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A Comprehensive Study on Various Encryption Techniques for Digital images in Multimedia Networks 2

A Comprehensive Study on Various Encryption Techniques for D...

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2nd IEEE International conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Santhosha, Benaka Sudheesh, K.V. Kiran, K. Parameshachari, B.D. Dayanand Sagar University Harohalli Bengaluru Visvesveraya Technological University Department of CSE Belagavi India Vidyavardhaka College of Enginerring Mysuru Visvesveraya Technological University Department of ECE Belagavi India Nitte Meenakshi Institute of Technology Department of ECE Bengaluru India

ISBN: (纸本)9798350317558

image encryption is a fundamental component of modern data security that guarantees the integrity, privacy, and confidentiality of sensitive visual content. This paper provides a thorough examination of image encryption, comparing and contrasting different encryption techniques and their benefits and limitations as well as real-world uses. In order to strengthen image data security against unwanted access and manipulation, we first offer some basic understanding of image encryption. The suitability of symmetric, asymmetric, and hybrid encryption techniques in various contexts is examined. We also explore the assessment criteria used to evaluate encryption algorithms, highlighting the significance of using suitable measures to precisely gauge security and effectiveness. We also discuss common issues with image encryption, like complicated key management and computational complexity. The review also explores future directions that picture encryption might take, including multi-media encryption techniques, resistance to cryptanalysis, and quantum image encryption. We also highlight how important image encryption is in a variety of fields, such as finance, healthcare, journalism, intellectual property protection, and military operations. We will conduct a thorough analysis of current cryptography schemes and multimedia encryption algorithms to provide a comprehensive overview of the current security landscape tailored to digital multimedia technology. The findings from this survey will enhance our understanding of the efficacy and dependability of secure multimedia encryption schemes, ultimately aiding in the development of more efficient and robust encryption methods for the future. This article aims to summarize and assess numerous algorithms using different methodologies based on multiple characteristics such as MSE, PSNR, NC, BER, and so on. © 2024 IEEE.

关键词： Cryptography image Multimedia Networks Security Attacks Steganography Watermarking

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Automated image Captioning and Speech Synthesis 2

Automated Image Captioning and Speech Synthesis

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2nd IEEE International conference on Futuristic Technologies, INCOFT 2023

作者： Mankawade, Amruta Pavitha, N. Vpatil, Amruta Vishwakarma Institute of Technology Department of Artificial Intelligence and Data Science India Vishwakarma University Department of Computer Engineering India Sjjtu Rajasthan India

ISBN: (纸本)9798350308846

An innovative system that creates natural language captions for supplied photos and transforms them to voice for increased accessibility is Automated image Captioning and voice Synthesis. The suggested system extracts important information from the image and generates a relevant and coherent caption using cutting-edge technologies of deep learning like Convolutional Neural Networks & Recurrent Neural Networks. Text-to-speech synthesis techniques are then used to translate the generated captions to speech, allowing visually challenged users to access the material. The suggested approach was tested using typical benchmark datasets and yielded encouraging results. Because the system can create natural language captions and convert them to voice, it May be used in diverse applications, consisting of media accessibility & assistive technology. © 2023 IEEE.

关键词： Deep learning image captioning LSTM RNN

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Plant Disease Detector and Treatment Provider 2

Plant Disease Detector and Treatment Provider

引用

2nd IEEE International conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2024

作者： Jadhav, Anjali Prasad, Bhavya Bala, Sai Sleghana Sndt Women's University Computer Science and Technology Usha Mittal Institute of Technology Mumbai India

ISBN: (纸本)9798350360523

Plant diseases hinder the quality and productivity of plants. Detection, correction and premature prevention is essential for the agriculture industry. This paper classifies plant diseases by assessing leaf images using a convolution neural network(CNN) model, a deep learning algorithm which trains on image features such as color, shape, and veins as input metrics with an accurate prediction probability of 95%. The data set used consists of the plant leaves from the Plant Village data set which include 26 varieties of crop species with 14 diseased variants. User input data into the application is securely stored authenticated and stored using SHA256 checksum. © 2024 IEEE.

关键词： image classification

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Deep Learning Techniques for Identification of Pneumonia: A CNN Approach 2

Deep Learning Techniques for Identification of Pneumonia: A ...

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2nd International conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2024

作者： Das, Ruchika Nayak, Debasish Swapnesh Kumar Rout, Chinmayee Priyadarshini Jena, Lambodar Swarnkar, Tripti Siksha 'o' Anusandhan University Department of Computer Science and Engineering Bhubaneswar India Nalanda Institute of Technology Department of Computer Application Bhubaneswar India Siksha 'O Anusandhan University Department of Data Science Bhubaneswar India Siksha 'o' Anusandhan University Department of Computer Application Bhubaneswar India

ISBN: (纸本)9798350370188

Pneumonia is an inflammatory lung disease that mostly affects the tiny air sacs known as alveoli in human bodies. Pneumonia may be caused by bacteria, viruses, or fungi. Mild to severe symptoms may include a mucus-producing cough and a fever, appals, and difficulty to breath. The approach most usually used to diagnose pneumonia is chest X-ray (CXR) imaging. images from a CXR screening can be used as an alternative or confirmation method because they are simple and affordable to acquire. Examining the chest X-rays however is a hard task that might be vulnerable to subjective variation. We used a deep learning technique in this investigation. Convolutional neural networks (CNNs) for deep learning were employed in this paper. In his work, the proposed model is applied to the original CXR image dataset and the augmented image dataset. We generated the augmented images from the existing CXR image dataset and deployed VGG-19, a component of the CNN model to create an autonomous Pneumonia detection system. The suggested techniques that have been employed include scaling the image, adding extra information, deep learning using Keras, batch normalization, and finally using weights from the pre-trained Keras model (VGG19). Finding pneumonia sickness is difficult and takes time. In this study, we address the issue and demonstrate how to detect pneumonia disease more quickly and accurately. Our proposed model shows an accuracy of 95% on the augmented CXR image dataset. © 2024 IEEE.

关键词： Convolutional neural networks

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Efficient and Lightweight Framework for Confidential Medical image Search using Edge Computing 2

Efficient and Lightweight Framework for Confidential Medical...

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2nd International conference on Computational Intelligence and Network Systems, CINS 2024

作者： Rajan, Arun Amaithi Vetriselvi, V. Aishwarya, R. Rajan, Anitha Amaithi Security Research Laboratory Department of Computer Science and Engineering College of Engineering Guindy Anna University Tamil Nadu Chennai600025 India Department of Computer Science and Business Systems Francis Xavier Engineering College Tamil Nadu Tirunelveli627011 India

ISBN: (纸本)9798331504106

In healthcare, medical images are playing a major role in accurate diagnosis. Cloud storage for medical images creates security risks and latency due to some critical vulnerabilities and their distance from data sources. By bringing computation closer to the source, edge computing improves efficiency, still edge servers cannot be trusted blindly, requiring robust security measures. In this article, an efficient and lightweight framework for secure medical image search and retrieval that leverages edge computing is proposed. Here, Medical images are encrypted and similarity-preserving hashcodes are generated locally at the source, which are sent to a trusted master edge server. Before being stored, searchable hashcodes undergo homomorphic encryption, allowing for secure computations on encrypted data. The master edge server generates n shares of the encrypted images by applying a Learning With Errors (LWE) based image sharing scheme and distributing them across nearby edge devices. During retrieval, to ensure resilience against device compromise, m out of n shares are used to reconstruct the image. The master edge server retrieves and reconstructs the top k relevant images in response to the query image. This framework enhances security and efficiency by reducing reliance on cloud storage and enabling secure processing on edge servers, making it ideal for modern healthcare environments. The efficiency and security of the proposed framework are proved theoretically and experimentally. Hence, it proved that the framework is performing better than existing state-of-the-art models. ©2024 IEEE.

关键词： Electronic health record

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Deep Masked Input UNet Framework based Lips Segmentation prediction for Gender Classification 2

Deep Masked Input UNet Framework based Lips Segmentation pre...

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2nd IEEE International conference on Advances in Information Technology, ICAIT 2024

作者： Devi, M. Shyamala Hepzhibha Rachel, S. Janani, G. Irene, B. Joy Praisy, E. Humaira, S. Department of Computer Science and Engineering Panimalar Engineering College Tamilnadu Chennai India Department of Artificial Intelligence and Data Science Panimalar Engineering College Tamilnadu Chennai India

ISBN: (纸本)9798350383867

Assessments have demonstrated that the human lip and its motions provide a wealth of knowledge about the identity and substance of communication. However, due to large differences in illumination condition, head perspective, and background, obtaining strong and precise lip image segmentation in natural settings remains difficult. This paper recommends Deep Masked Input UNet that categorizes the Gender based on the lip segmentation with high precision. For implementation, 10,132 face images from the KAGGLE Lip Segmentation dataset were used. The dataset includes 5066 images of the face and 5066 segmented lip images. The proposed Deep Masked Input UNet starts by masking the original picture with a segmented lip image to create masked face images with lips segments. Utilizing the Relu Activation function, Deep Masked Input UNet with contracting and expansion was enabled. The masked lip images are fitted to the proposed Deep Masked Input UNet and traditional CNN models. The results show that the suggested Deep Masked Input UNet model performs better in lip segmentation and gender classification, with a high accuracy of 98.95%. © 2024 IEEE.

关键词： image segmentation

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Exploring the Cosmos with Machine Learning: Advancements in Astronomical data Processing 2

Exploring the Cosmos with Machine Learning: Advancements in ...

引用

2nd International conference on data Science and Information System, ICDSIS 2024

作者： Ramya, B.N. Kashyap, Dheeraj N. Varun, G. Kashyap, Varun Kumble, Sameer Phaniraj Jyothy Institute Of Technology Department Of Ai&ml Bengaluru India

ISBN: (纸本)9798350381665

Astronomical Object Classification stands as a critical endeavor in modern astronomy, facilitating the systematic categorization and analysis of celestial phenomena. In our research, we address this challenge by leveraging the Recursive Convolutional Neural Network (RCNN), a well-established deep learning architecture known for its effectiveness in object detection and classification tasks. Our approach hinges on the exploitation of a vast dataset comprising 16,697 raw images sourced from various sources, encompassing a wide array of celestial objects such as stars & galaxies. The RCNN architecture, characterized by its ability to combine region proposals with deep convolutional networks, serves as the cornerstone of our classification framework. Through an extensive training sample, our model meticulously learns intricate patterns and distinguishing features inherent in different astronomical objects, enabling it to make accurate predictions. Our evaluation metrics reveal a commendable accuracy rate of 93.83% on the test dataset, underscoring the robustness and efficacy of our proposed approach. This not only highlights the potential of deep learning techniques in automating astronomical classification tasks but also underscores their significance in advancing our understanding of the cosmos. © 2024 IEEE.

关键词： image classification

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Uncertainty-Aware Prototype Learning for Open-Set Hyperspectral image Classification 2

Uncertainty-Aware Prototype Learning for Open-Set Hyperspect...

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2nd International conference on Pattern Recognition, Machine Vision and Intelligent Algorithms, PRMVIA 2024

作者： Xie, Zhuojun Duan, Puhong Liu, Wang Kang, Xudong Hunan University College of Electrical and Information Engineering Changsha China Hunan University School of Robotics Changsha China

ISBN: (纸本)9798350389746

Hyperspectral image classification, aiming to assign an unique label for each pixel, has garnered much more attention in recent years. However, current most classification approaches assume that all classes between training and test sets are consistent, which limits their applications for identifying the unknowns. In this article, an uncertainty-aware prototype learning model (UAPLM) is proposed for open-set hyperspectral image classification. First, a spectral-spatial convolutional network is utilized to extract the high-order semantic information from input data. Then, feature consistency and sample uncertainty are estimated to obtain the probability of an unknown class. Finally, the known class probabilities obtained by the fully connected layer and the unknown class probabilities are integrated to yield the final classification result. Experiments on several datasets demonstrate that the proposed UAPLM can better recognize the known and unknown samples compared to other open-set classification methods. © 2024 IEEE.

关键词： Adversarial machine learning

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Advanced Vision-Language Pipelines: Contextual Learning and Interactive Segmentation for Medical Imaging 2

Advanced Vision-Language Pipelines: Contextual Learning and ...

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

作者： Sabariram, Sachin M Sanjay Vikram, C.B. Sharon Deborah, E. Kavin, M. Saravanan, G. Erode Sengunthar Engineering College Erode Department Of Artificial Intelligence And Data Science Erode India

ISBN: (纸本)9798331505745

Medical imaging plays a pivotal role in modern diagnostics, yet existing AI-based pipelines face significant challenges, including reliance on proprietary datasets, limited modality support, and inadequate explainability. This research introduces a scalable, end-to-end pipeline that integrates anomaly detection, segmentation, and diagnostic report generation to address these limitations. By leveraging a VisualLanguage Model (VLM) trained on context-rich image-text pairs generated from open-source label-only datasets using a fine-tuned Large Language Model (LLM), the system ensures scalability across modalities and protects patient privacy by eliminating reliance on proprietary medical data. Interactive, text-driven segmentation is achieved through the integration of a CLIP-based Segment Anything Model (CLIP-SAM), enabling clinicians to refine anomaly boundaries with high precision. A fine-tuned LLaMA-based LLM, employing Chain-of-Thought reasoning, generates detailed, actionable diagnostic reports tailored to clinical needs. Experimental evaluations on MRI, CT, and X-ray datasets demonstrate an Area Under the Curve (AUC) of 0.91 for anomaly detection, a Dice Similarity Coefficient (DSC) of 84.5 % for segmentation, and a BLEU score of $\mathbf{8 5. 6 \%}$ for report quality, significantly surpassing state-of-the-art baselines. This robust and explainable framework establishes a new standard for automated medical imaging analysis, supporting diverse imaging modalities and aligning AIgenerated insights with clinical decision-making. © 2025 IEEE.

关键词： image segmentation

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A Document image Moiré Pattern Reduction Method Based on Convolutional Neural Networks 2

A Document Image Moiré Pattern Reduction Method Based on Co...

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2nd International conference on image Processing, Computer Vision and Machine Learning, ICICML 2023

作者： Liu, Limei Cao, Jiajian Luo, Xin Hunan University of Technology and Business Department of Frontier and Interdisciplinary Changsha China

ISBN: (纸本)9798350331417

In recent years, the removal of Moiré patterns from images has been a challenging problem in computer vision, especially for document images. Many deep learning-based image denoising methods have been proposed to address the problem of Moiré pattern removal, but these methods still have issues with recovering textural details and globally removing Moiré patterns. This paper proposes a coarse-to-fine two-stage convolutional neural network to address the problems of incomplete Moiré pattern removal and loss of textural details. In the first stage, the model estimates the positions of the Moiré patterns and performs preliminary removal. In the second stage, the focus is on optimizing the preliminary removal results, which avoids the problems of incomplete removal and loss of textural details. In particular, we replace the loss function in the second stage with a focus on improving image quality, and add a Non-Local module to increase the receptive field, paying more attention to the information around the Moiré patterns. Finally, experimental results on the public dataset of Baidu PaddlePaddle demonstrate that our model outperforms other convolutional neural network models. © 2023 IEEE.

关键词： Convolutional Neural Network Document image Moire pattern removal

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