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Fair Evaluation of Federated Learning Algorithms for Automated Breast Density Classification: The Results of the 2022 ACR-NCI-NVIDIA Federated Learning Challenge

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arXiv 2024年

作者： Schmidt, Kendall Bearce, Benjamin Chang, Ken Coombs, Laura Farahani, Keyvan Elbatel, Marawan Mouheb, Kaouther Marti, Robert Zhang, Ruipeng Zhang, Yao Wang, Yanfeng Hu, Yaojun Ying, Haochao Xu, Yuyang Testagrose, Conrad Demirer, Mutlu Gupta, Vikash Akünal, Ünal Bujotzek, Markus Maier-Hein, Klaus H. Qin, Yi Li, Xiaomeng Kalpathy-Cramer, Jayashree Roth, Holger R. American College of Radiology United States The Massachusetts General Hospital United States University of Colorado United States National Institutes of Health National Cancer Institute United States Computer Vision and Robotics Institute University of Girona Spain Cooperative Medianet Innovation Center Shanghai Jiao Tong University China Shanghai AI Laboratory China Real Doctor AI Research Centre Zhejiang University China School of Public Health Zhejiang University China College of Computer Science and Technology Zhejiang University China University of North Florida College of Computing Jacksonville United States Mayo Clinic Florida Radiology United States Division of Medical Image Computing German Cancer Research Center Heidelberg Germany Electronic and Computer Engineering Hong Kong University of Science and Technology China NVIDIA United States

The correct interpretation of breast density is important in the assessment of breast cancer risk. AI has been shown capable of accurately predicting breast density, however, due to the differences in imaging characteristics across mammography systems, models built using data from one system do not generalize well to other systems. Though federated learning (FL) has emerged as a way to improve the generalizability of AI without the need to share data, the best way to preserve features from all training data during FL is an active area of research. To explore FL methodology, the breast density classification FL challenge was hosted in partnership with the American College of Radiology, Harvard Medical Schools' Mass General Brigham, University of Colorado, NVIDIA, and the National Institutes of Health National Cancer Institute. Challenge participants were able to submit docker containers capable of implementing FL on three simulated medical facilities, each containing a unique large mammography dataset. The breast density FL challenge ran from June 15 to September 5, 2022, attracting seven finalists from around the world. The winning FL submission reached a linear kappa score of 0.653 on the challenge test data and 0.413 on an external testing dataset, scoring comparably to a model trained on the same data in a central location. © 2024, CC BY.

关键词： Learning algorithms

Towards Reliable Medical Image Segmentation by utilizing Evidential Calibrated Uncertainty

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arXiv 2023年

作者： Zou, Ke Chen, Yidi Huang, Ling Yuan, Xuedong Shen, Xiaojing Wang, Meng Goh, Rick Siow Mong Liu, Yong Fu, Huazhu National Key Laboratory of Fundamental Science on Synthetic Vision Sichuan University Chengdu China College of Computer Science Sichuan University Chengdu China College of Mathematics Sichuan University Chengdu China Department of Radiology West China Hospital Sichuan University Chengdu China Saw Swee Hock School of Public Health National University of Singapore Singapore Institute of High Performance Computing Agency for Science Technology and Research Singapore

Medical image segmentation is critical for disease diagnosis and treatment assessment. However, concerns regarding the reliability of segmentation regions persist among clinicians, mainly attributed to the absence of confidence assessment, robustness, and calibration to accuracy. To address this, we introduce DEviS, an easily implementable foundational model that seamlessly integrates into various medical image segmentation networks. DEviS not only enhances the calibration and robustness of baseline segmentation accuracy but also provides high-efficiency uncertainty estimation for reliable predictions. By leveraging subjective logic theory, we explicitly model probability and uncertainty for the problem of medical image segmentation. Here, the Dirichlet distribution parameterizes the distribution of probabilities for different classes of the segmentation results. To generate calibrated predictions and uncertainty, we develop a trainable calibrated uncertainty penalty. Furthermore, DEviS incorporates an uncertainty-aware filtering module, which utilizes the metric of uncertainty-calibrated error to filter reliable data within the dataset. We conducted validation studies to assess both the accuracy and robustness of DEviS segmentation, along with evaluating the efficiency and reliability of uncertainty estimation. These evaluations were performed using publicly available datasets including ISIC2018, LiTS2017, and BraTS2019. Additionally, two potential clinical trials are being conducted at Johns Hopkins OCT, Duke-OCT-DME, and FIVES datasets to demonstrate their efficacy in filtering high-quality or out-of-distribution data. Our code has been released in https://***/Cocofeat/DEviS. © 2023, CC BY.

关键词： Calibration

Line Drawing Guided Progressive Inpainting of Mural Damage

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arXiv 2022年

作者： Li, Luxi Zou, Qin Zhang, Fan Yu, Hongkai Chen, Long Song, Chengfang Huang, Xianfeng Wang, Xiaoguang Li, Qingquan Department of Computer Science Technology United International College of Beijing Normal University HongKong Baptist University Zhuhai China Machine Vision and Robotics Laboratory School of Computer Science Wuhan University Wuhan China State Key Laboratory of Surveying Mapping and Remote Sensing Information Engineering Wuhan University Wuhan China Department of Electrical Engineering and Computer Science Cleveland State University OH United States Institute of Automation Chinese Academy of Sciences Beijing China Cultural Heritage Intelligent Computing Laboratory Wuhan University Wuhan China Guangming Laboratory Shenzhen University Shenzhen China

Mural image inpainting is far less explored compared to its natural image counterpart and remains largely unsolved. Most existing image-inpainting methods tend to take the target image as the only input and directly repair the damage to generate a visually plausible result. These methods obtain high performance in restoration or completion of some pre-defined objects, e.g., human face, fabric texture, and printed texts, etc., however, are not suitable for repairing murals with varying subjects and large damaged areas. Moreover, due to discrete colors in paints, mural inpainting may suffer from apparent color bias. To this end, in this paper, we propose a line drawing guided progressive mural inpainting method. It divides the inpainting process into two steps: structure reconstruction and color correction, implemented by a structure reconstruction network (SRN) and a color correction network (CCN), respectively. In structure reconstruction, SRN utilizes the line drawing as an assistant to achieve large-scale content authenticity and structural stability. In color correction, CCN operates a local color adjustment for missing pixels which reduces the negative effects of color bias and edge jumping. The proposed approach is evaluated against the current state-of-the-art image inpainting methods. Qualitative and quantitative results demonstrate the superiority of the proposed method in mural image inpainting. The codes and data are available at https://***/qinnzou/mural-image-inpainting . © 2022, CC BY-NC-ND.

关键词： Restoration

DPER: Diffusion Prior Driven Neural Representation for Limited Angle and Sparse View CT Reconstruction

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arXiv 2024年

作者： Du, Chenhe Lin, Xiyue Wu, Qing Tian, Xuanyu Su, Ying Luo, Zhe Zheng, Rui Chen, Yang Wei, Hongjiang Zhou, S. Kevin Yu, Jingyi Zhang, Yuyao The School of Information Science and Technology ShanghaiTech University Shanghai China The Department of Critical Care Medicine Zhongshan Hospital Fudan University Shanghai China The Laboratory of Image Science and Technology The School of Computer Science and Engineering The Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Ministry of Education Nanjing210096 China The School of Biomedical Engineering Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China School of Biomedical Engineering Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou215123 China Institute of Computing Technology CAS Beijing100190 China

Limited-angle and sparse-view computed tomography (LACT and SVCT) are crucial for expanding the scope of X-ray CT applications. However, they face challenges due to incomplete data acquisition, resulting in diverse artifacts in the reconstructed CT images. Emerging implicit neural representation (INR) techniques, such as NeRF, NeAT, and NeRP, have shown promise in under-determined CT imaging reconstruction tasks. However, the unsupervised nature of INR architecture imposes limited constraints on the solution space, particularly for the highly ill-posed reconstruction task posed by LACT and ultra-SVCT. In this study, we introduce the Diffusion Prior Driven Neural Representation (DPER), an advanced unsupervised framework designed to address the exceptionally ill-posed CT reconstruction inverse problems. DPER adopts the Half Quadratic Splitting (HQS) algorithm to decompose the inverse problem into data fidelity and distribution prior sub-problems. The two sub-problems are respectively addressed by INR reconstruction scheme and pre-trained score-based diffusion model. This combination first injects the implicit image local consistency prior from INR. Additionally, it effectively augments the feasibility of the solution space for the inverse problem through the generative diffusion model, resulting in increased stability and precision in the solutions. We conduct comprehensive experiments to evaluate the performance of DPER on LACT and ultra-SVCT reconstruction with two public datasets (AAPM and LIDC), an in-house clinical COVID-19 dataset and a public raw projection dataset created by Mayo Clinic. The results show that our method outperforms the state-of-the-art reconstruction methods on in-domain datasets, while achieving significant performance improvements on out-of-domain (OOD) datasets. Copyright © 2024, The Authors. All rights reserved.

关键词： Inverse problems

NTIRE 2023 HR NonHomogeneous Dehazing Challenge Report

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NTIRE 2023 HR NonHomogeneous Dehazing Challenge Report

2023 IEEE/CVF Conference on computer vision and Pattern Recognition Workshops, CVPRW 2023

作者： Ancuti, Codruta O. Ancuti, Cosmin Vasluianu, Florin-Alexandru Timofte, Radu Zhou, Han Dong, Wei Liu, Yangyi Chen, Jun Liu, Huan Li, Liangyan Wu, Zijun Dong, Yubo Li, Yuyan Qiu, Tian He, Yu Lu, Yonghong Wu, Yinwei Jiang, Zhenxiang Liu, Songhua Yang, Xingyi Jing, Yongcheng Benjdira, Bilel Ali, Anas M. Koubaa, Anis Yang, Hao-Hsiang Chen, I-Hsiang Chen, Wei-Ting Huang, Zhi-Kai Chen, Yi-Chung Hsieh, Chia-Hsuan Chang, Hua-En Chiang, Yuan-Chun Kuo, Sy-Yen Guo, Yu Gao, Yuan Liu, Ryan Wen Lu, Yuxu Qu, Jingxiang He, Shengfeng Ren, Wenqi Hoang, Trung Zhang, Haichuan Yazdani, Amirsaeed Monga, Vishal Yang, Lehan Wu, Alex Jiahao Mai, Tiancheng Cong, Xiaofeng Yin, Xuemeng Yin, Xuefei Emad, Hazim Abdallah, Ahmed Yasser, Yahya Elshahat, Dalia Elbaz, Esraa Li, Zhan Kuang, Wenqing Luo, Ziwei Gustafsson, Fredrik K. Zhao, Zheng Sjölund, Jens Schön, Thomas B. Zhang, Zhao Wei, Yanyan Wang, Junhu Zhao, Suiyi Zheng, Huan Guo, Jin Sun, Yangfan Liu, Tianli Hao, Dejun Jiang, Kui Sarvaiya, Anjali Prajapati, Kalpesh Patra, Ratnadeep Barik, Pragnesh Rathod, Chaitanya Upla, Kishor Raja, Kiran Ramachandra, Raghavendra Busch, Christoph ETcTI Universitatea Politehnica Timisoara Romania ICTEAM UCL Belgium Computer Vision Lab University of Wuerzburg Germany Computer Vision Lab ETH Zurich Switzerland Department of Electrical and Computer Engineering McMaster University Canada Department of Electrical and Computer Engineering University of Alberta Canada McMaster University Canada Xidian University China Research Institute Singapore National University of Singapore Singapore University of Sydney Australia Robotics and Internet-of-Things Laboratory Prince Sultan University Riyadh12435 Saudi Arabia Department of Electrical Engineering National Taiwan University Taiwan Graduate Institute of Electronics Engineering National Taiwan University Taiwan Graduate Institute of Communication Engineering National Taiwan University Taiwan Wuhan University of Technology China Singapore Management University Singapore Singapore Sun Yat-sen University China Electrical Engineering Department Pennsylvania State University United States The University of Sydney Australia Southeast University China University of California Los Angeles United States Beijing Jiaotong University China Mansoura Univeristy Egypt College of Information Science and Technology Jinan University China Department of Information Technology Uppsala University Sweden Hefei University of Technology China Zhejiang Dahua Technology China Sardar Vallabhbhai National Institute of Technology India Norwegian University of Science and Technology Norway

ISBN: (纸本)9798350302493

This study assesses the outcomes of the NTIRE 2023 Challenge on Non-Homogeneous Dehazing, wherein novel techniques were proposed and evaluated on new image dataset called HD-NH-HAZE. The HD-NH-HAZE dataset contains 50 high resolution pairs of real-life outdoor images featuring nonhomogeneous hazy images and corresponding haze-free images of the same scene. The nonhomogeneous haze was simulated using a professional setup that replicated real-world conditions of hazy scenarios. The competition had 246 participants and 17 teams submitted solutions for the final testing phase. The proposed solutions demonstrated the cutting-edge in image dehazing technology. © 2023 IEEE.

关键词： Demulsification

CertainOdom: Uncertainty Weighted Multi-task Learning Model for LiDAR Odometry Estimation

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CertainOdom: Uncertainty Weighted Multi-task Learning Model ...

IEEE International Conference on robotics and Biomimetics

作者： Leyuan Sun Guanqun Ding Yusuke Yoshiyasu Fumio Kanehiro Department of Intelligent and Mechanical Interaction Systems Graduate School of Science and Technology University of Tsukuba Tsukuba Ibaraki Japan CNRS-AIST Joint Robotics Laboratory (JRL) IRL National Institute of Advanced Industrial Science and Technology (AIST). Digital Architecture Research Center (DARC) National Institute of Advanced Industrial Science and Technology (AIST) Tokyo Japan Computer Vision Research Team Artificial Intelligence Research Center (AIRC) National Institute of Advanced Industrial Science and Technology (AIST) Japan

ISBN: (纸本)9781665481106

As a basic and indispensable module, LiDAR odom-etry estimation is widely used in robotics. In recent years, learning-based modeling approaches for odometry estimation have been validated to be feasible. However, it is necessary to consider security factors as the highest priorities when we apply the learning-based model to certain high-risk real-world scenarios, such as autonomous driving. The odometry uncertainty estimation provides more valuable information for downstream tasks, such as route planning and navigation. In this paper, we propose an end-to-end neural network (namely CertainOdom) to solve odometry and uncertainty estimation tasks by applying multi-task learning. Instead of using the manually-tuned hyper-parameters, we employ the learnable uncertainties to weigh the balance between the error of translation and orientation in the loss function. We evaluate the estimated trajectory and uncertainty on KITTI dataset. We also compare the robustness against the traditional geometry-based methods on our artificially degraded KITTI LiDAR dataset. Extensive experimental results show that our model with uncertainty weighted loss achieves competitive performance in LiDAR odometry estimation. We also explain our uncertainties qualitatively and quantitatively.

关键词： Uncertainty Laser radar Biological system modeling Estimation Multitasking Robustness Trajectory

Automated Controller Placement for Software-Defined Networks to Resist DDoS Attacks

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computers, Materials & Continua 2021年第9期68卷 3147-3165页

作者： Muhammad Reazul Haque Saw Chin Tan Zulfadzli Yusoff Kashif Nisar Lee Ching Kwang Rizaludin Kaspin Bhawani Shankar Chowdhry Rajkumar Buyya Satya Prasad Majumder Manoj Gupta Shuaib Memon Faculty of Computing&Informatics Multimedia UniversityPersiaran MultimediaCyberjaya63100SelangorMalaysia Faculty of Engineering Multimedia UniversityPersiaran MultimediaCyberjaya63100SelangorMalaysia Faculty of Computing and Informatics University Malaysia SabahJalan UMSKota Kinabalu Sabah88400Malaysia Telekom Malaysia Research&Development TM Innovation CentreCyberjaya63000SelangorMalaysia National Center of Robotics and Automation Mehran University of Engineering&TechnologyJamshoroPakistan Department of Computer Science and Engineering Hanyang UniversitySeoul04763South Korea School of Electrical and Electronic Engineering Nanyang Technological University639798Singapore Cloud Computing and Distributed Systems Laboratory The University of MelbourneMelbourneVIC 3053Australia Department of Electrical and Electronic Engineering Bangladesh University of Engineering and Technology(BUET)Dhaka1205Bangladesh Department of Electronics and Communication Engineering JECRC UniversityVidhaniJaipur303905India Auckland Institute of Studies Mt AlbertAucklandNew Zealand

In software-defined networks(SDNs),controller placement is a critical factor in the design and planning for the future Internet of Things(IoT),telecommunication,and satellite communication *** research has concentrated largely on factors such as reliability,latency,controller capacity,propagation delay,and energy ***,SDNs are vulnerable to distributed denial of service(DDoS)attacks that interfere with legitimate use of the *** ever-increasing frequency of DDoS attacks has made it necessary to consider them in network design,especially in critical applications such as military,health care,and financial services networks requiring high *** propose a mathematical model for planning the deployment of SDN smart backup controllers(SBCs)to preserve service in the presence of DDoS *** a number of input parameters,our model has two distinct ***,it determines the optimal number of primary controllers to place at specific locations or nodes under normal operating ***,it recommends an optimal number of smart backup controllers for use with different levels of DDoS *** goal of the model is to improve resistance to DDoS attacks while optimizing the overall cost based on the *** simulated results demonstrate that the model is useful in planning for SDN reliability in the presence of DDoS attacks while managing the overall cost.

关键词： SDN automated controller placement SBC ILP DDoS attack

Opportunities and challenges for monitoring terrestrial biodiversity in the robotics age

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Nature Ecology and Evolution 2025年第6期9卷 1031-1042页

作者： Pringle, Stephen Dallimer, Martin Goddard, Mark A. Le Goff, Léni K. Hart, Emma Langdale, Simon J. Fisher, Jessica C. Abad, Sara-Adela Ancrenaz, Marc Angeoletto, Fabio Auat Cheein, Fernando Austen, Gail E. Bailey, Joseph J. Baldock, Katherine C. R. Banin, Lindsay F. Banks-Leite, Cristina Barau, Aliyu S. Bashyal, Reshu Bates, Adam J. Bicknell, Jake E. Bielby, Jon Bosilj, Petra Bush, Emma R. Butler, Simon J. Carpenter, Dan Clements, Christopher F. Cully, Antoine Davies, Kendi F. Deere, Nicolas J. Dodd, Michael Drinkwater, Rosie Driscoll, Don A. Dutilleux, Guillaume Dyrmann, Mads Edwards, David P. Farhadinia, Mohammad S. Faruk, Aisyah Field, Richard Fletcher, Robert J. Foster, Chris W. Fox, Richard Francksen, Richard M. Franco, Aldina M. A. Gainsbury, Alison M. Gardner, Charlie J. Giorgi, Ioanna Griffiths, Richard A. Hamaza, Salua Hanheide, Marc Hayward, Matt W. Hedblom, Marcus Helgason, Thorunn Heon, Sui P. Hughes, Kevin A. Hunt, Edmund R. Ingram, Daniel J. Jackson-Mills, George Jowett, Kelly Keitt, Timothy H. Kloepper, Laura N. Kramer-Schadt, Stephanie Labisko, Jim Labrosse, Frédéric Lawson, Jenna Lecomte, Nicolas de Lima, Ricardo F. Littlewood, Nick A. Marshall, Harry H. Masala, Giovanni L. Maskell, Lindsay C. Matechou, Eleni Mazzolai, Barbara McConnell, Alistair Melbourne, Brett A. Miriyev, Aslan Nana, Eric Djomo Ossola, Alessandro Papworth, Sarah Parr, Catherine L. Payo-Payo, Ana Perry, Gad Pettorelli, Nathalie Pillay, Rajeev Potts, Simon G. Prendergast-Miller, Miranda T. Qie, Lan Rolley-Parnell, Persie Rossiter, Stephen J. Rowcliffe, Marcus Rumble, Heather Sadler, Jon P. Sandom, Christopher J. Sanyal, Asiem Schrodt, Franziska Sethi, Sarab S. Shabrani, Adi Siddall, Robert Smith, Simón C. Snep, Robbert P. H. Soulsbury, Carl D. Stanley, Margaret C. Stephens, Philip A. Stephenson, P.J. Struebig, Matthew J. Studley, Matthew Svátek, Martin Tang, Gilbert Taylor, Nicholas K. Umbers, Kate D. L. Ward, Robert J. White, Patrick J. C. Whittingham, Mark J. Wich, Serge Williams, Christopher D. Yakubu, Ibrahim B. Yoh, Natalie Durrell Institute of Conservation and Ecology (DICE) School of Natural Sciences University of Kent Canterbury United Kingdom Centre for Environmental Policy Imperial College London London United Kingdom Department of Geography and Environmental Sciences Northumbria University Newcastle upon Tyne United Kingdom School of Computing Engineering and the Built Environment Edinburgh Napier University Edinburgh United Kingdom Synthotech Ltd Milner Court Hornbeam Square Harrogate United Kingdom Mechanical Engineering Department University College London London United Kingdom HUTAN SWD Kota Kinabalu Malaysia Programa de Pós-Graduação em Gestão e Tecnologia Ambiental da Universidade Federal de Rondonópolis Rondonópolis Brazil Department of Engineering Harper Adams University Newport United Kingdom Applied Ecology Research Group School of Life Sciences Anglia Ruskin University Cambridge United Kingdom Centre for Ecology and Hydrology Penicuik United Kingdom Department of Life Sciences Imperial College London Silwood Park Campus Ascot United Kingdom Department of Urban and Regional Planning Faculty of Earth and Environmental Sciences Bayero University Kano Nigeria Greenhood Nepal Kathmandu Nepal Animal Rural & Environmental Sciences Nottingham Trent University Nottinghamshire United Kingdom School of Biological and Environmental Sciences Liverpool John Moores University Liverpool United Kingdom Lincoln Centre for Autonomous Systems University of Lincoln Lincoln United Kingdom Royal Botanic Garden Edinburgh Edinburgh United Kingdom School of Biological Sciences University of East Anglia Norwich Research Park Norwich United Kingdom Digital Ecology Limited Bristol United Kingdom School of Biological Sciences University of Bristol Bristol United Kingdom Department of Computing Imperial College London London United Kingdom Department of Ecology and Evolutionary Biology University of Colorado Boulder CO United States Faculty of Science Technology Engineering

With biodiversity loss escalating globally, a step change is needed in our capacity to accurately monitor species populations across ecosystems. Robotic and autonomous systems (RAS) offer technological solutions that may substantially advance terrestrial biodiversity monitoring, but this potential is yet to be considered systematically. We used a modified Delphi technique to synthesize knowledge from 98 biodiversity experts and 31 RAS experts, who identified the major methodological barriers that currently hinder monitoring, and explored the opportunities and challenges that RAS offer in overcoming these barriers. Biodiversity experts identified four barrier categories: site access, species and individual identification, data handling and storage, and power and network availability. robotics experts highlighted technologies that could overcome these barriers and identified the developments needed to facilitate RAS-based autonomous biodiversity monitoring. Some existing RAS could be optimized relatively easily to survey species but would require development to be suitable for monitoring of more ‘difficult’ taxa and robust enough to work under uncontrolled conditions within ecosystems. Other nascent technologies (for instance, new sensors and biodegradable robots) need accelerated research. Overall, it was felt that RAS could lead to major progress in monitoring of terrestrial biodiversity by supplementing rather than supplanting existing methods. Transdisciplinarity needs to be fostered between biodiversity and RAS experts so that future ideas and technologies can be codeveloped effectively. © The Author(s) 2025.

关键词：

A DNA Based Colour Image Encryption Scheme Using A Convolutional Autoencoder

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arXiv 2022年

作者： Ahmed, Fawad Rehman, Muneeb Ur Ahmad, Jawad Khan, Muhammad Shahbaz Boulila, Wadii Srivastava, Gautam Lin, Jerry Chun-Wei Buchanan, William J. Department of Cyber Security Pakistan Navy Engineering College NUST Karachi75350 Pakistan Department of Electrical Engineering HITEC University Taxila Pakistan School of Computing Edinburgh Napier University Edinburgh United Kingdom Department of Electrical Engineering HITEC University Taxila47080 Pakistan Robotics and Internet-of-Things Laboratory Prince Sultan University Riyadh Saudi Arabia RIADI Laboratory University of Manouba Manouba Tunisia Math and Computer Science Brandon University Brandon Canada Research Centre for Interneural Computing China Medical University Taichung Taiwan Dept of Computer Science and Math Lebanese American University Beirut Lebanon Western Norway University of Applied Sciences Bergen Norway

With the advancement in technology, digital images can easily be transmitted and stored over the Internet. Encryption is used to avoid illegal interception of digital images. Encrypting large-sized colour images in their original dimension generally results in low encryption/decryption speed along with exerting a burden on the limited bandwidth of the transmission channel. To address the aforementioned issues, a new encryption scheme for colour images employing convolutional autoencoder, DNA and chaos is presented in this paper. The proposed scheme has two main modules, the dimensionality conversion module using the proposed convolutional autoencoder, and the encryption/decryption module using DNA and chaos. The dimension of the input colour image is first reduced from N × M × 3 toP × Q gray-scale image using the encoder. Encryption and decryption are then performed in the reduced dimension space. The decrypted gray-scale image is upsampled to obtain the original colour image having dimension N × M × 3. The training and validation accuracy of the proposed autoencoder is 97% and 95%, respectively. Once the autoencoder is trained, it can be used to reduce and subsequently increase the dimension of any arbitrary input colour image. The efficacy of the designed autoencoder has been demonstrated by the successful reconstruction of the compressed image into the original colour image with negligible perceptual distortion. The second major contribution presented in this paper is an image encryption scheme using DNA along with multiple chaotic sequences and substitution boxes. The security of the proposed image encryption algorithm has been gauged using several evaluation parameters, such as histogram of the cipher image, entropy, NPCR, UACI, key sensitivity, contrast, etc. The experimental results of the proposed scheme demonstrate its effectiveness to perform colour image encryption. © 2022, CC BY.

关键词： DNA