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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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TCIP: Transformed Colour Intensity Profiles analysis for fault detection in power line insulators

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Multimedia Tools and Applications 2025年第8期84卷 4503-4534页

作者： Tomaszewski, Michal Gasz, Rafal Kasana, Singara Singh Osuchowski, Jakub Singh, Simranjit Zator, Slawomir Department of Computer Science Faculty of Electrical Engineering Automatic Control and Informatics Opole University of Technology Prószkowska 76 Opole45-758 Poland Department of Computer Science and Information Technology Central University of Haryana Haryana Mahendergarh123031 India Department of Information Technology Dr B. R. Ambedkar National Institute of Technology Punjab Jalandhar India Department of Safety Engineering and Technical Systems Faculty of Production Engineering and Logistics Opole University of Technology Prószkowska 76 Opole45-758 Poland

In response to the escalating demand for electricity, the aging process and inherent failures in power lines have become unavoidable challenges in their operational integrity. This research addresses the imperative need for a robust method to detect damage in power insulator disc images, introducing a novel feature extraction technique grounded in color intensity profile transformation and ensemble learning. For the purposes of the research, a specific dataset was developed based on publicly available images of power insulators. The collection consists of 9232 images of insulator discs which included 2184 images of damaged discs. The discernible damages include breakages and burnouts. Two distinct analysis variants were employed. In the first variant (V1), two classification groups were selected: disc with damages and disc without damages. In the second variant (V2), three classification groups were selected: discs without damage, broken discs, and burnout discs. The described research was divided into two successive stages. In the first of them (S1), the transformed color intensity profiles for the prepared data set were built and various machine learning models for their classification were trained and evaluated. In the next stage (S2), previously developed, highest efficient machine learning models, were used to make the detection of various damaged types of overhead line insulator discs. For the first stage of research high-performance results and a high value of the F1 score parameters were obtained (F1 score = 0.999 for the first variant (S1V1) and F1 score = 0.992 for the second variant (S1V2). In the second stage of research and the first variant (S2V1), the highest result of the F1 score was 0.932 and for the second variant (S2V2) F1 score was 0.930. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature extraction

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An effective medical image fusion method utilizing moth-flame optimization and coupled neural P systems

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Neural Computing and Applications 2025年 1-41页

作者： Dinh, Phu-Hung Le, Thi-Hong-Ha Giang, Nguyen Long Faculty of Computer Science and Engineering Thuyloi University 175 Tay Son Dong Da Hanoi Viet Nam Hong Duc University 565 Quang Trung Dong Ve Thanh Hoa Viet Nam Institute of Information Technology Vietnam Academy of Science and Technology Hanoi Viet Nam Graduate University of Science and Technology Vietnam Academy of Science and Technology Hanoi Viet Nam

Medical image fusion is crucial for various applications, yet existing fusion models often encounter issues such as low quality, information loss, and insufficient contrast. This paper presents a novel approach to medical image fusion that directly addresses these challenges by enhancing brightness and contrast, improving overall image quality, and preserving essential structural features. Our method begins by decomposing input images into a base component and two detail components using a three-component decomposition (TCD) method with a truncated Huber filter (THF). The detail components are subsequently fused using an integrated approach combining the structure tensor saliency detection operator (STSDO), distance-weighted regional energy (DWRE), fast guided filter (FGF), and coupled neural P system (CNPS). The base components are then fused through an adaptive method guided by the moth-flame optimization (MFO) algorithm. To evaluate our approach, we used a dataset of 120 pairs of medical images and assessed performance with metrics such as QMLI, QCI, QAG, QAB/F, QP, QW, QE, and QCB. Experimental results demonstrate that the proposed model significantly enhances brightness and contrast and more effectively preserves information compared to recent methods. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Image fusion

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Specification of Complex Analytics Workflows: A Formal Language Model of Decision Options 20th

Specification of Complex Analytics Workflows: A Formal Lang...

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20th International Conference on the Economics of Grids, Clouds, Systems, and Services, GECON 2024

作者： Miri, Pouriya Kochovski, Petar Tuler de Oliveira, Marcela Stankovski, Vlado Faculty of Computer and Information Science University of Ljubljana Ljubljana Slovenia Department of Engineering Systems Delft University of Technology Delft Netherlands

ISBN: (纸本)9783031812255

The specification of experiments expressed as Complex Analytics Workflows is a complex task that involves many decision-making steps with various degrees of complexity. The use of the context, the expert knowledge, and the potential for its sharing and reuse in the context of experiment specification have not been addressed sufficiently until now. Moreover, to make such knowledge instrumental, it should be coupled with specific probabilistic measures, such as particular assurances, ranking, and verification of various options. The paper aims to present a novel semantic model for probabilistic reasoning in any experimentation context coupled with a functional system for knowledge generation, reuse, and sharing. The result of our work can be used within existing experimentation engines. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Markov processes

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Hybrid lightweight encryption for IoT: integrating chaotic key generators with Feistel and substitution-permutation networks for secure 3DV transmission

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Multimedia Tools and Applications 2025年 1-47页

作者： El-Shafai, Walid Mesrega, Ahmed K. Ahmed, Hossam Eldin H. Elbahnasawy, Nirmeen A. El-Samie, Fathi E. Abd Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Deptartment of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Computer Science and Engineering Faculty of Electronic Engineering Menoufia University Menouf Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

The Internet of Things (IoT) stands as a revolutionary leap in digital connectivity, envisioning a future network connecting billions of devices, seamlessly. Amidst the myriad benefits, there arises an intricate web of challenges, prominently centered around potential threats and data security implications. Recent cryptography techniques, such as DNA-based cryptography, 3D chaos-based cryptography, and optical cryptography, face challenges including large encryption times, high energy consumption, and suboptimal rather than optimal performance. Particularly, the burden of long encryption cycles strains the energy resources of typical low-power and compact IoT devices. These challenges render the devices vulnerable to unauthorized breaches, despite large storage capacities. The hallmark of the IoT ecosystem, characterized by its low-power compact devices, is the burgeoning volume of data they generate. This escalating data influx, while necessitating expansive storage, remains vulnerable to unauthorized access and breaches. Historically, encryption algorithms, with their multifaceted architectures, have been the bulwark against such intrusions. However, their inherently-complex nature, entailing multiple encryption cycles, strains the limited energy reserves of typical IoT devices. In response to this intricate dilemma, we present a hybrid lightweight encryption strategy. Our algorithm innovatively leverages both one-dimensional (1D) and two-dimensional (2D) chaotic key generators. Furthermore, it amalgamates a classical encryption philosophy, harmonizing the strengths of Feistel and substitution-permutation networks. The centerpiece of our strategy is achieving effective encryption in merely three rounds, tailored expressly for compressed Three-Dimensional Video (3DV) frames, ensuring their unwavering integrity. Our workflow commences with the H.264/MVC compression algorithm, setting the stage for the subsequent encryption phase. Through rigorous MATLAB simulations,

关键词： Image coding

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Utilizing faculty computers for Cryptocurrency Validation and Mining During Idle Time 33rd

Utilizing Faculty Computers for Cryptocurrency Validation an...

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33rd International Conference on Organization and technology of Maintenance, OTO 2024

作者： Švarcmajer, Miljenko Köhler, Mirko Marinčić, Juraj Perić, Juraj Faculty of Electrical Engineering Josip Juraj Strossmayer University of Osijek Computer Science and Information Technology Osijek Osijek31000 Croatia

ISBN: (纸本)9783031805967

In the world of cryptocurrencies, there is the possibility of participating in numerous processes in which some kind of reward is obtained, most often in the form of cryptocurrency. This paper investigates the possibilities of using laboratory computers at the faculty of Electrical Engineering, Computing and information Technologies in these processes when they are not used for teaching purposes. The hardware specifications of the computer were collected and an insight into the ways of cryptocurrency earnings mining and validation was provided. Mining is the most well-known earning process, and it was introduced with the Bitcoin cryptocurrency, and based on this cryptocurrency, this process is described in detail. Due to the hardware configuration of the computer, in this paper we focus on the profitability of CPU mining of cryptocurrencies that can be mined this way, namely Monero, Zephyr, Raptoreum, Skytere and Subi Network. This type of mining has been unprofitable for Bitcoin for years. Computing power does not have to be used only for the mining process. For example, Solana uses CPUs to validate transactions. This paper combines technical and economic analyses, taking into account computer specifications to assess the economic viability of cryptocurrency mining and validation on college computers. We highlight the importance of organization, maintenance, and economic factors in managing these processes, providing practical guidelines for optimal utilization of faculty resources for the purpose of cryptocurrency earnings. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Earnings

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An optimized ensemble model based on meta-heuristic algorithms for effective detection and classification of breast tumors

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Neural Computing and Applications 2025年第6期37卷 4881-4894页

作者： Saber, Abeer Elbedwehy, Samar Awad, Wael A. Hassan, Esraa Information Technology Department Faculty of Computers and Artificial Intelligence Damietta University Damietta34517 Egypt Department of Data Science Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh33511 Egypt Department of Computer Science Faculty of Computers and Artificial Intelligence Damietta University Damietta34517 Egypt Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh33511 Egypt

One of the most common cancers among women worldwide is breast cancer (BC), and early diagnosis can save lives. Early detection of BC increases the likelihood of a successful outcome by enabling treatment to start sooner. Even in areas without access to a specialist physician, machine learning (ML) aids in early BC detection. The medical imaging community is becoming more interested in using ML, and deep learning (DL) to increase the accuracy of cancer screening. Many disease-related data are sparse. However, for DL models to perform well, a large amount of data is required. Because of this, the DL models that are currently in use on medical images are not as effective as they could be. Convolutional neural network (CNN) models have recently gained popularity in the medical industry, and they perform admirably in terms of high performance and robustness at image classification. The proposed method classifies data using ensemble pre-trained models such as the dense convolutional network (DenseNet)-121 and EfficientNet-B5 feature extractor networks, as well as the support vector machine for classification. Using a modified meta-heuristic optimizer, the selected pre-trained CNN hyperparameters were optimized to improve the performance. The experimental results for the presented model on the INbreast dataset show that the EfficientNet-B5 model is effective for BC classification, with overall accuracy, sensitivity, specificity, precision, and area under the ROC curve (AUC) values of 99.9%, 99.9%, 99.8%, 99.1%, 1.0, respectively. © The Author(s) 2024.

关键词： Adversarial machine learning

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Tailoring whispering gallery mode resonator for a wide range of potential applications: Optoplazmonic erbium doped microspheres

Tailoring whispering gallery mode resonator for a wide range...

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2024 Conference on Research and Innovations in science and technology of Materials, CRISTMAS 2024

作者： Brice, Inga Sedulis, Arvids Alnis, Janis Institute of Atomic Physics and Spectroscopy Faculty of Science and Technology University of Latvia Riga Latvia Institute of Photonics Electronics and Telecommunications Faculty of Computer Science Information Technology and Energy Riga Technical University Riga Latvia

Different WGM resonator geometries and materials can be used to tailor WGM resonators for specific applications. WGM resonators can reach ultra-high quality factors that lead to enhanced light-matter interaction. Additionally, the surface of a WGM resonator can be functionalized with a nanomaterial layer to enhance desirable optical properties, tailoring it further - from sensitivity to any perturbations of the surrounding environment to the generation of nonlinear effects at relatively low powers. Silica doped with erbium is widely used for optical amplification while metal nanoparticles act like nano-antennas and increase sensitivity to surrounding media. Doping silica microspheres with erbium ions, we have observed lasing at 1530 - 1560 nm at a threshold of 2 dBm (1.6 mW) when pumping with 1470 - 1500 nm. Lasing was also observed when additionally functionalizing the sphere surface with gold nanoparticles. © Published under licence by IOP Publishing Ltd.

关键词： Metal nanoparticles

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A Comparative Study of Deep Learning and Modern Machine Learning Methods for Predicting Australia's Precipitation

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International Journal of Advanced computer science and Applications 2025年第4期16卷 943-966页

作者： Farman, Hira Mastoi, Qurat-Ul-Ain Abbas, Qaiser Ahmad, Saad Alshahrani, Abdulaziz Jan, Salman Syed, Toqeer Ali Department of Computer Science Iqra University Karachi Institute of Economics and Technology Karachi74600 Pakistan School of Computing and Creative Technologies University of the West of England Bristol United Kingdom Faculty of Computer and Information Systems Islamic University of Madinah Madinah42351 Saudi Arabia Faculty of Computer Science IQRA University Karachi Pakistan Faculty of Computer Studies Arab Open University 512 United Kingdom Islamic University of Madinah CO 42351 Madinah Saudi Arabia

Floods are chaotic weather patterns that cause irreversible and devastating harm to people's lives, crops, and the socioeconomic system. It causes extensive property damage, animal mortality, and even human fatalities. To mitigate the risk of flooding, it is imperative to create an early warning system that can accurately forecast the amount of rain that will fall tomorrow. Rainfall forecasting is essential to the lives of people and is absolutely important everywhere in the world. The rainfall prediction model reduces risk and helps to prevent further human deaths. Statistics cannot reliably forecast rainfall since the atmosphere is dynamic. Due to the preceding factors, this study uses machine learning and deep learning techniques to estimate precipitation. The purpose of this study is to develop and evaluate a prediction model for forecasting rainfall of 5 cities of Australia (Darwin, Sydney, Perth airport, Melbourne, Brisbane). The Dataset was gathered from the national meteo-rological organization of Australia is the Australian Government Bureau of Meteorology, also known as the BOM. To monitor and forecast meteorological conditions, climatic trends, and natural calamities like cyclones, storms, floods, the Bureau of Meteorology is essential. The dataset includes 14, 5460 size, 23 features detailed city-specific monthly averages for Australia from 2008 to 2017(10 years). An effective rainfall forecasting was produced by integration of a number of Machine Learning and Deep Learning techniques, including Random Forest model (RF), Decision Tree (DT) and Gradient Boosting classifier (GBC), Artificial Neural Network (ANN), and Recurrent Neural Network (RNN). The models were trained to forecast rainfall, reducing the potential impact of floods. Results indicate that combining neural networks and Random Forests provides the most accurate predictions. © (2025), (science and information Organization). All rights reserved.

关键词： Rain

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Warehouse Demand Variation Prediction and Optimization Based on Machine Learning Algorithms 24

Warehouse Demand Variation Prediction and Optimization Based...

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4th Asia-Pacific Artificial Intelligence and Big Data Forum, AIBDF 2024

作者： Lu, Yuting Jomhari, Nazean Liao, Shengshi Goyal, Shyam Bihari Jiangxi College of Applied Technology Jiangxi Ganzhou China Faculty of Information Technology City University Malaysia Kuala Lumpur Malaysia Faculty of Computer Science & Information Technology Universiti Malaya Kuala Lumpur Malaysia Chitkara University Rajpura India

ISBN: (纸本)9798400710865

With the intensification of market competition and the diversification of customer demands, efficient inventory management has become a key factor for the success of enterprises. Warehouse demand forecasting, as a core aspect of inventory management, is of great significance for enterprises to reasonably arrange production, optimize inventory levels, and reduce costs. This paper delves into the prediction and optimization methods of warehouse demand changes based on machine learning algorithms, with a particular focus on three powerful algorithms: Random Forest, XGBoost, and *** LightGBM model performed the best. The lowest MSE can provide a lower prediction error, and the highest R2 value, which is close to 0.786, can better fit the *** research results fully demonstrate that machine learning algorithms have significant application value and potential in the field of predicting changes in warehouse demand. © 2024 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

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