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RobustFace: a novel image restoration technique for face adversarial robustness improvement

Multimedia Tools and Applications 2025年第11期84卷 8965-8993页

作者： Sadu, Chiranjeevi Das, Pradip K. Yannam, V Ramanjaneyulu Nayyar, Anand Department of Computer Science and Engineering RGUKT Nuzvid Andhra Pradesh Nuzvid India Department of Computer Science and Engineering IIT Guwahati Assam Guwahati India School of Technology Woxsen University Telangana Hyderabad India Graduate School Faculty of Information Technology Duy Tan University Da Nang550000 Viet Nam

Machine Learning (ML) models, particularly Deep Learning (DL), have made rapid progress and achieved significant milestones across various applications, including numerous safety-critical contexts. However, these models have recently been discovered to be susceptible to adversarial attacks, which are well-crafted input images. The primary objective of this paper is to propose a novel methodology titled "RobustFace", which is based on deep image restoration networks, that significantly improves the facial adversarial robustness of various image-classification models. Adversarial images are created using the Private Fast Gradient Sign Method (P-FGSM), StyleGAN and Fast Landmark Manipulation (FLM) methods. The adversarial images are then enhanced using deep image restoration networks to bring back them into the original space. The encoded weighted local magnitude patterns (WLMP) are extracted and provided to different types of classifiers to detect facial adversarial images from the clean images. The effectiveness of RobustFace has been demonstrated on two real-world datasets and experimental outcomes show that it significantly improves facial adversarial robustness on all evaluating classifiers. It improves the highest classification accuracy from 98.75% to 99.00% on P-FGSM attacks, from 77.94% to 85.25% on adversarial attacks generated by StyleGAN and from 65.52% to 69.50% for FLM attacks. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Image reconstruction

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Multimodal Information Extraction:A systematic Review of subtask, modal types and applications based on Deep Learning in Banking sector 5

Multimodal Information Extraction:A systematic Review of sub...

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5th IEEE International Conference for Emerging technology, INCET 2024

作者： Srinivsan, Samundiswary Krishna, R. Sathya Bama Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India SIES Graduate School of Technology Navi Mumbai India

ISBN: (纸本)9798350361155

Deep Learning has been widely applied in all application domains namely Healthcare, Entertainment, Banking & finance, Agriculture, Aerospace & defence etc. Due to stupendous achievement of deep learning as a data processing technique has fascinated in all research areas. Handling of Deep learning in Banking and Financial sector is pervasive. Advancement of Information extraction especially because of different formats of information such as text, image, sound, video can be extracted simultaneously by the usage of deep learning techniques. This study reviews and analyses multimodal information extraction based on deep learning techniques especially on banking and financial sector. It aims to enhance the efficiency of document-intensive business tasks in the financial sector. Deep review analysis will be carried out to understand subtasks of Information extraction and techniques involved in Banking domain. Finally, how multimodal information extraction using deep learning enhances the efficiency than unimodal and its use cases in current AI based automation world will be highlighted. © 2024 IEEE.

关键词： Finance

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Formal Security Verification for Searchable Symmetric Encryption Using ProVerif 18

Formal Security Verification for Searchable Symmetric Encryp...

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18th International Symposium on Information Theory and Its Applications, ISITA 2024

作者： Mieno, Takehiko Okazaki, Hiroyuki Arai, Kenichi Futa, Yuichi Yamamoto, Hiroaki Graduate School of Medicine Science and Technology Shinshu University Epson Avasys Corporation Japan Graduate School of Science and Technology Shinshu University Japan Graduate School of Engineering Nagasaki University Japan School of Computer Science Tokyo University of Technology Japan Shinshu University Faculty of Engineering Japan

ISBN: (纸本)9784885523519

With the rapid proliferation of various cloud storage services in recent years, the development of technology to efficiently search data while ensuring its confidentiality during cloud usage is an important issue. The technology that enables keyword searches on encrypted files using previously set keywords is called searchable symmetric encryption (SSE). In this paper, we propose a method formally representing encrypted document, and verify the security of SSE using the formal verification tool ProVerif. Our proposed method considers the channel-type terms of ProVerif as a Document that includes different keywords to verify the indistinguishability of encrypted documents. © 2024 The Institute of Electronics, Information and Communication Engineers.

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High-Performance Face Identification Using Randomly Selected Channels from Hyperspectral Images

High-Performance Face Identification Using Randomly Selected...

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2024 IEEE International Conference on Imaging Systems and Techniques, IST 2024

作者： Furusawa, Sota Premachandra, Chinthaka Graduate School of Engineering and Science Shibaura Institute of Technology Dept. of Electrical Engineering and Computer Science Tokyo Japan

ISBN: (纸本)9798350378214

With the recent development of information technology, the importance of protecting personal information has increased. Because of the vulnerability in passwords, biometric authentication is now being used as a method of personal information protection. However, biometric authentication has the possibility of malicious authentication due to the emergence of technology that can generate biometric data that is difficult to duplicate. Therefore, this study aimed to solve the security problems posed by Generative Adversarial Networks (GANs) in biometric authentication by using Hyperspectral Images (HSI). We searched for solutions to the problems identified in previous studies, such as the inability to correctly identify individuals who have not been previously learned and the length of time required for identification. In this paper, we used three channels of data randomly selected from hyperspectral face images for learning to identify individuals by binary classification into 'learned subject (class)' and 'unlearned subjects (classes)'. In this case, discriminators based on the least-squares generative adversarial network (LSGAN) model was developed for each class using three randomly selected channels of data from the HSI for two class classification. The proposed method significantly reduced the identification time and achieved very high discrimination accuracy in all classes. In addition, the results showed high accuracy throughout multiple face identification experiments. © 2024 IEEE.

关键词： Generative adversarial networks

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REMAINING CONDITION AND RENOVATION OF SEMI-DETACHED HOUSE IN HAKODATE, HOKKAIDO

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AIJ Journal of technology and Design 2023年第72期29卷 1035-1040页

作者： Uda, Kyosuke Matsui, Daisuke Graduate School of Science and Technology Niigata Univ Japan Program of Arch. Faculty of Eng Niigata Univ. Japan

This study is aim to clarify the remaining condition and renovation of semi-detached house in Hakodate city, Hokkaido. The results are as follows;1) We extracted 143 buildings as semi-detached house. 2)Among those we extracted representative 19 types by design characteristic of semi-detached house. 3)112 buildings has changed from the symmetrical appearance at the time it was built. 4)In the case of the owners of the building are different on the left and right, there was a tendency for the appearance of buildings to differ between the left and right. © 2023 Architectural Institute of Japan. All rights reserved.

关键词： Houses

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An electron-transfer-tuning strategy at the graphene/metal interface for improving acidic water electrolysis in proton exchange membrane electrolyzers

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Journal of Energy Chemistry 2025年第4期103卷 344-352页

作者： Yue Xu Shuaidong Li Yingjian He Fumiya Shiokawa Samuel Jeong Aimi Asilah Haji Tajuddin Zeyu Xi Yoshikazu Ito Jingzi Zhang Zeyun Cai Xi Lin Kailong Hu School of Materials Science and Engineering and Institute of Materials Genome&Big DataHarbin Institute of TechnologyShenzhen 518055GuangdongChina Institute of Special Environments Physical Sciences Harbin Institute of TechnologyShenzhen 518055GuangdongChina School of Computer Science and Technology Harbin Institute of TechnologyShenzhen 518055GuangdongChina Institute of Applied Physics Graduate School of Pure and Applied SciencesUniversity of TsukubaTsukuba 305-8573Japan

Graphene encapsulation has been shown to be an effective technique for improving the corrosion resistance of non-noble metal catalysts for the acidic water *** key challenge lies in enhancing the electrocatalytic activity of graphene-encapsulated metals while maintaining their durability in acidic ***,an electron-transfer-tuning strategy is investigated at the graphene/NiMo interface,aiming to improve the hydrogen evolution reaction(HER)performance of graphene-encapsulated NiMo *** doping of Ti,a low electronegativity element,into NiMo substrate was confirmed to increase electron transfer from the metal core toward the *** electron-rich state on graphene facilitates the adsorption of positively charged protons on graphene,thereby enabling a Pt/C-comparable performance in 0.5 M H_(2)SO_(4),with only a 3.8%degradation in performance over a 120-h continuous *** proton exchange membrane(PEM)water electrolyzer assembled by the N-doped grapheneencapsulated Ti-doped NiMo exhibits a smaller cell voltage to achieve a current density of 2.0 A cm^(-2),in comparison to the Pt/C based *** study proposes a novel electron-transfer-tuning strategy to improve the HER activity of graphene-encapsulated non-noble metal catalysts without sacrificing durability in acidic electrolytes.

关键词： Hydrogen evolution Acidic media Graphene encapsulation Interface Proton exchange membrane

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The advancement in multi-label and label distribution learning

The advancement in multi-label and label distribution learni...

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2024 International Conference on Electronics and Devices, Computational science, ICEDCS 2024

作者： Liu, Suping Samonte, Mary Jane C. Mapua University School of Graduate Studies and Information Technology Manila1002 Philippines Guangdong University of Science and Technology School of Computer Science Dongguan523083 China

ISBN: (纸本)9798331527624

Multi label learning and label distribution learning as emerging machine learning paradigms have developed rapidly in recent years. This article aims to review the research progress in this field, focusing on the algorithms and applications of multi label learning and label distribution learning. Firstly, the basic concepts and related backgrounds of multi label learning and label distribution learning were introduced, and their differences and connections with traditional single label learning were explained. Secondly, the research progress on multi label learning, multi label classification, multi label feature selection, and label distribution learning were introduced separately. Finally, this article summarizes and looks forward to the future development directions of multi label learning and label distribution learning. © 2024 IEEE.

关键词： Adversarial machine learning

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Partial Multi-label Learning with a Few Accurately Labeled Data 20th

Partial Multi-label Learning with a Few Accurately Labeled...

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20th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2023

作者： Mizuguchi, Haruhi Kimura, Keigo Kudo, Mineichi Sun, Lu Division of Computer Science and Information Technology Graduate School of Information Science and Technology Hokkaido University Sapporo060-0814 Japan School of Information Science and Technology ShanghaiTech University Shanghai201210 China

ISBN: (纸本)9789819970216

Partial Multi-label Learning is a multi-label classification problem where only candidate labels are given for training data. These candidate labels consist of relevant labels and false-positive labels. In this paper, we consider the PML when a few accurately labeled data are available. In practice, it is difficult to remove false-positive labels fully due to a large cost, but it is possible to do that in a few instances with a smaller cost. Conventional PML methods do not assume those accurately labeled data so it is hard to utilize data effectively. We propose a new algorithm called PML-VD to utilize those accurately labeled data. PML-VD first disambiguates the noisy-labeled data with both accurately labeled data and noisy labeled data and then learns a classifier. This two-stage approach enables the effective utilization of accurately labeled data without overfitting. Experiments on nine PML datasets shows the effectiveness of explicit utilization of accurately labeled data. In best cases, PML-VD improves 7% classification accuracy in terms of ranking loss. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Classification (of information)

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DFNet: Dense fusion convolution neural network for plant leaf disease classification

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Agronomy Journal 2024年第3期116卷 826-838页

作者： Faisal, Muhamad Leu, Jenq-Shiou Avian, Cries Prakosa, Setya Widyawan Köppen, Mario Department of Electronic and Computer Engineering (ECE) National Taiwan University of Science and Technology Taipei City Taiwan Department of Computer Science and Systems Engineering (CSSE) Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Fukuoka Iizuka-shi Japan

The early identification of plant diseases is crucial for preventing the loss of crop production. Recently, the advancement of deep learning has significantly improved the identification of plant leaf diseases. However, most approaches depend on a single convolutional neural network (CNN) to extract the leaf features, ignoring the opportunity to take full advantage of the feature richness available in the images. This paper explores a novel CNN model with multiple automated feature extractors, namely, dense fusion CNN (DFNet), for classifying plant leaf diseases. DFNet aims to increase the diversity of extracted features in order to improve discrimination. Instead of using a single-CNN model, DFNet relies on a double-pretrained CNN model, MobileNetV2 and NASNetMobile, as the feature extractor. The features extracted from each CNN are fused in the fusion layer using a fully connected network. The proposed method was evaluated using corn (Zea mays L.) and coffee (Coffea canephora) leaf disease datasets and compared to the existing models. The experiment showed that DFNet is superior and consistent to other CNN methods by achieving an accuracy of 97.53% for corn leaf diseases and 94.65% for coffee leaf diseases. © 2023 The Authors. Agronomy Journal © 2023 American Society of Agronomy.

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Addressing the role and opportunities of machine learning utilization in brain tumor detection 9th

Addressing the role and opportunities of machine learning ut...

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9th International Conference on computer science and Computational Intelligence, ICCSCI 2024

作者： Lesmana, Vallerie Delia Agustine, Holly Wairooy, Irma Kartika Makalew, Brilly Andro Computer Science Department School of Computer Science Binus University Jakarta11480 Indonesia Mobile Application & Technology Program Computer Science Department Binus University Jakarta11480 Indonesia

This research aims to develop a brain tumor detection model by utilizing the machine learning techniques and Convolutional Neural Network (CNN). A significant matter to address is revolving around early detection and the proper handling regarding the brain tumor. This research's methodology consists of collecting the dataset, identifying the tools and language to use, prepare and preprocessing the data, data augmentation, splitting and label encoding, building the model architecture, compiling the model, training, and evaluating the model, predicting the model, and comparing it with other models. Dataset consists of 7022 MRI images, divided into training and testing subsets;and four classes: glioma, meningioma, pituitary, and no tumor. There are four different CNN models that have been built and evaluated, namely VGG16, InceptionV3, ResNet50, and DenseNet121. The result gained shows VGG16 with the best performance achieving an accuracy rate of 96.43%, followed by DenseNet121 (94,96%), InceptionV3 (92,40%), and ResNet50 (78,69%). Although there is still room for improvement regarding overfitting and increasing the models' overall performance, this result is promising enough to enhance early diagnosis and offer an appropriate and effective treatment for patients. © 2024 The Authors.

关键词： Convolutional neural networks

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