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AN EXPANDED GENETICALLY ENCODED VOLTAGE INDICATOR TOOLBOX FOR DEEP, RAPID, AND PROLONGED VOLTAGE RECORDINGS IN VIVO

IBRO Neuroscience Reports 2023年 15卷 S264-S265页

作者： Shuyuan Yang Xiaoyu Lu Michelle Land Zhuohe Liu Alex Mcdonald Yueyang Gou Yunmiao Wang Elsayed Zaabout Xiaoyu Dong Shujuan Lai Dieter Jaeger Jacob Reimer François St-Pierre Rice University Department Of Chemical And Biomolecular Engineering Houston United States of America Rice University Systems Synthetic And Physical Biology Program Houston United States of America Baylor College of Medicine Department Of Neuroscience Houston United States of America Emory University Department Of Biology Atlanta United States of America Emory University Neuroscience Graduate Program Atlanta United States of America Baylor College of Medicine Center For Neuroscience And Artificial Intelligence Houston United States of America Rice University Department Of Electrical And Computer Engineering Houston United States of America Baylor College of Medicine Department Of Biochemistry And Molecular Biology Houston United States of America

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Current Progress and Challenges in Large-scale 3D Mitochondria Instance Segmentation

TechRxiv

引用

TechRxiv 2023年

作者： Franco-Barranco, Daniel Lin, Zudi Jang, Won-Dong Wang, Xueying Shen, Qijia Yin, Wenjie Fan, Yutian Li, Mingxing Chen, Chang Xiong, Zhiwei Xin, Rui Liu, Hao Chen, Huai Li, Zhili Zhao, Jie Chen, Xuejin Pape, Constantin Conrad, Ryan De Folter, Jozefus Nightingale, Luke Jones, Martin L. Liu, Yanling Ziaei, Dorsa Huschauer, Stephan Arganda-Carreras, Ignacio Pfister, Hanspeter Wei, Donglai The Department of Computer Science and Artificial Intelligence University of the Basque Country Donostia-San Sebastian Spain San Sebastian Spain Ikerbasque Basque Foundation for Science Bilbao Spain Biofisika Institute CSIC UPV/EHU Bilbao Spain Harvard University All-ston MA United States The Department of Molecular and Cellular Biology Harvard University CambridgeMA United States The Wellcome Centre for Integrative Neuroimaging FMRIB Nuffield Department of Clinical Neurosciences University of Oxford Oxford United Kingdom University of Science and Technology of China Anhui China The Institute of Image Processing and Pattern Recognition Department of Automation Shanghai Jiao Tong University Shanghai China The National Engineering Laboratory for Brain-inspired Intelligence Technology and Application University of Science and Technology of China Anhui China The Georg-August University Goettingen Germany The Center for Molecular Microscopy Center for Cancer Research National Cancer Institute National Institutes of Health Bethesda United States The Cancer Research Technology Program Frederick National Laboratory for Cancer Research Frederick United States The Francis Crick Institute London United Kingdom The Advanced Biomedical Computational Science Group Frederick National Laboratory for Cancer Research FrederickMD United States The Computer Science Department Boston College Chestnut Hill MA United States

In this paper, we present the results of the MitoEM challenge on mitochondria 3D instance segmentation from electron microscopy images, organized in conjunction with the IEEE-ISBI 2021 conference. Our benchmark dataset consists of two large-scale 3D volumes, one from human and one from rat cortex tissue, which are 3,600 times larger than previously used datasets. At the time of paper submission, 257 participants had registered for the challenge, 14 teams had submitted their results, and six teams participated in the challenge workshop. Here, we present eight top-performing approaches from the challenge participants, along with our own baseline strategies. Posterior to the challenge, annotation errors in the ground truth were corrected without altering the final ranking. Additionally, we present a retrospective evaluation of the scoring system which revealed that (1) the challenge metric was permissive with the false positive predictions and (2) the size-based grouping of instances did not correctly categorize mitochondria of interest. Thus, we propose a new scoring system that better reflects the correctness of the segmentation results. Although several of the top methods are compared favorably to our own baselines, substantial errors remain unsolved for mitochondria with challenging morphologies. Thus, the challenge remains open for submission and automatic evaluation, with all volumes available for download. © 2023, CC BY.

关键词： Electron microscopy

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Nonreciprocal transport in a bilayer of MnBi2Te4 and Pt

arXiv

引用

arXiv 2022年

作者： Ye, Chen Xie, Xiangnan Lv, Wenxing Huang, Ke Yang, Allen Jian Jiang, Sicong Liu, Xue Zhu, Dapeng Qiu, Xuepeng Tong, Mingyu Zhou, Tong Hsu, Chuang-Han Chang, Guoqing Lin, Hsin Li, Peisen Yang, Kesong Wang, Zhenyu Jiang, Tian Wang, Xiao Renshaw Division of Physics and Applied Physics School of Physical and Mathematical Sciences Nanyang Technological University 21 Nanyang Link 637371 Singapore State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha410073 China Physics Laboratory Industrial Training Center Shenzhen Polytechnic Guangdong Shenzhen518055 China Department of NanoEngineering Program of Chemical Engineering University of California La Jolla San DiegoCA92093-0448 United States Program of Materials Science and Engineering University of California La Jolla San DiegoCA92093-0418 United States Institutes of Physical Science and Information Technology Anhui University Hefei230601 China Fert Beijing Institute MIIT Key Laboratory of Spintronics School of Integrated Circuit Science and Engineering Beihang University Beijing100191 China Beihang-Goertek Joint Microelectronics Institute Qingdao Research Institute Beihang University Qingdao266000 China Shanghai Key Laboratory of Special Artificial Microstructure Materials and Technology School of Physics Science and Engineering Tongji University Shanghai200092 China College of Advanced Interdisciplinary Studies National University of Defense Technology Changsha410073 China Insitute of Physics Academia Sinica Taipei115229 Taiwan College of Intelligence Science and Technology National University of Defense Technology Changsha410073 China National Innovation Institute of Defense Technology Academy of Military Sciences PLA China Beijing100010 China Beijing Academy of Quantum Information Sciences Beijing100084 China Beijing Institute for Advanced Study National University of Defense Technology Changsha410073 China School of Electrical and Electronic Engineering Nanyang Technological University 50 Nanyang Ave 639798 Singapore

MnBi2Te4 (MBT) is the first intrinsic magnetic topological insulator with the interaction of spin-momentum locked surface electrons and intrinsic magnetism, and it exhibits novel magnetic and topological phenomena. Recent studies suggested that the interaction of electrons and magnetism can be affected by the Mn-doped Bi2Te3 phase at the surface due to inevitable structural defects. Here we report an observation of nonreciprocal transport, i.e. current-direction-dependent resistance, in a bilayer composed of antiferromagnetic MBT and nonmagnetic Pt. The emergence of the nonreciprocal response below the Néel temperature confirms a correlation between nonreciprocity and intrinsic magnetism in the surface state of MBT. The angular dependence of the nonreciprocal transport indicates that nonreciprocal response originates from the asymmetry scattering of electrons at the surface of MBT mediated by magnon. Our work provides an insight into nonreciprocity arising from the correlation between magnetism and Dirac surface electrons in intrinsic magnetic topological insulators. © 2022, CC BY.

关键词： Magnetism

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FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

arXiv

引用

arXiv 2023年

作者： Lekadir, Karim Feragen, Aasa Fofanah, Abdul Joseph Frangi, Alejandro F. Buyx, Alena Emelie, Anais Lara, Andrea Porras, Antonio R. Chan, An-Wen Navarro, Arcadi Glocker, Ben Botwe, Benard O. Khanal, Bishesh Beger, Brigit Wu, Carol C. Cintas, Celia Langlotz, Curtis P. Rueckert, Daniel Mzurikwao, Deogratias Fotiadis, Dimitrios I. Zhussupov, Doszhan Ferrante, Enzo Meijering, Erik Weicken, Eva González, Fabio A. Asselbergs, Folkert W. Prior, Fred Krestin, Gabriel P. Collins, Gary S. Tegenaw, Geletaw S. Kaissis, Georgios Misuraca, Gianluca Tsakou, Gianna Dwivedi, Girish Kondylakis, Haridimos Jayakody, Harsha Woodruf, Henry C. Mayer, Horst Joachim Aerts, Hugo JWL Walsh, Ian Chouvarda, Ioanna Buvat, Irène Tributsch, Isabell Rekik, Islem Duncan, James Kalpathy-Cramer, Jayashree Zahir, Jihad Park, Jinah Mongan, John Gichoya, Judy W. Schnabel, Julia A. Kushibar, Kaisar Riklund, Katrine Mori, Kensaku Marias, Kostas Amugongo, Lameck M. Fromont, Lauren A. Maier-Hein, Lena Alberich, Leonor Cerdá Rittner, Leticia Phiri, Lighton Marrakchi-Kacem, Linda Donoso-Bach, Lluís Martí-Bonmatí, Luis Cardoso, M. Jorge Bobowicz, Maciej Shabani, Mahsa Tsiknakis, Manolis Zuluaga, Maria A. Bielikova, Maria Fritzsche, Marie-Christine Camacho, Marina Linguraru, Marius George Wenzel, Markus De Bruijne, Marleen Tolsgaard, Martin G. Ghassemi, Marzyeh Ashrafuzzaman, Md Goisauf, Melanie Yaqub, Mohammad Abadía, Mónica Cano Mahmoud, Mukhtar M.E. Elattar, Mustafa Rieke, Nicola Papanikolaou, Nikolaos Lazrak, Noussair Díaz, Oliver Salvado, Olivier Pujol, Oriol Sall, Ousmane Guevara, Pamela Gordebeke, Peter Lambin, Philippe Brown, Pieta Abolmaesumi, Purang Dou, Qi Lu, Qinghua Osuala, Richard Nakasi, Rose Zhou, S. Kevin Napel, Sandy Colantonio, Sara Albarqouni, Shadi Joshi, Smriti Carter, Stacy Klein, Stefan Petersen, Steffen E. Aussó, Susanna Awate, Suyash Raviv, Tammy Riklin Cook, Tessa Mutsvangwa, Tinashe E.M. Rogers, Wendy A. Niessen, Wiro J. Puig-Bosch, Xènia Zeng, Yi Mohammed, Yunusa G. Aquino, Yves Saint James Salahuddin, Zohaib Starmans, Martijn P.A. Department de Matemàtiques i Informàtica Universitat de Barcelona Barcelona Spain Barcelona Spain DTU Compute Technical University of Denmark Kgs Lyngby Denmark Department of Mathematics and Computer Science Faculty of Science and Technology Milton Margai Technical University Freetown Sierra Leone Center for Computational Imaging & Simulation Technologies in Biomedicine Schools of Computing and Medicine University of Leeds Leeds United Kingdom Cardiovascular Science and Electronic Engineering Departments KU Leuven Leuven Belgium Institute of History and Ethics in Medicine Technical University of Munich Munich Germany Faculty of Engineering of Systems Informatics and Sciences of Computing Galileo University Guatemala City Guatemala Department of Biostatistics and Informatics Colorado School of Public Health University of Colorado Anschutz Medical Campus AuroraCO United States Department of Medicine Women’s College Research Institute University of Toronto Toronto Canada Universitat Pompeu Fabra BarcelonaBeta Brain Research Center Barcelona Spain Department of Computing Imperial College London London United Kingdom School of Biomedical & Allied Health Sciences University of Ghana Accra Ghana Department of Midwifery & Radiography School of Health & Psychological Sciences City University of London United Kingdom Kathmandu Nepal European Heart Network Brussels Belgium Department of Thoracic Imaging University of Texas MD Anderson Cancer Center Houston United States IBM Research Africa Nairobi Kenya Departments of Radiology Medicine and Biomedical Data Science Stanford University School of Medicine Stanford United States Institute for AI and Informatics in Medicine Klinikum rechts der Isar Technical University Munich Munich Germany Department of Computing Imperial College London London United Kingdom Muhimbili University of Health and Allied Sciences Dar es Salaam Tanzania United Republic of Ioannina Greece Almaty AI Lab Almaty Kazakhstan

Background: Despite major advances in artificial intelligence (AI) research for healthcare, the deployment and adoption of AI technologies remain limited in clinical practice. In recent years, concerns have been raised about the clinical, technical, ethical and legal risks associated with healthcare AI. To increase adoption in the real world, it is essential that AI technologies are trusted and accepted by patients, clinicians, health organisations and authorities. This paper describes the FUTURE-AI framework as the first international consensus guideline for trustworthy AI in healthcare. Methods: The FUTURE-AI consortium was founded in 2021 and now comprises 117 interdisciplinary experts from 50 countries representing all continents, including AI scientists, clinical researchers, biomedical ethicists, and social scientists. Over a two-year period, the consortium established guiding principles and best practices for trustworthy and deployable AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. Findings: The FUTURE-AI framework was established based on six guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 30 best practices were defined, addressing technical, clinical, socio-ethical and legal dimensions of trustworthy AI. The recommendations cover the entire lifecycle of healthcare AI, from design, development and validation to regulation, deployment, and monitoring. Interpretation: FUTURE-AI is a structured, risk-informed framework which provides guidance for constructing healthcare AI tools that will be trusted, deployed and adopted in real-world clinical practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of healthcare AI. Copyright © 2023, The Authors. All rights

关键词： Clinical research

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RRNet: Repetition-reduction network for energy efficient decoder of depth estimation

arXiv

引用

arXiv 2019年

作者： Oh, Sangyun Kim, Hye-Jin S. Lee, Jongeun Kim, Junmo School of Electrical and Computer Engineering UNIST Ulsan Korea Republic of Neural Processing Research Center Seoul National University Seoul Korea Republic of Robotics Program School of Electrical Engineering KAIST Daejeon Korea Republic of Artificial Intelligence Laboratory ETRI Daejeon Korea Republic of

We introduce Repetition-Reduction network (RRNet) for resource-constrained depth estimation, offering significantly improved efficiency in terms of computation, memory and energy consumption. The proposed method is based on repetition-reduction (RR) blocks. The RR blocks consist of the set of repeated convolutions and the residual connection layer that take place of the pointwise reduction layer with linear connection to the decoder. The RRNet help reduce memory usage and power consumption in the residual connections to the decoder layers. RRNet consumes approximately 3.84 times less energy and 3.06 times less memory and is approaximately 2.21 times faster, without increasing the demand on hardware resource relative to the baseline network [1], outperforming current state-of-the-art lightweight architectures such as SqueezeNet [2], ShuffleNet [3], MobileNetv2 [4] and PyDNet [5]. Copyright © 2019, The Authors. All rights reserved.

关键词： Energy utilization

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A Real-time and Online Multiple-Type Object Tracking Method with Deep Features

A Real-time and Online Multiple-Type Object Tracking Method ...

引用

Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)

作者： Yi-Hsuan Hsu Jiun-In Guo Graduate Degree Program of College of Electrical and Computer Engineering National Chiao Tung University 1001 University Road Hsinchu City Taiwan R.O.C. Pervasive Artificial Intelligence Research Labs (PAIR Labs)

ISBN: (数字)9781728132488

ISBN: (纸本)9781728132495

Object tracking is one of the most important things in intelligent vision system. Meanwhile, the most challenging issue in object tracking is how to keep the target's identity unchangeable with limited power consumption. In this paper, we propose a real-time and online tracking method to track multiple types of objects (e.g. pedestrian and car). Furthermore, to handle the ID switching problem, we provide a lightweight deep learning model which can recognize the similarity of objects. It can effectively solve the ID switching problem resulted from occlusion. Finally, we do some experiments to demonstrate that the proposed method achieves the state-of-the-art performance with less power consumption. The proposed method can solve the problem of high computation of tracking and keep the high accuracy of counting results with low ID switching rate. The experimental result shows that the average counting accuracy of the proposed method can reach more than 93% on pedestrian and vehicle counting applications. Also, it shows that the proposed method improves 68.2% on average of ID switching rate than previous works.

关键词： Target tracking Switches Machine learning Computational modeling Detectors Object tracking Feature extraction

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ThermalProGAN: A sequence-based thermally stable protein generator trained using unpaired data

引用

Journal of bioinformatics and computational biology 2023年第1期21卷 2350008页

作者： Hui-Ling Huang Chong-Heng Weng Torbjörn E M Nordling Yi-Fan Liou International Program of Health Informatics and Management College of Management Chang Gung University No. 259 Wenhua 1st Road Guishan District Taoyuan City 33302 Taiwan. Department of Computer Science and Information Engineering National Central University No. 300 Zhongda Road Zhongli District Taoyuan City 320317 Taiwan. Department of Mechanical Engineering National Cheng Kung University No. 1 University Road Tainan City 701 Taiwan. Department of Applied Physics and Electronics Umeå University 90187 Umeå Sweden. Department of Virtual-Reality Interaction with Artificial Intelligence Technology Coretronic Reality Incorporation No. 5 Wenhua Rd. Hukou Township Hsinchu County 303 Taiwan.

MOTIVATION:The synthesis of proteins with novel desired properties is challenging but sought after by the industry and academia. The dominating approach is based on trial-and-error inducing point mutations, assisted by structural information or predictive models built with paired data that are difficult to collect. This study proposes a sequence-based unpaired-sample of novel protein inventor (SUNI) to build ThermalProGAN for generating thermally stable proteins based on sequence information. RESULTS:The ThermalProGAN can strongly mutate the input sequence with a median number of 32 residues. A known normal protein, 1RG0, was used to generate a thermally stable form by mutating 51 residues. After superimposing the two structures, high similarity is shown, indicating that the basic function would be conserved. Eighty four molecular dynamics simulation results of 1RG0 and the COVID-19 vaccine candidates with a total simulation time of 840[Formula: see text]ns indicate that the thermal stability increased. CONCLUSION:This proof of concept demonstrated that transfer of a desired protein property from one set of proteins is feasible. The source code of ThermalProGAN can be freely accessed at https://***/markliou/ThermalProGAN/ with an MIT license. The website is https://***:433. Supplementary data are available on Github.

关键词： CycleGAN Protein synthesis generative adversarial neural network thermal stability

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Sex Differences in Heart Failure Following Acute Coronary Syndromes

JACC: Advances

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JACC: Advances 2023年第3期2卷 100294-100294页

作者： Cenko, Edina Manfrini, Olivia Yoon, Jinsung van der Schaar, Mihaela Bergami, Maria Vasiljevic, Zorana Mendieta, Guiomar Stankovic, Goran Vavlukis, Marija Kedev, Sasko Miličić, Davor Badimon, Lina Bugiardini, Raffaele Laboratory of Epidemiological and Clinical Cardiology Department of Medical and Surgical Sciences University of Bologna Bologna Italy IRCCS Azienda Ospedaliero-Universitaria di Bologna Sant'Orsola Hospital Bologna Italy Google Cloud AI Sunnyvale CA United States Department of Electrical and Computer Engineering University of California Los Angeles Los Angeles CA United States Department of Applied Mathematics and Theoretical Physics and Department of Population Health Cambridge Centre for Artificial Intelligence in Medicine University of Cambridge Cambridge United Kingdom Medical Faculty University of Belgrade Belgrade Serbia Centro Nacional de Investigaciones Cardiovasculares Carlos III (CNIC) Madrid Spain Clinic of Cardiology University Clinical Centre of Serbia Belgrade Serbia University Clinic for Cardiology Skopje North Macedonia Faculty of Medicine Ss. Cyril and Methodius University in Skopje Skopje North Macedonia Department for Cardiovascular Diseases University Hospital Center Zagreb University of Zagreb Zagreb Croatia Cardiovascular Research Program ICCC IR-IIB Sant Pau Hospital de la Santa Creu i Sant Pau CiberCV-Institute Carlos III Barcelona Spain

Background: There have been conflicting reports regarding outcomes in women presenting with an acute coronary syndrome (ACS). Objectives: The objective of the study was to examine sex-specific differences in 30-day mortality in patients with ACS and acute heart failure (HF) at the time of presentation. Methods: This was a retrospective study of patients included in the International Survey of Acute Coronary Syndromes-ARCHIVES (ISACS-ARCHIVES;NCT04008173). Acute HF was defined as Killip classes ≥2. Participants were stratified according to ACS presentation: ST-segment elevation myocardial infarction (STEMI) and non-ST-segment elevation ACS (NSTE-ACS). Differences in 30-day mortality and acute HF presentation at admission between sexes were examined using inverse propensity weighting based on the propensity score. Estimates were compared by test of interaction on the log scale. Results: A total of 87,812 patients were included, of whom 30,922 (35.2%) were women. Mortality was higher in women compared with men in those presenting with STEMI (risk ratio [RR]: 1.65;95% CI: 1.56-1.73) and NSTE-ACS (RR: 1.18;95% CI: 1.09-1.28;P_interaction <0.001). Acute HF was more common in women when compared to men with STEMI (RR: 1.24;95% CI: 1.20-1.29) but not in those with NSTE-ACS (RR: 1.02;95% CI: 0.97-1.08) (P_interaction <0.001). The presence of acute HF increased the risk of mortality for both sexes (odds ratio: 6.60;95% CI: 6.25-6.98). Conclusions: In patients presenting with ACS, mortality is higher in women. The presence of acute HF at hospital presentation increases the risk of mortality in both sexes. Women with STEMI are more likely to present with acute HF and this may, in part, explain sex differences in mortality. These findings may be helpful to improve sex-specific personalized risk stratification. © 2023 The Authors

关键词： acute heart failure outcomes sex differences

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Stratifying vascular disease patients into homogeneous subgroups using machine learning and FLAIR MRI biomarkers

Npj imaging

引用

Npj imaging 2024年第1期2卷 56页

作者： Karissa Chan Corinne Fischer Pejman Jabehdar Maralani Sandra E Black Alan R Moody April Khademi Department of Electrical Computer and Biomedical Engineering Toronto Metropolitan University Toronto ON Canada. Institute for Biomedical Engineering Science Tech (iBEST) a Partnership between St. Michael's Hospital and Toronto Metropolitan University Toronto ON Canada. Keenan Research Center for Biomedical Science St. Michael's Hospital Unity Health Network Toronto ON Canada. Department of Medical Imaging University of Toronto Toronto ON Canada. Institute of Medical Science University of Toronto Toronto ON Canada. Horvitz Brain Sciences Research Program Sunnybrook Research Institute Toronto ON Canada. Division of Neurology Department of Medicine Sunnybrook Health Sciences Centre Toronto ON Canada. L.C. Campbell Cognitive Neurology Research Unit Sunnybrook Health Sciences Centre Toronto ON Canada. Vector Institute for Artificial Intelligence Toronto ON Canada.

This study proposes a framework to stratify vascular disease patients based on brain health and cerebrovascular disease (CVD) risk using regional FLAIR biomarkers. Intensity and texture biomarkers were extracted from FLAIR volumes of 379 atherosclerosis patients. K-Means clustering identified five homogeneous subgroups. The 15 most important biomarkers for subgroup differentiation, identified via Random Forest classification, were used to generate biomarker profiles. ANOVA tests showed age and white matter lesion volume were significantly ( < 0.05) different across subgroups, while Fisher's tests revealed significant ( < 0.05) differences in the prevalence of several vascular risk factors across subgroup. Based on biomarker and clinical profiles, Subgroup 4 was characterized with neurodegeneration unrelated to CVD, Subgroup 3 identified patients with high CVD risk requiring aggressive intervention, and Subgroups 1, 2, and 5 identified patients with varying levels of moderate risk, suitable for long-term lifestyle interventions. This study supports personalized treatment and risk stratification based on FLAIR biomarkers.

关键词： Brain imaging Image processing

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PixelSteganalysis: Pixel-wise Hidden Information Removal with Low Visual Degradation

arXiv

引用

arXiv 2019年

作者： Jung, Dahuin Bae, Ho Choi, Hyun-Soo Yoon, Sungroh The Dept. of Electrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of Dept. of Cyber Security Ewha Womans University Seoul03760 Korea Republic of Dept. of Computer Science & Engineering Interdisciplinary Graduate Program in Medical Bigdata Convergence Kangwon National University Chuncheon 24341 Korea Republic of Ziovision Chuncheon24341 Korea Republic of Interdisciplinary Program in Artificial Intelligence Seoul National University Seoul08826 Korea Republic of

Recently, the field of steganography has experienced rapid developments based on deep learning (DL). DL based steganography distributes secret information over all the available bits of the cover image, thereby posing difficulties in using conventional steganalysis methods to detect, extract or remove hidden secret images. However, our proposed framework is the first to effectively disable covert communications and transactions that use DL based steganography. We propose a DL based steganalysis technique that effectively removes secret images by restoring the distribution of the original images. We formulate a problem and address it by exploiting sophisticated pixel distributions and an edge distribution of images by using a deep neural network. Based on the given information, we remove the hidden secret information at the pixel level. We evaluate our technique by comparing it with conventional steganalysis methods using three public benchmarks. As the decoding method of DL based steganography is approximate (lossy) and is different from the decoding method of conventional steganography, we also introduce a new quantitative metric called the destruction rate (DT). The experimental results demonstrate performance improvements of 10-20% in both the decoded rate and the DT. Copyright © 2019, The Authors. All rights reserved.

关键词： Steganography

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