A Comprehensive Review Analysis of Image Similarity Detection Techniques in Deep Learning to Detect the Image Plagiarism

Abstract

The proliferation of images on the internet in the era of artificial intelligence has increased the risk of image plagiarism. Image plagiarism is the most serious problem in the field of research. Using artificial intelligence (AI) tools, writers and scholars now modify text, codes, and images. While these artificial intelligence systems have certain advantages, the writers gain more in the research domain. Machine learning includes deep learning, while artificial intelligence includes machine learning. The majority of Deep Learning's attention is directed towards images with several characteristics. Deep learning uses a variety of techniques to process images. Once image plagiarism has been found in a document with the help of plagiarism detection tools, it should be eliminated. In order to make the Internet of Things more environmentally friendly, plagiarism must also be found and eliminated. Finding plagiarism in the source code is also a difficult task. In the presented paper, various methods of similarity detection are discussed and compared in order to determine which method of image similarity detection is most effective in detecting plagiarism in images or in the text of images. If the similarity index between the images is lower, then there is a greater likelihood of plagiarism. Different types of distances are calculated in order to check the image similarity. These methods are also used to check the similarity index with their accuracy. Anyone is able to select the most accurate and user-friendly Deep Learning method for image similarity with ease after going over all of the available ways for detecting image plagiarism.

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