Plagiarism detection is one of the most researched areas among the Natural Language Processing(NLP) community. A good plagiarism detection covers all the NLP methods including semantics, named entities, paraphrases etc. and produces detailed plagiarism reports. Detection of Cross Lingual Plagiarism requires deep knowledge of various advanced methods and algorithms to perform effective text similarity checking. Nowadays the plagiarists are also advancing themselves from hiding the identity from being catch in such offense. The plagiarists are bypassed from being detected with techniques like paraphrasing, synonym replacement, mismatching citations, translating one language to another. Image Content Plagiarism Detection (ICPD) has gained importance, utilizing advanced image content processing to identify instances of plagiarism to ensure the integrity of image content. The issue of plagiarism extends beyond textual content, as images such as figures, graphs, and tables also have the potential to be plagiarized. However, image content plagiarism detection remains an unaddressed challenge. Therefore, there is a critical need to develop methods and systems for detecting plagiarism in image content. In this paper, the system has been implemented to detect plagiarism form contents of Images such as Figures, Graphs, Tables etc. Along with statistical algorithms such as Jaccard and Cosine, introducing semantic algorithms such as LSA, BERT, WordNet outperformed in detecting efficient and accurate plagiarism.

翻译：剽窃检测是自然语言处理（NLP）领域研究最广泛的课题之一。一个完善的剽窃检测系统需涵盖包括语义分析、命名实体识别、复述检测等在内的所有NLP方法，并生成详细的剽窃检测报告。跨语言剽窃检测需要深入掌握多种先进方法与算法，才能实现高效的文本相似度比对。如今，剽窃者也在不断升级手段以规避检测，例如通过复述、同义词替换、错误引用、跨语言翻译等方式隐藏身份。图像内容剽窃检测（ICPD）的重要性日益凸显，它利用先进的图像内容处理技术识别剽窃行为，以保障图像内容的完整性。剽窃问题不仅限于文本，图表、图形、表格等图像内容同样可能被剽窃。然而，图像内容剽窃检测仍是一个尚未得到充分解决的挑战。因此，亟需开发用于检测图像内容剽窃的方法与系统。本文实现了一种可从图像（如图形、图表、表格等）内容中检测剽窃的系统。在采用Jaccard系数和余弦相似度等统计算法的同时，引入LSA、BERT、WordNet等语义算法，在高效精确的剽窃检测中展现出更优性能。