• SHAIK GOUSE MOHAMMED AHMED ALISHA
• BALA MURALI KRISHNA CHALAPA
• PANDURI SURYA TEJA
• SRINIVAS VAJJIPARTHI
• CHANDRA SEKHAR K

The problem of plagiarism has become increasingly prevalent in academia and other fields, with students and researchers alike often tempted to copy and paste text and images from various sources. In this paper, we propose a method for detecting both text and image plagiarism, using the least common subsequence and five modulus method algorithms. We implement this method in a system that can compare two texts or two images, or a text and an image, and determine the degree of similarity between them. We evaluate our method using a dataset of text and image samples, and compare it with other state-of-the-art plagiarism detection techniques. The results show that our method achieves high accuracy and outperforms other methods in terms of precision, recall, and F1-score.

Plagiarism detection, least common subsequence, five modulus method, text similarity, image similarity.

"TEXT AND IMAGE PLAGIARISM DETECTION USING LCS AND FMM ALGORITHMS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 8, page no.b210-b212, August-2023, Available :http://www.jetir.org/papers/JETIR2308125.pdf

"TEXT AND IMAGE PLAGIARISM DETECTION USING LCS AND FMM ALGORITHMS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 8, page no. ppb210-b212, August-2023, Available at : http://www.jetir.org/papers/JETIR2308125.pdf