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Enhancing Secure Data Transmission Through Deep Learning-Based Image Steganography and Super-Resolution Generative Adversarial Networks

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SN Computer science 2024年第8期5卷 1138页

作者： Ramesh, Sreyas Sukanth, B.N. Jaswanth, Sathyavarapu Sri Singh, Rimjhim Padam Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

In the era of multimedia technology digital images are essential and keeping them safe from unauthorised access is crucial. To address this issue, the proposed research explores the intersection of image steganography and deep learning for enhanced secure data transmission. It proposes a unique approach that leverages skip-connection-based residual network to extract semantic features and reconstruct images for secure data transmission. This paper introduces a novel steganographic approach combining LSB embedding with sprite image generation instead of directly using image features. Hence, the proposed method effectively hides data within images while maintaining high invisibility. Furthermore, Super-Resolution Generative Adversarial Network (SRGAN) is employed to improve the quality of the reconstructed images. Several other custom and state-of-art convolutional architectures have also been investigated for semantic feature extraction and image reconstruction, including Convolutional Autoencoders (CAE), Deep convolutional network (DCN), DenseNet, InceptionNet and the XceptionNet model, etc. for the comprehensive comparisons against the proposed model. The proposed technique proved to be the best performing model with an SSIM value of 1.00 and PSNR value of 33.04 on the test data. Overall, this research work combines deep learning with steganography to create a secure and hidden communication method. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Data security Least significant bit Sprite image Steganography Super-resolution generative adversarial network

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Convergence of Deep Learning and Forensic Methodologies Using Self-attention Integrated EfficientNet Model for Deep Fake Detection

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SN Computer science 2024年第8期5卷 1-12页

作者： Singh, Rimjhim Padam Sree, Nichenametla Hima Reddy, Koti Leela Sai Praneeth Jashwanth, Kandukuri Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

The immensely increasing number of Deepfake technologies poses significant challenges to digital media integrity, leading to the immediate need for effective Deepfake detection methods. In light of the growing threat posed by sophisticated synthetic media, this paper tackles the urgent need for accurate deepfake detection in real-time by proposing a novel classification model employing a fine-tuned EfficientNet-B0 model with an optimally integrated Self-attention mechanism. The study also analyzes various Deepfake generation techniques by utilizing a suite of Convolutional Neural Networks namely, XceptionNet, NasNetLarge, InceptionV3, ConNextv1, ConvNext2 and EfficientB0 model on the Celeb-DF dataset for baseline model selection and performance comparison purposes. The results prove that the proposed fine-tuned EfficientNet-B0 model with integrated self-attention mechanism is the most superior at identifying complex long-range dependencies and relations in various unconnected image regions, highlighting the importance of attention mechanisms in improving deep learning models for multimedia forensics with a remarkable F-score of 97.67%. In an era marked by the proliferation of sophisticated artificial intelligence (AI)-driven synthetic media, these findings establish the foundation for strengthening digital media platform security and protecting information authenticity. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Celeb-DF dataset EfficientNetB0 Multimedia forensics NasNetLarge Self-attention mechanism Xception Deepfake detection

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A Multi-path Droplet Routing Protocol for Digital Microfluidic Biochip

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Informatica (Slovenia) 2024年第7期48卷 95-102页

作者： Swain, Jyotiranjan Pyne, Sumanta School of Computing and Information Technology REVA University Bangalore India Department of Computer Science and Engineering National Institute of Technology Rourkela India

Digital microfluidic biochip provides an alternative platform to synthesize the biochemical protocols. Droplet routing in biochemical synthesis involves moving multiple droplets across the biochip simultaneously. It involves challenges like moving droplets without violating the fluid constraints. This article proposed a multi-path droplet routing protocol. Multiple routes are explored and validated using special packets. These routes are then classified based on user-defined heuristics. A single route is sequentially selected for each droplet. The routes are then compacted to generate a parallel moving sequence. The simulation result shows 2.087% and 4.952% improvement in the latest arrival time for free and virtual topology, respectively. © 2024 Slovene Society Informatika. All rights reserved.

关键词： Biochips

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Mobile Health Service Adoption: Reducing the Digital Gap and Age’s Moderating Influence

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2024年第17期18卷 139-153页

作者： Muruganantham, B. Karim, Hezerul Abdul Mansor, Sarina Binti Department of Computing Technologies SRM Institute of Science and Technology Tamil Nadu Kattankulathur India Faculty of Engineering Multimedia University Cyberjaya Malaysia

In the past few years, mobile health services (MHS)—a growing topic in the healthcare sector—have received increasing attention. Technologies related to mobile health, or m-health, have several advantages for people, businesses, and medical professionals. Indeed, using m-health can help older people become more proactive patients and lessen the strain on health systems and financial resources. However, the potentially revolutionary impact of m-health is constrained since a large number of senior citizens oppose its adoption, which creates an age-based digital divide. Few studies have examined how public consumers of MHS adopt MHS, even though prior study on this topic is very substantial. We discover that user attitudes towards MHS are positively impacted by perceived benefits and usefulness, perceived barriers, and external cues. Perceived service availability also has a major impact on perceived benefits and usefulness, which, when combined with attitude, directly increases intention. Additionally, we discover that the MHS’s utilization intent has moderating effects. The final section discusses the implications for mobile health marketing. The results indicate that perceived ease of use, vulnerability, and severity are more significant factors for middle-aged and older users to use MHS. The moderator analysis confirmed that different age groups have specific moderating effects on the adoption of mobile health services. © 2024 by the authors of this article.

关键词： Electronic health record

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Enhancing pre-trained models for text summarization: a multi-objective genetic algorithm optimization approach

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Multimedia Tools and Applications 2024年 1-17页

作者： Mohan, G. Bharathi Kumar, R. Prasanna Elakkiya, R. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Birla Institute of Technology and Science Dubai345055 United Arab Emirates

Pre-trained language models have significantly advanced text summarization by leveraging extensive pre-training data to enhance performance. Many cutting-edge models undergo an initial pre-training phase on a large corpus before being fine-tuned specifically for text summarization tasks. The challenge arises when the limited data available for these summarization tasks and the high complexity of the models lead to aggressive fine-tuning, which can cause the models to over fit the training data and fail to generalize well to new, unseen data. To address these challenges, the study proposes a multi-objective based Genetic Algorithm (MOGA) approach for optimizing pre-trained models for summarization The proposed approach first compares the performance of various pre-trained models by fine-tuning them with the CNN/Daily Mail dataset. Secondly, the proposed Multi-Objective Genetic Algorithm (MOGA) is applied to all validated pre-trained models and compared using a statistical approach. Experimental results reveal that Pre-training with Extracted Gap-sentences for Abstractive Summarization Sequence-to-sequence (PEGASUS) model performs best among the pre-trained models. Further optimization with the proposed MOGA method yields even better results, with the optimized model achieving a Recall-Oriented Understudy for Gisting Evaluation) ROUGE-1 score of 0.514, a ROUGE-2 score of 0.4, and a ROUGE-L score of 0.5. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Genetic algorithms

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IoT-Based Fire Alarm System and Motion Detector: A Comprehensive Protocol Analysis for Enhanced Performance 9

IoT-Based Fire Alarm System and Motion Detector: A Comprehen...

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9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Samudrala, Sai Santhoshi Haneesha Thambi, Jaishitha Vadluri, Sai Rishisri Rajagopal, Shinu M. Bhaskaran, Sreebha Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science And Engineering Bangalore India Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science And Engineering Bengaluru India

ISBN: (纸本)9798350394474

Fire alarm systems play a vital role in providing early warnings, facilitating prompt evacuation in emergency situations. These systems ensure the security of individuals by alerting them to possible risks and allowing rapid responses to minimize the impact of fires. The research investigates the integration of IoT devices into a Cisco Packet Tracer simulated environment for fire detectionwithin a centralized architecture using a star topology. The significance of an IoT system determines its ability to simulate and analyse diverse IoT device integrations, evaluate various protocols. The System incorporates smoke detection sensors for precise identification of fire events and motion-detecting sensors seamlessly integrated with cameras for motion capture. Performance of different protocols, including BGP, DHCP, EIGRP, HSRP, ICMP, OSPF, RIPis evaluated in terms of response time. The findings contribute valuable insights into optimizing protocol selection for enhanced real-time responsiveness in IoT-based fire alarm systems. © 2024 IEEE.

关键词： Smoke

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A novel brain tumor segmentation and classification model using deep neural network over MRI-flair images

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Multimedia Tools and Applications 2025年第17期84卷 17645-17676页

作者： Rajendirane, Rajmohan Ananth Kumar, T. Sandhya, S.G. Hu, Yu-Chen Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur Campus Tamil Nadu Kattankulathur India Department of Computer Science and Engineering IFET College of Engineering Tamil Nadu Villupuram India Department of Computer Science and Engineering Annamalai University Chidambaram India Department of Computer Science Tunghai University No. 1727 Sec. 4 Boulevard Xitun District Taichung City407224 Taiwan

Brain tumors pose a significant health concern globally, with their detection and diagnosis being crucial for timely intervention and treatment planning. These abnormal growths can develop within the brain or originate from other parts of the body, spreading to the brain. They can be benign or malignant, and their impact on cognitive and physical function can vary widely depending on their location, size, and type. Detecting brain tumors involves a combination of imaging techniques and clinical assessment. Magnetic Resonance Imaging (MRI) is one of the primary imaging modalities used for this purpose due to its ability to provide detailed images of the brain's anatomy and pathology. Advanced image processing techniques and machine learning algorithms play an increasingly important role in enhancing the accuracy and efficiency of brain tumor detection. This study presents a comprehensive approach for enhancing brain tumor detection in MRI images using a combination of advanced techniques. Firstly, an improved K-means clustering algorithm is introduced to segment tumor regions effectively. Following this, Median Filtering is applied to refine image quality and reduce noise interference. Subsequently, a deep neural network architecture is employed for precise tumor classification utilizing the BRATS dataset. The proposed methodology aims to improve the accuracy and efficiency of brain tumor detection, offering potential advancements in medical image analysis and diagnosis. Experimental results demonstrate promising outcomes, indicating the effectiveness of the proposed approach in accurately identifying tumor regions in MRI scans. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Magnetic resonance imaging

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Network Intrusion Detection System Using Optuna 5

Network Intrusion Detection System Using Optuna

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Parekh, Nishank Sen, Arzob Rajasekaran, P. Jayaseeli, J. D. Dorathi Robert, P. School of Computing Department of Computer Science and Engineering SRM Institute of Science and Technology Tamil Nadu Chengalpattu India School of Computing Department of Computing Technologies SRM Institute of Science and Technology Tamil Nadu Chengalpattu India

ISBN: (纸本)9798331518097

The rapid growth of the digital industry has created a higher demand for robust Network Intrusion Detection Systems (NIDS) to protect valuable information and the integrity of network infrastructures as the digital industry grows rapidly. One of the most important challenges in the current intrusion detection landscape is the growing sophistication of cyber threats, including zero-day attacks, polymorphic malware, and advanced persistent threats, which are difficult to detect using traditional methods. Furthermore, systems often suffer from high false positive rates and struggle to scale effectively in real-time applications. Traditionally, intrusion detection methods were quite effective, but performance is still lacking due to the inability to adapt to evolving threats. Recent breakthroughs include deep learning approaches, ensemble methods, and hybrid detection models. However, these are still plagued by high computational overhead and a lack of transparency in their decision-making processes. The work exploits Optuna for the optimization of hyperparameters, specifically in the performance improvement of various ML models. Among the best-ranked frameworks for the optimization of hyperparameters, Optuna provides a principled method for tuning hyperparameters, resulting in significantly enhanced accuracy and efficiency of the intrusion detection model. The implication of this research work is that it searches for the best configuration of parameters for each algorithm with balanced false positives and detection rates. The study includes an overall scenario of recent development in NIDS. More precisely, this paper shows how Hyperparameter tuning attains very superior model performance compared to other models. The comparative results presented have shown that models which are optimized using Optuna surpass the non-optimized ones by a huge margin with respect to accuracy, recall, precision, and F1-score. The paper also discusses ensemble techniques by integrating the

关键词： Network intrusion

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NeurDB: an AI-powered autonomous data system

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science China(Information sciences) 2024年第10期67卷 129-150页

作者： Beng Chin OOI Shaofeng CAI Gang CHEN Yanyan SHEN Kian-Lee TAN Yuncheng WU Xiaokui XIAO Naili XING Cong YUE Lingze ZENG Meihui ZHANG Zhanhao ZHAO School of Computing National University of Singapore College of Computer Science and Technology Zhejiang University Department of Computer Science and Engineering Shanghai Jiao Tong University School of Information Renmin University of China School of Computer Science and Technology Beijing Institute of Technology

In the wake of rapid advancements in artificial intelligence(AI), we stand on the brink of a transformative leap in data systems. The imminent fusion of AI and DB(AI×DB) promises a new generation of data systems, which will relieve the burden on end-users across all industry sectors by featuring AI-enhanced functionalities, such as personalized and automated in-database AI-powered analytics, and selfdriving capabilities for improved system performance. In this paper, we explore the evolution of data systems with a focus on deepening the fusion of AI and DB. We present NeurDB, an AI-powered autonomous data system designed to fully embrace AI design in each major system component and provide in-database AI-powered analytics. We outline the conceptual and architectural overview of NeurDB, discuss its design choices and key components, and report its current development and future plan.

关键词： AI$\times$DB in-database AI intelligent data system

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Signature Based Identification using Locality-Sensitive Hashing for IoT Devices 2

Signature Based Identification using Locality-Sensitive Hash...

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2nd IEEE International Conference on Distributed computing and Electrical Circuits and Electronics, ICDCECE 2023

作者： Singh, Amit Sindhu Madhuri, G. Teerthanker Mahaveer University College of Computing Science & It Department of Computing Science & It Moradabad India Faculty of Engineering and Technology Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350347456

On current days, the growing applications in Internet of Things (IoT) is increasing in various real-time sectors as smart city applications (air pollution, atmospheric noise canceling and traffic flow management). Now, these technologies are combined to provide the comfort configuration for each application. However, malware / virus are highly possible to extract the device software or hardware information in IoT. All sensed information about the environment or any event is stored in these devices which are hacked by the malicious attackers. To overcome these adversities, we introduced the concept of locality sensitive hash (LSH) for IoT device trustworthiness identification. The main objective of our proposed system is to allow secure transfer of data from the resource constrained devices. In this project, the resource constrained devices are the wireless sensor nodes. The experimental analysis confirmed that our proposed approach is suitable for the application or environment with resource constrained devices. Also, the time, energy, latency and packet overhead is considerably reduced than the existing systems considered for this project. © 2023 IEEE.

关键词： Internet of things

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