERA ET AL, "One-Class Classification: A Survey", ARXIV.ORG, CORNELL UNIVERSITY LIBRARY, 201 OLIN LIBRARY CORNELL UNIVERSITY ITHACA, NY 14853, (20210108), XP081855203 [A] 1-25 * the whole document *
ref_type: npl
severity: background
Final disposition: pending (2022-12-14)