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Robust steganographic approach using generative adversarial network and compressive autoencoder

Multimedia Tools and Applications 2024年 1-38页

作者： Qasaimeh, Malik Qtaish, Alaa Abu Aljawarneh, Shadi Department of Computer Information Systems Faculty of Computer and Information Technology Jordan University of Science and Technology Irbid Jordan Department of Software Engineering co-joint with the Department of Cyber Security Faculty of Computer and Information Technology Jordan University of Science and Technology Irbid Jordan

Nowadays, social media applications and websites have become a crucial part of people’s lives;for sharing their moments, contacting their families and friends, or even for their jobs. However, the fact that these valuable data are transferred via the Internet and open channels, which are vulnerable to attacks and espionage, requires using defense methods to improve the security of data transmission. Recently, the progress of Deep Learning (DL) inspired researchers to use it with security methods such as steganography, the art of hiding secret data in unrelated content. DL and steganography combined enhanced the hiding properties, especially in the Coverless Image Steganography (CIS) methods. In this paper, we propose a Compressive Coverless Image Steganography (CCIS) model, which is a generated-based CIS, to improve the capacity and robustness of Steganography. This model uses the compressive autoencoder to compress the message, thus increasing the capacity, and uses the Generative Adversarial Network (GAN) to generate a stego image from the compressed vector, thus enhancing concealment ability, besides using a regression model and optimization method to extract the data. Another version was designed to enable sending binary messages by replacing the compressive autoencoder network with a robust mapping rule. Experiments show that the proposed model could improve the characteristics of Steganography. Furthermore, the proposed model could extract binary messages with 256 bits from attacked stego images with 99% recovery accuracy. Thus, capacity and robustness were enhanced using our model with both images and text as secret messages. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Generative adversarial networks

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Analysis of community question-answering issues via machine learning and deep learning:State-of-the-art review

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CAAI Transactions on Intelligence Technology 2023年第1期8卷 95-117页

作者： Pradeep Kumar Roy Sunil Saumya Jyoti Prakash Singh Snehasish Banerjee Adnan Gutub Department of Computer Science and Engineering Indian Institute of Information TechnologySuratIndia Indian Institute of Information Technology DharwadIndia Department of Computer Science and Engineering National Institute of TechnologyPatnaIndia The York Management School University of YorkYorkUK Computer Engineering Department College of Computer&Information SystemsUmm Al-Qura UniversityMakkahSaudi Arabia

Over the last couple of decades,community question-answering sites(CQAs)have been a topic of much academic *** have often leveraged traditional machine learning(ML)and deep learning(DL)to explore the ever-growing volume of content that CQAs *** clarify the current state of the CQA literature that has used ML and DL,this paper reports a systematic literature *** goal is to summarise and synthesise the major themes of CQA research related to(i)questions,(ii)answers and(iii)*** final review included 133 *** research themes include question quality,answer quality,and expert *** terms of dataset,some of the most widely studied platforms include Yahoo!Answers,Stack Exchange and Stack *** scope of most articles was confined to just one platform with few cross-platform *** with ML outnumber those with ***,the use of DL in CQA research is on an upward trajectory.A number of research directions are proposed.

关键词： answer quality community question answering deep learning expert user machine learning question quality

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A Length-Adaptive Non-Dominated Sorting Genetic Algorithm for Bi-Objective High-Dimensional Feature Selection

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IEEE/CAA Journal of Automatica Sinica 2023年第9期10卷 1834-1844页

作者： Yanlu Gong Junhai Zhou Quanwang Wu MengChu Zhou Junhao Wen College of Computer Science Chongqing UniversityChongqing 400044China IEEE Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA College of Big Data and Software Engineering Chongqing UniversityChongqing 400044China

As a crucial data preprocessing method in data mining,feature selection(FS)can be regarded as a bi-objective optimization problem that aims to maximize classification accuracy and minimize the number of selected *** computing(EC)is promising for FS owing to its powerful search ***,in traditional EC-based methods,feature subsets are represented via a length-fixed individual *** is ineffective for high-dimensional data,because it results in a huge search space and prohibitive training *** work proposes a length-adaptive non-dominated sorting genetic algorithm(LA-NSGA)with a length-variable individual encoding and a length-adaptive evolution mechanism for bi-objective highdimensional *** LA-NSGA,an initialization method based on correlation and redundancy is devised to initialize individuals of diverse lengths,and a Pareto dominance-based length change operator is introduced to guide individuals to explore in promising search space ***,a dominance-based local search method is employed for further *** experimental results based on 12 high-dimensional gene datasets show that the Pareto front of feature subsets produced by LA-NSGA is superior to those of existing algorithms.

关键词： Bi-objective optimization feature selection(FS) genetic algorithm high-dimensional data length-adaptive

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Enhanced Road Safety Through Intelligent Lane Detection and Driver Drowsiness Monitoring 15

Enhanced Road Safety Through Intelligent Lane Detection and ...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Arunpandian, J. Gunasekaran, V. Vellore Institute of Technology School of Computer Science Engineering and Information Systems Vellore India

ISBN: (纸本)9798350370249

The innovative system outlined in the provided research demonstrates a significant stride towards addressing the growing concern of road safety. Lane departure and driver drowsiness are two major causes of road accidents. In this paper, we present an observation of road lane and drowsiness detection system that uses a camera to capture real-time video footage of the road ahead and applies image processing techniques to detect and track road lanes. The system's road lane detection module employs the canny edge detection algorithm and Hough transform to detect lane lines accurately. The drowsiness detection module of the system analyzes the driver's facial features and tracks the driver's eye movements, blink frequency to identify signs of drowsiness. If the driver found to be sleepy, it issues an audio alert to the driver. The system's capability to detect and track road lanes with precision, along with its ability to identify signs of driver drowsiness, underscores its potential as a promising solution for enhancing road safety. © 2024 IEEE.

关键词： Canny edge algorithm drowsiness lane detection road safety vehicles

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CropViT: A light-weight Transformer Model for Crop Disease Detection 3

CropViT: A light-weight Transformer Model for Crop Disease D...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Chemmalar Selvi, G. Charan, H.J. Kumar, Dinesh Vellore Institute of Technology School of Computer Science Engineering and Information Systems Vellore India

ISBN: (纸本)9798350372120

Agricultural industry has grown significantly bring sustainable farming practices in improving the food quality, enhancing agricultural productivity and global food security. However, the crop yield and its quality are impacted when the timely identification and management of the crop diseases are not promptly solved. Thus, the crop disease detection becomes the critical aspect in the smart agriculture. Several researchers addressed this crop disease detection by developing optimized solutions through machine learning, deep learning and image processing techniques. However, these techniques face serious challenges when deployed in real-time crop disease identification problems like annotated training images, capturing long-range dependencies, adaption to real-time changes evolving over time and so on. This leads to performance degradation seeking for more powerful applications like some advanced deep learning techniques, in particular, the transformer models. In this paper, a novel crop disease detection model known as CropViT was proposed based on Vision Transformer. This CropViT was developed by fine-tuning the architecture of existing Vision Transformer and PlantVillage dataset was used in the experimental study. Nine crop species were selected and trained using CropViT whose performance was then compared with traditional Convolutional Neural Network model. The experimental study clearly showed the highest mean accuracy of 98.64% for CropViT when compared to 95.52% mean accuracy for traditional CNN. The experimental results also outperformed other existing state-of-the-art models. Finally, the work was concluded with the discussion of experimental results and its future scope of work. © 2024 IEEE.

关键词： Convolutional neural networks

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Multi-Objective Equilibrium Optimizer for Feature Selection in High-Dimensional English Speech Emotion Recognition

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computers, Materials & Continua 2024年第2期78卷 1957-1975页

作者： Liya Yue Pei Hu Shu-Chuan Chu Jeng-Shyang Pan Fanli Business School Nanyang Institute of TechnologyNanyang473004China School of Computer and Software Nanyang Institute of TechnologyNanyang473004China College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao266590China Department of Information Management Chaoyang University of TechnologyTaichung413310Taiwan

Speech emotion recognition(SER)uses acoustic analysis to find features for emotion recognition and examines variations in voice that are caused by *** number of features acquired with acoustic analysis is extremely high,so we introduce a hybrid filter-wrapper feature selection algorithm based on an improved equilibrium optimizer for constructing an emotion recognition *** proposed algorithm implements multi-objective emotion recognition with the minimum number of selected features and maximum ***,we use the information gain and Fisher Score to sort the features extracted from ***,we employ a multi-objective ranking method to evaluate these features and assign different importance to *** with high rankings have a large probability of being ***,we propose a repair strategy to address the problem of duplicate solutions in multi-objective feature selection,which can improve the diversity of solutions and avoid falling into local *** random forest and K-nearest neighbor classifiers,four English speech emotion datasets are employed to test the proposed algorithm(MBEO)as well as other multi-objective emotion identification *** results illustrate that it performs well in inverted generational distance,hypervolume,Pareto solutions,and execution time,and MBEO is appropriate for high-dimensional English SER.

关键词： Speech emotion recognition filter-wrapper high-dimensional feature selection equilibrium optimizer multi-objective

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Design of a Hybrid AI- based Phishing Website Detection using LSTM, CNN, and Random Forest based Ensemble Learning Analysis 8

Design of a Hybrid AI- based Phishing Website Detection usin...

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8th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2024

作者： Kavya, S. Sumathi, D. Vellore Institute of Technology School of Computer Science Engineering and Information Systems Vellore India

ISBN: (纸本)9798350367904

Phishing attacks are always surfacing as key threats against internet users, necessitating advanced detection methods. Blacklist-based systems and rule-based models of phishing detection generally have had critical limitations in dealing with evolving tactics and new phishing schemes. Some of these approaches fail to cope with the temporal and visual patterns of phishing sites, which are crucial for timely and accurate detection. To overcome these difficulties, this work introduces a hybrid AI-based phishing website detection model that utilizes several machine learning and deep learning techniques to improve the accuracy of the detection and remove false positives. The proposed model uses LSTM networks, Genetic Algorithms, Random Forest, and CNN through the stacking ensemble framework. Since LSTM is adopted to capture the temporal dependencies in the website traffic and user interaction patterns, this model can effectively model their phishing behavior over time. GA is used for bioinspired feature selection to reduce the dimensionality of features while optimizing model performance. Random Forest is used as a base layer addressing structured features like URL characteristics and WHOIS information. CNNs are incorporated to extract feature content from a webpage and images that carry various visual indicators often used in phishing attacks including counterfeit logos or banners. A meta-classifier is then used to combine the outputs of LSTMs, CNN, and RF and generate the final classification to boost the detection rate. The proposed hybrid model surpasses the existing techniques and facilitates the analysis of temporal, visual, and structured data, making the detection considerably more accurate. Achieving accuracy of as much as 96-97% and having an AUC of 0.97 with a false positive rate below 3%, the model then impacts the more powerful and more flexible phishing detection system, which is then capable of being more protective against higher sophisticated phishing te

关键词： Phishing

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Study on sonic-Assisted processing method for micro pattern formation on Cu film 1

Study on sonic-Assisted processing method for micro pattern ...

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1st International Conference on Robotics, engineering, science, and Technology, RESTCON 2024

作者： Yamamoto, Soma Fukushige, Yuho Nishizawa, Hideaki Khajornrungruang, Panart Suzuki, Keisuke Kyushu Institute of Technology Graduate School of Computer Science and Systems Engineering Fukuoka Japan

ISBN: (纸本)9798350348842

In semiconductor processing to form surface shapes, photolithography and dry etching are used. In this case, the vacuum process requires improvements cost and productivity. We propose a sonic-Assisted processing method using the resonance phenomenon of stainless used steel plate diaphragms. This method is characterized by vibrating the diaphragm with sound pressure to vibrate the particles and microfabricate on Cu thin film [1][2]. To evaluate the sonic-Assisted processing method, we attempted to process Cu wafers by transmitting vibrations perpendicular to the wafer to a SUS plate diaphragm as a micro-shape transfer process using a micro-pattern pad. As a result, 100 μm length and 20 μm width grooves were formed on the Cu wafer surface. © 2024 IEEE.

关键词： Copper

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Multifunctional Pre-processing Approach for Diabetes Mellitus Prediction Using Ensemble Learning 3

Multifunctional Pre-processing Approach for Diabetes Mellitu...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Divya, S. Sudha, M. Vellore Institute of Technology School of Computer Science Engineering and Information Systems Vellore India

ISBN: (纸本)9798350372120

Diabetes mellitus when untreated can result in a number of health issues. It is a metabolic disease marked by abnormal blood glucose levels. Early detection of diabetes improves a person's long-term health by halting its progression. Making better medical decisions can be greatly aided through the implementation into practice automated techniques that can precisely identify pre- and diabetic situations. In this paper Synthetic Minority Oversampling Technique and Adaptive Oversampling Technique is used to address class imbalance problem and Chi-square test, Recursive feature elimination methods are involved in detecting and extracting relevant components from unprocessed data. These techniques are applied to the framework and compared using different ensemble learning classifiers such as XGBoost, AdaBoost, LightGBM and CATBoost. The result shows us CATBoost with ADASYN and RFE provides us with better accuracy of 0.96%. © 2024 IEEE.

关键词： Adaptive boosting

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Enhanced Traffic Management in IoT-Integrated Internet of Vehicles (IoV) with Optimized Routing and Deep Learning Algorithm 5th

Enhanced Traffic Management in IoT-Integrated Internet of Ve...

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5th International Conference on Advances in Distributed Computing and Machine Learning, ICADCML 2024

作者： Sahoo, Arundhati Tripathy, Asis Kumar School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (纸本)9789819735228

The integration of the Internet of Things (IoT) into vehicular networks has paved the way for the exciting development of the Internet of Vehicles (IoV) within Intelligent Transportation systems (ITS). These breakthroughs bring about enhancements in urban transportation services, including improved quality and interaction, reduced costs, more efficient resource utilization, and enhanced traffic management capabilities. The incorporation of IoV into transportation has the potential to address various traffic-related challenges in smart cities. Nevertheless, optimizing routing and monitoring network traffic presents significant challenges, primarily due to limited node-to-node communication capabilities. To tackle these problems, this study proposes the BiLSTM-GreedyNet, which combines the Improved Greedy Perimeter Stateless Routing Algorithm with the Bi-LSTM neural network technology in a 5G network environment. The system delivers impressive results, as evidenced by simulations in MATLAB, boasting a remarkable 98.22% accuracy rate, a low 34.2 MJ energy consumption, and a high Packet Delivery Ratio (PDR) of 91%, all tested with a network of 100 nodes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Internet of things

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