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Plag detection
Plag detection
Plag detection
Plag detection
Plag detection
Plag detection
Plag detection
Plag detection
Plag detection
Plag detection
Plag detection
Plag detection
Plag detection
Plag detection
Plag detection
Plag detection
Plag detection
Plag detection
Plag detection
Plag detection
Plag detection
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Plag detection

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  • 1. KIET SCHOOL OF ENGINEERING & TECHNOLOGY DEPARTMENT OF COMPUTER APPICATIONS PRESENTATION ON Plagiarism-Detection-Technique SUBMITTED BY ROLL NO. :- 1102914084 NAME :- Rishabh Dixit SEMESTER :- 6th sem SECTION :- B 1
  • 2.  Introduction  Describing Plagiarism  Plagiarism Detection  Software Aided Detection  Approaches  Disadvantage of the Plagiarism  Conclusion  References 2
  • 3.  Plagiarism is a significant problem on almost every college and university campus. The problems of plagiarism go beyond the campus, and have become an issue in industry, journalism, and government activities.  Plagiarism refers to “the act of copying materials without actually acknowledging the original source”. 3
  • 4. Plagiarism can be described as:  Turning in someone else's work as your own.  Copying words or ideas from someone else without giving credit.  Giving incorrect information about the source of a quotation.  Changing words but copying the sentence structure of a source without giving credit. 4
  • 5. 5
  • 6.  collection stage may be defined as the process of electronically collecting and pre-processing student submissions into a suitable format.  Analysis is defined as “where the submissions are compared with each other and with Documents obtained from the Web and the list of those submissions, or pairs, the list of those submissions, or pairs, that require further investigation is produced.” 6
  • 7.  Verification (confirmation) is required to ensure that those pairs reported as being suspicious are worth investigating with a view to possible disciplinary action (this is a task normally undertaken by humans, since value judgements May be involved).  The final stage, investigation, will determine the extent of the alleged misconduct and will “also involve the process of deciding culpability and possible penalties.” 7
  • 8. 8
  • 9.  Computer-assisted plagiarism detection (CaPD) is an Information retrieval (IR) task supported by specialized IR systems, referred to as plagiarism detection systems (PDS). 9
  • 10. 10
  • 11. Fingerprinting:-  Fingerprinting is currently the most widely applied approach to plagiarism detection.  The sets represent the fingerprints and their elements are called minutiae.  A suspicious document is checked for plagiarism by computing its fingerprint and querying minutiae with a precomputed index of fingerprints for all documents of a reference collection. 11
  • 12. String matching :-  String matching is a prevalent approach used in computer science.  Checking a suspicious document in this setting requires the computation and storage of efficiently comparable representations for all documents in the reference collection to compare them pairwise. 12
  • 13. Bag of words:-  Bag of words analysis represent the adoption of vector space retrieval, a traditional IR concept, to the domain of plagiarism detection.  Documents are represented as one or multiple vectors, e.g. for different document parts, which are used for pair wise similarity computations. 13
  • 14. TurnItIn:  Four UC Berkeley graduate students designed a peer review application to use for their classes — thus, TurnItIn was born. Eventually, that prototype developed into one of the most recognizable names in plagiarism detection.  Students can use TurnItIn's WriteCheck service to maintain proper citations and to access various writing tools. Teachers can ask students to submit their papers through the service as a first measure. 14
  • 15. Viper:  Viper calls itself the "Free TurnItIn Alternative." It scans a large database of academic essays and other online sources, offering side-by-side comparisons for plagiarism. 15
  • 16. Plagiarism Detection systems are built based on a few languages. To check for plagiarism with the Same software can be difficult.  Most of the detection software checking is done with some repository situated in an organization .Other people are unable to access it and verify for plagiarism.  As the number of digital copies are going up the repository size should be large and the plagiarism Detection software should be able to handle it. 16
  • 17.  There is some plagiarism detection software available which ask us to load a file to their link. Once done the file is copied to their database and then checked for plagiarism. This also comes with an inherent chance of our data being leaked or hacked for other purposes. 17
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  • 19.  Plagiarism is rampant now. With most of the data available to us in digital format the venues for plagiarism is opening up.  To avoid this kind of cheating and to acknowledge the originality of the author new detection techniques are to be created.  To protect the intellectual property source code new techniques are to be developed and implemented. 19
  • 20. [1] Steven Burrows 1, Seyed M. M. Tahaghoghi 1 & Justin Zobel , Proceedings of the Second Australian Undergraduate Students' Computing Conference, 2004 http://citeseerx.ist.psu.edu/viewdoc/download?doi= 10.1.1.69.4500&rep=rep1&type=pdf [2] Georgina Cosma , Mike Joy, Daniel White and Jane Yau, 9th August 2007 ,ICS University of Ulster. http://www.ics.heacademy.ac.uk/resources/assessment/p lagiarism/ [3] Plagiarism detection software, How effective it is?, 20
  • 21. CSHE, http://www.cshe.unimelb.edu.au/assessinglearning/docs/ PlagSoftware.pdf [4] Maeve Paris, School of Computing & Intelligent Systems, University of Ulster http://www.ics.heacademy.ac.uk/Events/conf2003/ maeve.htm [5] S.A.F.E, http://www.safe-corp.biz/products_codesuite.htm 21

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