This presentation discusses techniques for detecting plagiarism. It begins by defining plagiarism and describing the issues it causes. It then outlines the main approaches used for detection, including fingerprinting, string matching, and bag-of-words analysis. Specific software tools for detection are also mentioned, like TurnItIn and Viper. The disadvantages of detection software and challenges in the field are then discussed, before concluding that new techniques need to be developed to address the growing issue of plagiarism.

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
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2.  Introduction
 Describing Plagiarism
 Plagiarism Detection
 Software Aided Detection
 Approaches
 Disadvantage of the Plagiarism
 Conclusion
 References
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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”.
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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.
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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.”
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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.”
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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).
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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18. 18

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.
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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?,
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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
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