Security Protection Mechanism in Cloud Computing Authorization Model Using Machine Learning Techniques

Abstract

Because of the vast number of applications and the ambiguity in application methods, handwritten character recognition has garnered widespread recognition and increased prominence in the community of pattern recognition researchers ever since it was first developed. This is due to the fact that application methods can be quite ambiguous. Computer in the cloud, on the other hand, allows for suitable network access on demand to a shared pool of customizable computing resources and digital devices. According to those knowledgeable in the subject, the standard filtering techniques are not enough when it comes to the process of denoising images. In many different approaches to machine learning, information is lost not just during the filtering process itself but also at other points during the process. When a convolutional neural network is going through its pooling operation, the internal data representation either becomes misaligned or entirely vanishes (CNN). The reconstruction of low-intensity digital photographs, which takes place during repetitive filtering, breaks away the artefacts that remain after each filtering function, which results in an image that is more uniform. The multilayer wavelet transform, or MLWT, is a method for processing features that comprises of many filter bands and is used in cloud computing authorization that is protected securely. In this scenario, a significant quantity of information gets obliterated from digital photographs during the process of feature extraction and processing. These issues are investigated by the deep learning algorithms that make use of autoencoder, and the methods also handle the novel windowing blocks that are being introduced to the layers. In this section, the magnitude and phase information is considered in order to construct a deep learning framework that will provide good denoising of digital images. The proposed architecture is equipped with the capability of accurately identifying, in real time, the noise level and type that was employed in the training of the network. The method that we have proposed, which is centred on the distribution of noise, may be used to determine the kind of noise. In order to categorise the various types of noise, we investigated nine distinct noise distributions. Dilated convolutional filtering will be used as the method of choice in order to ascertain the specific nature of the noise that can be found in the digital images. An autoencoder-based deep learning algorithm is able to accomplish numerous experimental results in digital image denoising operations that are superior to those achieved by a typical deep learning algorithm even when the intensity of the scenario is low. The performance of the method that is produced by combining the autoencoder and the dilated convolutional filtering techniques is enhanced over the performance of the technology that is currently in use. Using the method that we have outlined in this study, it is possible to recreate the low-intensity images in their entirety. We were able to show that our proposed method beat other existing algorithms for low-density digital photos by comparing several metrics, such as the peak signal-to-noise ratio (PSNR), the structural similarity index, and others (SSIM).

Efficient and Privacy-Preserving Image Classification Using Homomorphic Encryption and Chunk-based Convolutional Neural Network

“…This indexing scheme helps track split chunks and their related information, providing a systematic method for managing the large amount of data generated during the image splitting process. It also aids subsequent steps in our method, such as encrypting feature-rich split chunks and inputting these split chunks into our improved ciphertext convolutional neural network classification model [30,31].…”

Efficient and privacy-preserving image classification using homomorphic encryption and chunk-based convolutional neural network

“…This indexing scheme helps track split chunks and their related information, providing a systematic method for managing the large amount of data generated during the image splitting process. It also aids subsequent steps in our method, such as encrypting feature-rich split chunks and inputting these split chunks into our improved ciphertext convolutional neural network classification model [39,40].…”

Securing the Information using Improved Modular Encryption Standard in Cloud Computing Environment

“…For instance, the Google App Engine is a cloud computing service that serves as a platform for hosting web applications. Examples of software as a service are Google app and Microsoft office online which offer both infrastructure and platform as a service for servers [4]. Cloud services are mainly categorized into Infrastructure as a Service (IaaS), Platform-based services (PaaS), and Software-based Services (SaaS).…”