the primary objective of this project is to enhance the security of digital images transmitted over open networks by addressing privacy risks through cryptographic techniques. Recent imageencryption methods, such as ...
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ISBN:
(数字)9798331523923
ISBN:
(纸本)9798331523930
the primary objective of this project is to enhance the security of digital images transmitted over open networks by addressing privacy risks through cryptographic techniques. Recent imageencryption methods, such as chaotic-based algorithms, permutation techniques, and DNA coding, have shown promise but face significant challenges. these include limited key sensitivity, computational inefficiency, pattern retention in encrypted images, and susceptibility to cryptanalytic attacks like brute force and differential analysis. To address these issues, this project proposes a dual-layer encryption approach combining the Advanced encryption Standard (AES) algorithm and a randomized AES-compatible key generator. In the first step, the AES algorithm is applied to the plain image, creating a strong initial encryption layer. Next, an AES-compatible key generator produces a randomized key, which is used in an XOR operation to add an additional layer of security. this two-step process ensures that encrypted images exhibit highly randomized outputs, with histograms that differ significantly from the original images, thus eliminating statistical patterns and mitigating statistical attacks. the proposed encryption method successfully enhances security by producing highly randomized encryption results, withthe histogram of the encrypted image differing significantly from that of the original image. this dual-layered approach to encryption demonstrates robustness against conventional decryption attempts and strengthens the data integrity of the encrypted images.
the use of digital images is increasingly widespread currently. there is a need for security in digital photos. Cryptography is a technique that can be applied to secure data. In addition to safety, data integrity als...
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the proceedings contain 74 papers. the special focus in this conference is on Doctoral Symposium on Computational Intelligence. the topics include: Computational Models to Prove Security Protocols Against Adversary;Ad...
ISBN:
(纸本)9789819937158
the proceedings contain 74 papers. the special focus in this conference is on Doctoral Symposium on Computational Intelligence. the topics include: Computational Models to Prove Security Protocols Against Adversary;Advanced Blockchain-Based Scheme for Efficient and Secured Sharing of Customers Information in Logistics Management Model Using RSA encryption Method;CG-Transmission: A New Encrypted Transmission Method for the data Middle Platform;blockchain Technology-Based Framework for Anti-Counterfeiting and Traceability;blockchain-Based Mechanism for Secured Information Exchange in Digital Governance Systems;a Study on Identification of Human Using Palm Vein Recognition System;accident Hotspot Identification by Affinity Propagation and Compared With Different Clustering Algorithms;a Self Diagnosis Medical Chatbot Using Sklearn;a Comprehensive Review of the Works of Literature for the Prediction of Protein Structure—Perceptions on Traditional and Deep Learning Approaches;Simulation Secure MQTT Protocol Based on TLS in IoT-Fog Computing Environment;security Analysis on Android Application through Penetration Testing;convalesce Optimisation Using a Customizable Mutation Testing Tool;garment Defect Detection System Based on Histogram Using Deep Learning;identification and Spatiotemporal Observation of Public Transports for Semi-urban Regions;Relative Rendition Dissection of MIMOOFDM-IDMA and MIMO-OFDM Scheme for Acoustic Assertion;wavelet Tree compression in Legal Documents;a Machine Learning Approach to Predict Software Faults;fakeRealIndian dataset: A Benchmark Indian Context dataset;identification of Racial Propaganda in Tweets Using Sentimental Analysis Models: A Comparative Study;A Comparative Analysis of Multiclass Human Activity Recognition Using LSTM-Based Model;An Adaptive Whale Optimization Algorithm-Based CT image Denoising in Wavelet Domain;a Real-Time image Recognition Using TensorFlow Framework;blockchain-Enabled Ridesharing Platform.
image classification is an essential part of computer vision. A lot of approaches, in particular, based on applying convolutional neural networks have been proposed. In this paper, we investigate the impact of discret...
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ISBN:
(数字)9798331520564
ISBN:
(纸本)9798331520571
image classification is an essential part of computer vision. A lot of approaches, in particular, based on applying convolutional neural networks have been proposed. In this paper, we investigate the impact of discrete atomic compression (DAC) on state-of-the-art models. the DAC algorithm ensures low resource intensive imageencryption and compression features. Such a combination makes its usage promising, especially taking into account a huge number of digital images and current data protection requirements. DAC has lossy and lossless compression modes. the first one provides a higher compression ratio in combination with distortions. the aim of this research is to answer the following question: how does quality loss, which is introduced by DAC, affect the efficiency of the MobileNetV2, VGG16, VGG19, ResNet50, NASNetMobile and NASNetLarge models? It is shown that the difference between classifying images before and after lossy DAC-compression is insignificant.
the recent technological innovations and the emergence of web-based applications that employ multimedia data transfer limits the network bandwidth. this problem can adequately be solved withthe help of effective data...
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ISBN:
(数字)9798331529635
ISBN:
(纸本)9798331529642
the recent technological innovations and the emergence of web-based applications that employ multimedia data transfer limits the network bandwidth. this problem can adequately be solved withthe help of effective data compression algorithms. Video data is composed of stream of pictures known as frames, whose compression can be improved by elimination of useless or overlapping information from these frames. Transform-based methods are particularly looked for as they have good performance in image and video frame transmission. this research study the practical application of two transforms: Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT). the aim of both approaches is to increase the quality of the frames while achieving a high degree of compression which seems to be perfect for transmission of audio and video data.
Over the past years, deep learning techniques has had a strong impact on many areas of technical intelligence, including handwriting recognition. Handwriting recognition it is defined as the domain which allow a compu...
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the exponential growth in data creation and transmission, particularly high-resolution images, poses a significant challenge to network bandwidth, necessitating advanced compression techniques. this paper introduces C...
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ISBN:
(数字)9798350354218
ISBN:
(纸本)9798350354225
the exponential growth in data creation and transmission, particularly high-resolution images, poses a significant challenge to network bandwidth, necessitating advanced compression techniques. this paper introduces ConvAE-512, a novel convolutional neural network-based encoder-decoder architecture optimized for edge computing environments. Our approach significantly outperforms existing lossy compression algorithms in image fidelity while efficiently reducing data transmission size and network utilization. ConvAE-512 achieves a compression rate of 6x, outperforming traditional Direct compression with a higher Structural Similarity Index (SSIM) of 0.9150 and a Peak Signal-to-Noise Ratio (PSNR) of 27.15 dB, underscoring its efficiency in preserving image quality despite compression. these results establish ConvAE-512 as an advanced alternative for efficient image compression, adeptly balancing quality retention and compression ratio.
the significance of enhancing image compression efficiency for machine vision, analysis, and comprehension tasks has gained increasing recognition. In response to this need, we propose and implement a novel method cal...
the significance of enhancing image compression efficiency for machine vision, analysis, and comprehension tasks has gained increasing recognition. In response to this need, we propose and implement a novel method called ELIC (Efficient Learned image compression with Unevenly Grouped Space-Channel Contextual Adaptive coding) to achieve high compression efficiency. Our method is evaluated on the classic Openimage V6 Common Test Condition (CTC) eval datasets, and its performance is compared to baseline methods for machine vision tasks. the results of our study demonstrate a substantial enhancement in compression efficiency, suggesting that the ELIC technique holds promise for pushing the boundaries of state-of-the-art visual compression for vision tasks. Furthermore, we believe that our approach can promote the application of learning-based image compression.
image compression is a critical component of digital systems, as it reduces the file size of images while preserving quality, thereby facilitating minimal storage requirements and seamless transmission. Wavelet transf...
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ISBN:
(数字)9798331528171
ISBN:
(纸本)9798331528188
image compression is a critical component of digital systems, as it reduces the file size of images while preserving quality, thereby facilitating minimal storage requirements and seamless transmission. Wavelet transforms have emerged as powerful tools in image compression for providing both time and frequency information, making them particularly effective in capturing essential image features. Despite numerous advancements, there is still significant demand for image compression techniques that achieve higher compression ratios with minimal loss of quality. this paper presents a comprehensive analysis of various lossy image compression techniques, focusing on the strengths of wavelet-based methods. Techniques such as Discrete Wavelet Transform, Lifting Wavelet Transform, and Integer Wavelet Transform are examined in detail, with key metrics including Peak Signal-to-Noise Ratio (PSNR), compression ratio, and time efficiency analyzed to provide a thorough understanding of each technique's effectiveness. the timing analysis is particularly crucial for assessing the practical applicability of these methods in real-world scenarios. this study aims to offer valuable insights for developing more advanced image compression methods that meet the growing demands of modern data management systems.
Cloud desktop has the advantages of security, easy management, low comprehensive cost, and can meet the needs of mobile office. Cloud desktop based on B/S architecture also has a lot of performance optimization space,...
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ISBN:
(数字)9798331534622
ISBN:
(纸本)9798331534639
Cloud desktop has the advantages of security, easy management, low comprehensive cost, and can meet the needs of mobile office. Cloud desktop based on B/S architecture also has a lot of performance optimization space, in the low-bandwidth network video playback will be stuck, bandwidth usage is too high, response time is too long and other problems. therefore, this paper proposes a cloud desktop video transmission optimization scheme based on X2Go KDrive protocol, and designs an image block caching algorithm and a hybrid coding algorithm based on video detection. First, the client and the server maintain synchronous image block cache, and then the server detects the video area of the cloud desktop, and adopts H.264 video coding for the image block of the video area and JPEG imagecoding for the common area. Finally, the mark value of the unchanged image block in the block cache and the compressed data of the changed image block are assembled together to form the final transmission data and sent to the client. After receiving such data, the client can distinguish the unchanged image block and the changed image block, process them respectively, and restore the original image to the screen. the experimental results show that this optimization scheme effectively reduces the network bandwidth occupation during video playback, improves the fluency of video playback, and optimizes the performance of cloud desktop video transmission.
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