This document discusses plagiarism and algorithms for detecting it. It defines plagiarism as presenting another's work as one's own. To avoid plagiarism, it recommends using one's own words and ideas and properly citing sources. The document then describes string matching algorithms like Naive, KMP, Boyer-Moore, and Rabin Karp that can be used to compute the similarity between files and detect plagiarism. It concludes by stating that while changing societal attitudes is most important, computer tools can help identify plagiarized documents.

1. Prepared by:
Ayush GOYAL
KUMAR VIKRAMADITYa
shivaM SHARMA
TAUQEER KHAN

2. INTRODUCTION
 The representation of another’s work as one’s
own.
 It is serious problem for academics now a days.
 In computing courses, students often plagiarize
various assignments, source code.
 Usually they may copy it from their friends or
web.
 Manual comparison is rather impractical or
difficult too.

3. What is Plagiarism?
 “Copying the language, structure, ideas
and/or thoughts of another and adopting the
same as one’s own original work.”
 Taking over the ideas, methods, or written
words of another, without acknowledgement
and with the intention that they be taken as
the work of the deceiver.

4. Why is plagiarism strongly
discouraged?
 Plagiarism is dishonest because one uses another’s
work as one’s own.
 If one plagiarize, they are cheating themselves.
Plagiarism violates intellectual property rights,
leading to damages or fines.

5. How to avoid plagiarism
 Use your own words and ideas.
 Give proper credit for copied, adapted or
paraphrased materials.
 Avoid using others work with minor cosmetic
changes. Examples: using synonyms such as
therefore and thus, reversing sentence order or
changing format or layout of a text.
 If you use another’s exact words, you must use
quotation marks (“..”) or (‘..’).

6. how IT WORKS?
 In the program we have used Several String
matching algorithms like Naïve, KMP, Boyre’s
Moore, Rabin Karp. These Algorithms attempts
to compute the degree of similarity between the
selected file.
 This involves in each case , Removing comments
and string-constants. Translating upper case
letters into lower case.
 The output gives us the detailed Analysis of the
percentage copied between various input files.

7.  LCS (LONGEST COMMON SUBSEQUENCE)

8.  KMP (KNUTH-MORRIS-PRATT)

9.  NAIVE

10.  RABIN KARP

11. Conclusion
In the age of information technologies plagiarism has
become more actual and turned into a serious problem. In
this ways how to reduce plagiarism are discussed.
Plagiarism prevention methods which are based on society’s
change of attitude against plagiarism without any doubt are
the most significant means to fight against plagiarism, but
implementation of these methods is a challenge for society
as a whole. At the present such abilities are not available for
plagiarism detection software tools. But nevertheless
computer based plagiarism detection tools can considerably
help to find plagiarized documents.

12. Thanks for
Watching