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Exploring information hiding in images – problems and measures: a survey

Multimedia Tools and Applications 2025年 1-63页

作者： Khan, Ahmed Sarfaraz, Aaliya School of Computer Science Taylor’s University Subang Jaya Malaysia School of Computing and Artificial Intelligence Faculty of Engineering and Technology Sunway University No. 5 Jalan Universiti Selangor Darul Ehsan Bandar Sunway47500 Malaysia

The widespread adoption of digital communication technologies across various domains has led to a significant shift towards digitalization. However, this evolution has also introduced vulnerabilities, including unauthorized access, data manipulation, and fabrication by malicious entities, which severely impact the confidentiality and integrity of information. The proliferation of digital images on the internet has facilitated the use of these media for embedding confidential data in a way that prevents detection or extraction by unauthorized third parties. Despite numerous efforts, existing image steganography and watermarking techniques often lack robustness. This paper proposes several novel strategies to enhance the dynamism and imperceptibility of these techniques, integrating cryptographic methods by manipulating the entropy of encrypted images using minimal pixel value differences and applying unique image transformations. Additionally, the study highlights that implementing multi-layered security mechanisms can protect confidential information from modern statistical steganalysis attacks, thereby preserving both the integrity of hidden data and image quality. A comprehensive review of information hiding, specifically focusing on image watermarking and steganography, is provided. Furthermore, this survey outlines key problem statements and offers quantitative metrics for future research in this area. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Steganography

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Comparative Study of Customized CNN Model and Transfer Learning for Brain Tumor Classification

Comparative Study of Customized CNN Model and Transfer Learn...

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2023 IEEE International Conference on Network, Multimedia and Information technology, NMITCON 2023

作者： Vaishnavi, N. Sinchana, K. Kodipalli, Ashwini Rao, Trupthi Kumarswamy, S. Global Academy of Technology Department of Computer Science and Engineering Karnataka Banglore India Global Academy of Technology Department of Artificial Intelligence and Data Science and Engineering Karnataka Bangalore India

ISBN: (纸本)9798350300826

Brain tumors are abnormal cell growths in the brain, which can be cancerous or non-cancerous. They have diverse effects, including cognitive and neurological impairments, as well as personality and behavioral changes. This study proposes a multiclass classification framework utilizing a plain CNN architecture and Transfer learning to accurately identify brain tumor types: meningioma, glioma, pituitary tumor, and no tumor. We have explored transfer learning models such as VGG16, AlexNet, and ResNet50, these are initialized with pre-trained weights and fine-tuned on a comprehensive brain tumor dataset which has an equal distribution among classes. Optimizers such as Adam, AdaDelta, and SGD are modified to enhance performance. AdaDelta was used as the VGG16 optimizer, and it helped us achieve the highest accuracy of 99.83% with a loss of 0.74%. ResNet50 on the other hand, had the lowest accuracy 70% and highest loss 66.68%. While AlexNet without dropouts and AdaDelta as optimizer gives highest loss of 92.83%. The custom CNN model's loss was 0.04%, which is the lowest of any of them. The findings contribute to robust tools for accurate brain tumor diagnosis and treatment planning. © 2023 IEEE.

关键词： Tumors

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Exploring the Role of Internet of Things in Wireless Sensor Networks for Industry 4.0 Applications 1

Exploring the Role of Internet of Things in Wireless Sensor ...

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1st International Conference on Pioneering Developments in Computer science and Digital Technologies, IC2SDT 2024

作者： Palarimath, Suresh Maran, Pyingkodi N.r, Wilfred Blessing Sujatha, T. Renuga, S. R.s, Chithra College of Computing and Information Sciences University of Technology and Applied Sciences Salalah Oman Kongu Engineering College Department of Computer Application Erode India College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Oman Sri Krishna College of Engineering and Technology Department of Artificial Intelligence and Data Science Coimbatore India Panimalar Engineering College Department of Artificial Intelligence and Data Science Chennai India Ponjesly College of Engineering Department of Computer Science and Engineering Kanyakumari India

ISBN: (纸本)9798350365016

Recent developments in computer networking have made it possible to access information from a faraway location via either wireless or wired networks. The recent advancements in wireless infrastructure are directly responsible for the advent of wireless sensor networks, often known as WSNs. These networks make it easier to keep track of environmental happenings and activities, as well as document them and manage them. In WSNs, data relaying is accomplished through the use of a variety of routing strategies. Industry 4.0, also known as the fourth industrial revolution, is characterized by the incorporation of highly developed physical automation systems. These systems are made up of several pieces of machinery and gadgets that are linked together by sensors and are operated by software. The goal of the fourth industrial revolution is to improve the effectiveness and dependability of business processes. The Internet of Things (IoT) can be leveraged in manufacturing, which gives a means of linking engines, power grids, and sensors to the cloud in an industrial context. This is accomplished through the use of Industrial IoT. The purpose of this paper is to gain an understanding of how the Internet of Things functions in wireless sensor networks and its possible applications in a variety of settings. © 2024 IEEE.

关键词： Wireless sensor networks

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An Overview of Different Types of Recommendations Systems-A Survey 4

An Overview of Different Types of Recommendations Systems-A ...

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4th International Conference on Innovative Trends in Information technology, ICITIIT 2023

作者： Duraisamy, Premkumar Yuvaraj, S. Natarajan, Yuvaraj Niranjani, V. KPR Institute of Engineering and Technology Department of Artificial Intelligence and Data Science Coimbatore India Sri Eshwar College of Engineering Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9781665494144

In recent years the boom of internet and social media usage everyone spend their invaluable time in social media app and looking for the solution for all kind of their problems. This work analysis deeply on how recommendation system works and its types in different platforms. Most of the modern recommendation system use machine learning algorithms like linear regression, random forest regression and support vector model with collaborative filtering method. Recommendation is nothing but an choice making system. It is vary from person to person based on their interest, culture, locality, education background, interpersonal skills etc., The huge item can be filtered from one by one based on each parameter and finally it will reach the right recommendation item. The research community has worked tremendous way in the field of recommendation system and produced huge variety of result. This survey enlightening the ideas about variety of recommendation system and techniques used by the research community. © 2023 IEEE.

关键词： Recommender systems

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Comparative Analysis of Optimizers and Transfer Learning Methods for Headgear Detection Using Convolutional Neural Network

Comparative Analysis of Optimizers and Transfer Learning Met...

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2023 IEEE International Conference on Network, Multimedia and Information technology, NMITCON 2023

作者： Sri Sai Teja, M.S. Ullas, P. Vikas, K.R. Kumarswamy, S. Kodipalli, Ashwini Rao, Trupthi Global Academy of Technology Department of Computer Science and Engineering Karnataka Bangalore India Global Academy of Technology Department of Artificial Intelligence and Data Science and Engineering Karnataka Bangalore India

ISBN: (纸本)9798350300826

In untapped nations, cars are quite uncommon because motorcycles have always dominated the transportation industry. Over the past few years, there has been an increase in motorbike accidents. Motorcyclists who do not use fluorescent helmets because they don't think they offer enough protection are frequently engaged in traffic accidents. Today, it is against the law to operate a motorbike without a helmet, which increases accidents and fatalities in India. Every motorcyclist must wear a helmet when operating a motorcycle by law for the protection of the public. However, the majority of people continue to disprove this by driving without wearing helmets. In order to solve this problem, we provide a method in this work that uses real-time surveillance footage to automatically identify motorcycle riders without helmets and identify license plates. When the traffic police observe motorbike riders riding without helmets in a group at intersections, they also use CCTV footage to seize control of the drivers of those cars and fine the helmet-less riders. But only human initiative and dedication can make it happen. Because of its great accuracy, Convolutional neural networks are utilized for picture categorization and recognition. The Convolutional neural network uses a hierarchical model that builds a network in the shape of a funnel and eventually produces a fully connected layer where all the neurons are connected to one another and the output is processed. The 764 photos in the helmet detection dataset that is accessible on Kaggle have bounding box annotations in the PASCAL VOC format. © 2023 IEEE.

关键词： Convolutional neural networks

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AI-Enhanced Imaging Techniques for Understanding Alzheimer's Progression 2

AI-Enhanced Imaging Techniques for Understanding Alzheimer's...

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Chaudhari, Aarti Saratkar, Saniya Thute, Trupti Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha442001 India

ISBN: (纸本)9798331505745

Alzheimer's disease (AD) is a fatal neuronal disorder which occurs mainly in the elderly and causes a gradual dementia with or without memory loss, functional disability and death. AD is the most prevalent form of dementia, and the condition is positively affiliated with amyloid-beta plaques, tau protein tangles, neuronal ischemic losses, and brain atrophy. Even after studying for a long, the cause(s) of AD has not been well established, though it is well understood that age, genetics (specifically APOE ?4 gene), and lifestyle are some of the leading causes. At the moment there is no cure and management involves the alleviation of symptoms and the prevention of disease progression. The key for better patient management is the early-stage triage as this would increase the chance of getting proper care, fulfilling criteria for clinical trials, and receive better therapeutic management. For a long time now, an early diagnosis has been emphasized due to the improved prognosis, but little has been done in that area instead a lot of efforts are directed at treatment. MRI, PET, and fMRI consisting of imaging approaches that enables the identification of brain changes and progression of the disease. Healthcare & medicine;artificial intelligence (AI) is changing the way Alzheimer's is diagnosed and treated. In fact, classifiers such as Convolutional Neural Networks, Recurrent Neural Networks, and Transformer models can investigate imaging data and distinguish the features of early stage, progression, and survive rate, so as to assist with medical treatment planning. The general strength of AI to analyses and manage large amounts of information provides new chances for enhancing clinical practice and investigation in Alzheimer's disease. © 2025 IEEE.

关键词： Neurodegenerative diseases

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Retinal Disease Classification: Binary and Multiclass Perspectives

Retinal Disease Classification: Binary and Multiclass Perspe...

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Sentiment Analysis and Deep Learning (ICSADL), International Conference on

作者： Rutuja Nitin Lanjewar Utkarsha Pacharaney Department of Artificial intelligence and Data science Faculty of Engineering and Technology DMIHER(DU) Wardha Maharashtra

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

Retinal diseases, among which diabetic retinopathy, age-related macular degeneration, and glaucoma account for the greatest number of blindness cases worldwide, affect more than 338 million people. It is essential that early detection leads to timely intervention to preserve vision, but it is limited due to the ever-increasing demand for ophthalmic care and a shortage of specialists. Machine learning-driven systems, especially Convolutional Neural Networks (CNNs), have shown great promise in overcoming these challenges, achieving high accuracy in medical imaging tasks. This paper explores a unified framework for both multiclass and binary classification of retinal diseases, addressing issues like class imbalance and inter-class similarity while leveraging CNNs for robust feature extraction. Techniques like Grad-CAM also increase model interpretability, allowing clinicians to understand and trust AI-driven diagnostic processes.

关键词： Macular degeneration Glaucoma Sentiment analysis Medical services Retina Feature extraction data models Planning Convolutional neural networks Diseases

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Criminal Identification Based On database Management Using Blockchain 4

Criminal Identification Based On Database Management Using B...

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4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Suganya, R. Ashok, P. Lekshitha, J. Shanmuga Priya, S. Monica, K. Somasundaram, K. Sri Sai Ram Institute of Technology Department of Computer Science and Engineering Chennai India Saveetha Engineering College Department of Artificial Intelligence and Data Science Chennai India Sri Muthukumaran Institute of Technology Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798331508845

ISBN: (纸本)9798331508845

The crime monitoring system is a unique and authentic project which functions with the concepts of block chain language. Blockchain technology has the potential to revolutionize the management of criminal records by providing a secure network where documents are readily accessible and immune to tampering. This innovation ensures the safety and integrity of sensitive information, offering a robust solution to the challenges posed by traditional recordkeeping system. The basic use of the crime identification system is to maintain and process the criminal records and data that are given by the user. This can be used in the security system during the arrival of dignitary people for the occasional events by recognizing the people based upon the criminal records. Thus, criminals are restricted from entering inside the event. This can also be embedded for the security cameras of the event and are constantly monitored. © 2024 IEEE.

关键词： Records management

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Video Summarization Approach Based on Binary Robust Invariant Scalable Keypoints and Bisecting K-Means

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Computers, Materials & Continua 2024年第3期78卷 3565-3583页

作者： Sameh Zarif Eman Morad Khalid Amin Abdullah Alharbi Wail S.Elkilani Shouze Tang Department of Information Technology Faculty of Computers and InformationMenoufia UniversityMenoufia32511Egypt Department of Artificial Intelligence Faculty of Artificial IntelligenceEgyptian Russian UniversityBader11829Egypt Department of Computer Science Community CollegeKing Saud UniversityRiyadh11362Saudi Arabia College of Applied Computer Science King Saud UniversityAl-Muzahmiya19676Saudi Arabia School of Communication and Information Engineering Chongqing University of Posts and TelecommunicationsChongqing400065China

Due to the exponential growth of video data,aided by rapid advancements in multimedia *** became difficult for the user to obtain information from a large video *** process of providing an abstract of the entire video that includes the most representative frames is known as static video *** method resulted in rapid exploration,indexing,and retrieval of massive video *** propose a framework for static video summary based on a Binary Robust Invariant Scalable Keypoint(BRISK)and bisecting K-means clustering *** current method effectively recognizes relevant frames using BRISK by extracting keypoints and the descriptors from video *** video frames’BRISK features are clustered using a bisecting K-means,and the keyframe is determined by selecting the frame that is most near the cluster *** applying any clustering parameters,the appropriate clusters number is determined using the silhouette *** were carried out on a publicly available open video project(OVP)dataset that contained videos of different *** proposed method’s effectiveness is compared to existing methods using a variety of evaluation metrics,and the proposed method achieves a trade-off between computational cost and quality.

关键词： BRISK bisecting K-mean video summarization keyframe extraction shot detection

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Analysis of Multi-Class Convolution Neural Network on Garbage Classification System

Analysis of Multi-Class Convolution Neural Network on Garbag...

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2023 IEEE International Conference on Network, Multimedia and Information technology, NMITCON 2023

作者： Vikas, K.R. Ullas, P. Sri Sai Teja, M.S. Kumar Swamy, S. Kodipalli, Ashwini Rao, Trupthi Global Academy of Technology Department of Computer Science and Engineering Karnataka Bangalore India Global Academy of Technology Department of Artificial Intelligence and Data Science and Engineering Karnataka Bangalore India

ISBN: (纸本)9798350300826

The buildup of solid waste in metropolitan areas is a major worry that, if not effectively handled, might lead to environmental contamination and be dangerous to human health. To manage a range of waste products, it's crucial to have an advanced/intelligent waste management system. The act of separating garbage into its many components is one of the most crucial parts of waste management, and it is often carried out manually by hand-picking. To simplify this, we suggest a customized architecture for multiclass Convolutional Neural Networks (CNNs). The design includes elements that are individual-specific, enabling the model to adjust to the traits of each class. Extensive evaluation has shown that our CNN architecture, utilising a personalised approach, outperforms traditional CNN models like ResNet50 and VGG16 in multiclass classification situations. The effectiveness and resilience of our suggested architecture are demonstrated by experimental results on our dataset, opening the path for increased classification accuracy of 71.15%. © 2023 IEEE.

关键词： Classification (of information)

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