UNVEILING AUTHENTICITY

UNVEILING AUTHENTICITY

1S. Prudhivi Raj, 2Y. Sesha Sai Harshith, 3V.Raja Narsimha, 4V. Srinath , 5T. Santhoshi

1Associate Professor, 2,3,4,5Students of Dept CSE(AIML)

Sreyas Institute of Engineering and Technology

ABSTRACT

In an educational terrain, plagiarism is a pivotal task that needs to be linked, in recent times each known journals and conferences, as well as universities, request a plagiarism report from scholars and experimenters to prove the originality of published textbook or scientific paper. Plagiarism discovery generally checks the textbook content via numerous of the platforms which are available for productive use reliably relating copied textbook or near- clones of textbook and these systems generally fail to descry the images, and Files plagiarism since it's firstly erected for  textbook  substantially. We suggest an adaptive, scalable, and extensible, robust  system for image plagiarism which is tested in designs collect from department of armature University of Technology, this  system  substantially compare the data or designs images entered to the system with data sets saved in the database  substantially these designs are saved as  point which is one of the artificial intelligence algorithms and match by using k- mean clustering and the similarity check is done with threshold used 40 which can be changed to an accepted  situations when  demanded. Using the k- mean algorithm in clustering, which is a robust artificial intelligence clustering algorithm giving us a strong system that isn't discarding any point uprooted from the image. Our observation in this connection that the evaluation of plagiarism discovery algorithms isn't formalized, i.e., utmost of the time the algorithms are estimated on manual corpora using colorful different performance measures. This situation renders the being exploration nearly inimitable

Keywords: Plagiarism Detection, Text Comparision, Similarity Analysis, Tokenization