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Comparative Study of image Encryption and image Steganography Using Cryptographic Algorithms and image Evaluation Metrics 1

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2nd International Semantic Intelligence conference (ISIC)

作者： Chavali, Surya Teja Kandavalli, Charan Tej Sugash, T. M. Prakash, G. Amrita Vishwa Vidyapeetham Amrita Sch Engn Dept Comp Sci & Engn Bengaluru Karnataka India Vellore Inst Technol Sch Comp Sci & Engn Vellore 632014 Tamil Nadu India

ISBN: (数字)9789811971266

ISBN: (纸本)9789811971280;9789811971266;9789811971259

In this paper, Steganography is carried out by converting the message hidden into ciphertext using renowned cryptographic algorithms like Advanced Encryption Standard (AES) and Triple data Encryption Algorithm (TDEA or Triple DEA). The embedding technique produces a Stego image. Considering this as the first level of encryption, we then encrypted the whole stego image using AES and TDEA algorithms, giving us the second layer of encryption. The encrypted stego image will be returned in enc format, unreadable without the proper key. We have good resistance to brute-force, statistical, and differential attacks due to the experimental results of two tiers of protection. In addition to the encryption and decryption process, this paper also talks about image evaluation metrics like PSNR value and SSIM value, performed on stego images, which use ciphertext from AES and TDEA algorithms that provides the statistical information.

关键词： image Encryption & Decryption image Steganography Hashing SHA-256 MD5 AES Triple-DES PSNR And SSIM

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Transform Domain and Singular Value Decomposition for Robust and Imperceptible image Watermarking 2

Transform Domain and Singular Value Decomposition for Robust...

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2nd International conference on Intelligent Cybernetics Technology and Applications (ICICyTA) - Cybernetic Technology for Better Future of Human Life

作者： Aufa, Muhammad Fikri Novamizanti, Ledya Purnamasari, Rita Telkom Univ Sch Elect Engn Bandung Indonesia Telkom Univ Human Centr HUMIC Engn Sch Elect Engn Bandung Indonesia

ISBN: (纸本)9798350399134

As technology advances at an ever-increasing rate, data is disseminated online through multimedia, particularly images, which are relatively simple to access. However, this situation is prone to theft of digital data and copyright infraction. A method to preserve and maintain multimedia data information, particularly images, is the robust watermarking approach. The implanted watermark can be recovered even after being altered by numerous techniques and will be difficult to erase. The host image is altered by redundant wavelets, and the suggested watermarking approach incorporates watermarks situated in the low and high-frequency sub-bands of the curvelet transform to provide a fair trade-off between robustness and imperceptibility. Applying a discrete cosine transform to the watermark causes it to be integrated into the image. Then, using the singular value decomposition approach, the watermark's unique value is added to the host image's unique value. The watermarked image is extracted on the receiving side in order to return the watermark to its original state. According to the study's findings, the scheme has a good imperceptibility and is resilient with a maximum peak signal to noise ratio value of 71.7630 dB, SSIM 1, NC 1, and BER 0. The suggested approach is resistant to attacks from Gaussian filter, rotation, and JPEG compression with noise that is (Gaussian, salt-and-pepper, speckle).

关键词： imperceptible robust SVD transform domain watermarking

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Automated Kidney Segmentation and Disease Classification Using CNN-Based Models 2nd

Automated Kidney Segmentation and Disease Classification Usi...

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2nd Pan-African conference on Artificial Intelligence (PanAfriCon AI)

作者： Abraham, Akalu Tuse, Misganu Meshesha, Million Addis Ababa Sci & Technol Univ Addis Ababa Ethiopia Addis Ababa Univ Sch Informat Sci Addis Ababa Ethiopia

ISBN: (纸本)9783031576232;9783031576249

According to studies, kidney disease (KD) is predicted to be the 5th leading cause of death by 2040. Some works have been done on KD segmentation and classification using CNN-based models. However, works that consider automated KD segmentation and classification using CNN-based models for more than one KD type, together with model optimization are rarely investigated. In this study, four KD conditions (tumor, cyst, stone, and normal) are considered to develop automated kidney segmentation and disease classification using CNN-based models. We utilized a balanced and limited number of images from a publicly available CT scan image dataset containing 12,446 images categorized into four classes (tumor, cyst, stone, and normal). The overall model development procedure has four major stages. The first stage, the data preprocessing stage, involves target mask preparation and data augmentation. The second stage involves training a U-Net segmentation model using the augmented dataset. In the third stage, CNN-based models, VGG-16, ResNet-50, and Inception-V3, were trained on segmented images generated using a segmentation model to classify KD. In this stage, batch normalization, dropout, regularization, and dense layers are added to optimize the models. Finally, the model is designed to provide possible expert-level treatment recommendations. The experiment results confirmed that using the threshold technique improved the performance of the U-Net model with an accuracy of 98%, and a Dice coefficient of 0.9. All the models were trained with the same amount of dataset and fixed hyperparameters;however, ResNet-50 outperformed with a testing accuracy of 97%.

关键词： UNet segmentation image classification Automated disease diagnosis CT scan images

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A Novel Methodology for the Optimization of Photogrammetry data of Physical Objects for Use in Metaverse Virtual Environments 2

A Novel Methodology for the Optimization of Photogrammetry D...

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2nd Edition IEEE International conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering, MetroXRAINE 2023

作者： Gledhill, Duke Novak, Matthew Computing and Engineering University of Huddersfield Huddersfield United Kingdom

ISBN: (纸本)9798350300802

In this paper, we introduce a novel methodology for the optimization of photogrammetry data of physical objects for use in metaverse virtual environments. Our approach focuses on an artist-driven process to optimize mesh and textures derived from photogrammetry, ensuring authentic and accurate recreation of real-world objects for low-powered VR devices. By leveraging this methodology, we aim to enhance the user experience in virtual environments by providing realistic and immersive representations of physical objects while maintaining optimal performance on a wide range of devices. This paper details the development and implementation of our methodology, as well as its potential applications and benefits in the rapidly evolving metaverse landscape. © 2023 IEEE.

关键词： image processing optimization technique photogrammetry virtual reality

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Realization Technology of Texture Feature Extraction Algorithm of Remote Sensing Satellite image Based on FPGA 2

Realization Technology of Texture Feature Extraction Algorit...

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2nd IEEE International conference on data Science and Computer Application, ICDSCA 2022

作者： Feng, Kai Zhang, Rui Shaanxi Academy of Aerospace Technology Application Co. Ltd. Xi'an China

ISBN: (数字)9781665472005

ISBN: (纸本)9781665472005

In recent years, the research on remote sensing image information output has developed rapidly, including not only remote sensing technology, image processing, pattern recognition, etc., but also land use, environmental monitoring, disaster forecasting, urban planning and other fields. The purpose of this paper is to study the realization technology of remote sensing satellite image texture feature extraction algorithm based on FPGA. A hardware system implementation scheme based on FPGA is proposed. Firstly, the overall structure of the system is established, and the software and hardware platforms are selected. Then, according to the difficulty of the algorithm, it is proposed to divide the algorithm into two parts, and the system based on hardware logic and based on soft core processor is described in detail. Implementation plan. An object modeling method based on Gabor texture blocks is introduced. This method is applied to the extraction of urban buildings from remote sensing images, and the final building extraction accuracy reaches 91.5%. © 2022 IEEE.

关键词： Field programmable gate arrays (FPGA)

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Machine Learning-Based Pattern Recognition Models for image Recognition and Classification 1

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2nd International conference on data Analytics and Learning, DAL 2022

作者： Madhuri, G.R. Jagadale, Basavaraj N Salma, N. Akshata, G.M. Gupta, Ajaykumar Chandrakantha, T.S. Department of PG Studies and Research in Electronics Kuvempu University Shankaragatta Karnataka Shimoga District India

ISBN: (数字)9789819963461

ISBN: (纸本)9789819963454

Recognizing patterns and identifying different objects by a machine using human intelligence is a challenging issue in real world. Machine learning techniques can help a machine to achieve automating manual tasks and advanced technologies such as self-driving car and robot in industry. The automated analysis of medical images, tax returns to audit, automatic inspection of printed circuits, etc. are some more applications of pattern recognition for which we are required to design a machine which classifies perfectly. This paper presents different machine learning-based pattern recognition approaches to recognize an object. The first step in building an automatic classification is separating the objects from its background. Based on the literature survey, this paper encompasses the various models of pattern recognition for image analysis and recognition with the aid of machine learning algorithms. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Machine learning

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A Conceptual Investigation on the image Processing using Artificial Intelligence and Tensor Flow Models through Correlation Analysis 2

A Conceptual Investigation on the Image Processing using Art...

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2nd International conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2022

作者： Ambore, Bhagyashree Gupta, Ayan Das Rafi, Shaik Mohammad Yadav, Sandeep Joshi, Kapil Sivakumar, R.D. Rnsit Bangalore India Hooghly West Bengal Chandernagore Government College Affiliated to the University of Burdwan Chandernagore Government College Postgraduate Department of Geography India Sri Mittapalli College of Engineering Artificial Intelligence and Information Technology Andhrapradesh Guntur India Department of Physics Gurugram University Haryana Gurugram India Uttaranchal University Uit India Department of Computer Science Bell Institute Sivakasi India

ISBN: (纸本)9781665437899

Artificial intelligence plays a vital role in image processing and tensor flow models. Due to increasing demand of generating data from image as well as to make distinction between images the necessity and relevance of these two topics are also growing. image classification and processing is a recent trend where traffic lights and other objects are identified by Machine Learning (ML) and Deep Learning (DL) technologies. These technologies are further implemented inside self-driven vehicles for developing autonomous processing's and information transfer. However, an accuracy and speed of detection process is a concern in today's world. Thus, this study paper provides an insight into image processing accuracy by 'single-stage object detection model' or SSD MobileNetV2 and 'Regional-based Convolutional Neural Network' or RCNN. Pearson correlation analysis and mean was obtained using the input data. The output data were further analysed and discussed. Findings suggested that RCNN is 80.90% accurate when it is allowed to detect traffic lights present at 11.9 metres apart;whereas SSD MobileNet V2 has shown 71.8% of accuracy when the traffic light was detected present at the same distance. On average, the RCNN shows higher accuracy than SSD MobileNet V2. The discussion found that RCNN is more accurate and slower than SSD MobileNetV2. © 2022 IEEE.

关键词： Deep learning

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12th International Workshop on Clinical image-Based Procedures, CLIP 2023, 1st MICCAI Workshop on Fairness of AI in Medical Imaging, FAIMI 2023, held in conjunction with MICCAI 2023 and 2nd MICCAI Workshop on the Ethical and Philosophical Issues in Medical Imaging, EPIMI 2023

12th International Workshop on Clinical Image-Based Procedur...

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12th International Workshop on Clinical image-Based Procedures, CLIP 2023, 1st MICCAI Workshop on Fairness of AI in Medical Imaging, FAIMI 2023, held in conjunction with MICCAI 2023 and 2nd MICCAI Workshop on the Ethical and Philosophical Issues in Medical Imaging, EPIMI 2023

ISBN: (纸本)9783031452482

The proceedings contain 29 papers. The special focus in this conference is on Clinical image-Based Procedures, Fairness of AI in Medical Imaging and Ethical and Philosophical Issues in Medical Imaging. The topics include: Unveiling Fairness Biases in Deep Learning-Based Brain MRI reconstruction;Brain Matters: Exploring Bias in AI for Neuroimaging Research;Bias in Unsupervised Anomaly Detection in Brain MRI;towards Unraveling Calibration Biases in Medical image Analysis;are Sex-Based Physiological Differences the Cause of Gender Bias for Chest X-Ray Diagnosis?;bayesian Uncertainty-Weighted Loss for Improved Generalisability on Polyp Segmentation Task;Mitigating Bias in MRI-Based Alzheimer’s Disease Classifiers Through Pruning of Deep Neural Networks;Auditing Unfair Biases in CNN-Based Diagnosis of Alzheimer’s Disease;distributionally Robust Optimization and Invariant Representation Learning for Addressing Subgroup Underrepresentation: Mechanisms and Limitations;analysing Race and Sex Bias in Brain Age Prediction;automatic Neurocranial Landmarks Detection from Visible Facial Landmarks Leveraging 3D Head Priors;Studying the Effects of Sex-Related Differences on Brain Age Prediction Using Brain MR Imaging;An Investigation into the Impact of Deep Learning Model Choice on Sex and Race Bias in Cardiac MR Segmentation;An Investigation into Race Bias in Random Forest Models Based on Breast DCE-MRI Derived Radiomics Features;How You Split Matters: data Leakage and Subject Characteristics Studies in Longitudinal Brain MRI Analysis;revisiting Skin Tone Fairness in Dermatological Lesion Classification;a Study of Age and Sex Bias in Multiple Instance Learning Based Classification of Acute Myeloid Leukemia Subtypes;unsupervised Bias Discovery in Medical image Segmentation;debiasing Counterfactuals in the Presence of Spurious Correlations;on the Relationship Between Open Science in Artificial Intelligence for Medical Imaging and Global Health Equity;gradient-Based Enhancement Attac

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Review of Machine Learning and Deep Learning algorithms for Personality traits classification 2

Review of Machine Learning and Deep Learning algorithms for ...

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2nd DMIHER International conference on Artificial Intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Bhadane, Satish N. Verma, Prateek Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Sawangi Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Sawangi Maharashtra Wardha442001 India

ISBN: (纸本)9798331528713

Nowadays, people deal with major psychological issues on a daily basis, often in the absence of a desired support system. In the field of psychology, focus is on the thoughts, feelings, behaviors, goals, and interests of individuals. The psychologist conventionally considers various types of personality traits, like the Big 5 personality traits, the Myers-Briggs Type Indicator (MBTI), the OCEAN Model (Openness, Conscientiousness, Agreeableness, Extraversion, Neuroticism), and other methods also considered for measuring personality. To forecast personality traits like extraversion, agreeableness, openness, conscientiousness, and neuroticism, consider the name "Big Five personality traits"It is a universal model to forecast these personality traits. Studies on personality prediction that identify personality disorders from user content on social media sites like Facebook, Twitter, and WhatsApp primarily rely on user behavior on these platforms. A number of popular Deep Learning (DL) techniques have advanced recently, including Deep Belief Networks (DBNs), Convolution Neural Networks (CNNs), and Recurrent Neural Networks (RNNs). Examine these methods in terms of single and multiple modalities, including psychological, audio, video, text, and image information. Researchers currently focus on predicting personality traits using single-modality text features, static facial images, audio, and video clips, including interview recordings and YouTube videos. Therefore, the current study highlights that personality prediction mostly relies on written text, audio and video datasets used in Machine Learning (ML) and DL models. Additional investigation is needed to resolve ethical concerns and justify the use of psychological datasets and DL models to evaluate personality traits. By combining personality traits with a psychological dataset, this study imagines a time when people everyday struggles can be effectively addressed. The integration of ML and DL in the development of pe

关键词： Adversarial machine learning

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CropOptimizer: Intelligent Crop Forecasting and Decision Support System 2

CropOptimizer: Intelligent Crop Forecasting and Decision Sup...

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2nd IEEE Global conference on Communications and Information Technologies, GCCIT 2024

作者： Jadhav, Balasaheb Ligde, Vaidehi Malpani, Rushikesh Lakhotiya, Rushikesh Savale, Phalguni Vishwakarma Institute of Technology Department of Artificial Intelligence and Data Science Pune India

ISBN: (纸本)9798350388916

CropOptimizer is an innovative crop forecasting and decision support system designed to revolutionize agricultural practices. Comprising five key modules - crop recommendation, fertilizer suggestion, disease detection, weather forecasting, and market price tracking - CropOptimizer combines modern technologies to help farmers make informed decisions. The crop recommendation module, leveraging XGBoost, uses soil, climate, and nutrient data to select suitable crops that maximize production potential. The fertilizer suggestion module provides specialized guidance based on specific crop and soil characteristics, ensuring effective nutrient management through XGBoost. The disease detection module employs deep learning with ResNet-9 for image processing to identify plant diseases from leaf images, followed by specific treatment recommendations, achieving high accuracy. Weather forecasting offers a 7-day forecast, including rainfall predictions, based on plant type, state, and city through an API. Market price tracking provides live market prices for various crops, enabling farmers to make informed selling decisions. By integrating these components, CropOptimizer increases crop yield and sustainability, fostering a more resilient agricultural ecosystem and improving food security and economic success. © 2024 IEEE.

关键词： Crop rotation

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