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Text of the page (random words):
etection methods 16 fingerprinting edit fingerprinting is currently the most widely applied approach to content similarity detection this method forms representative digests of documents by selecting a set of multiple substrings n grams from them the sets represent the fingerprints and their elements are called minutiae 17 18 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 minutiae matching with those of other documents indicate shared text segments and suggest potential plagiarism if they exceed a chosen similarity threshold 19 computational resources and time are limiting factors to fingerprinting which is why this method typically only compares a subset of minutiae to speed up the computation and allow for checks in very large collection such as the internet 17 string matching edit string matching is a prevalent approach used in computer science when applied to the problem of plagiarism detection documents are compared for verbatim text overlaps numerous methods have been proposed to tackle this task of which some have been adapted to external plagiarism detection 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 generally suffix document models such as suffix trees or suffix vectors have been used for this task nonetheless substring matching remains computationally expensive which makes it a non viable solution for checking large collections of documents 20 21 22 bag of words edit bag of words analysis represents the adoption of vector space retrieval a traditional ir concept to the domain of content similarity detection documents are represented as one or multiple vectors e g for different document parts which are used for pair wise similarity computations similarity computation may then rely on the traditional cosine similarity measure or on more sophisticated similarity measures 23 24 25 citation analysis edit citation based plagiarism detection cbpd 26 relies on citation analysis and is the only approach to plagiarism detection that does not rely on the textual similarity 27 cbpd examines the citation and reference information in texts to identify similar patterns in the citation sequences as such this approach is suitable for scientific texts or other academic documents that contain citations citation analysis to detect plagiarism is a relatively young concept it has not been adopted by commercial software but a first prototype of a citation based plagiarism detection system exists 28 similar order and proximity of citations in the examined documents are the main criteria used to compute citation pattern similarities citation patterns represent subsequences non exclusively containing citations shared by the documents compared 27 29 factors including the absolute number or relative fraction of shared citations in the pattern as well as the probability that citations co occur in a document are also considered to quantify the patterns degree of similarity 27 29 30 31 stylometry edit stylometry subsumes statistical methods for quantifying an author s unique writing style 32 33 and is mainly used for authorship attribution or intrinsic plagiarism detection 34 detecting plagiarism by authorship attribution requires checking whether the writing style of the suspicious document which is written supposedly by a certain author matches with that of a corpus of documents written by the same author intrinsic plagiarism detection on the other hand uncovers plagiarism based on internal evidences in the suspicious document without comparing it with other documents this is performed by constructing and comparing stylometric models for different text segments of the suspicious document and passages that are stylistically different from others are marked as potentially plagiarized infringed 8 although they are simple to extract character n grams are proven to be among the best stylometric features for intrinsic plagiarism detection 35 neural networks edit more recent approaches to assess content similarity using neural networks have achieved significantly greater accuracy but come at great computational cost 36 traditional neural network approaches embed both pieces of content into semantic vector embeddings to calculate their similarity which is often their cosine similarity more advanced methods perform end to end prediction of similarity or classifications using the transformer architecture 37 38 paraphrase detection particularly benefits from highly parameterized pre trained models performance edit comparative evaluations of content similarity detection systems 6 39 40 41 42 43 indicate that their performance depends on the type of plagiarism present see figure except for citation pattern analysis all detection approaches rely on textual similarity it is therefore symptomatic that detection accuracy decreases the more plagiarism cases are obfuscated detection performance of computer assisted plagiarism detection approaches depending on the type of plagiarism being present literal copies a k a copy and paste plagiarism or blatant copyright infringement or modestly disguised plagiarism cases can be detected with high accuracy by current external pds if the source is accessible to the software in particular substring matching procedures achieve good performance for copy and paste plagiarism since they commonly use lossless document models such as suffix trees the performance of systems using fingerprinting or bag of words analysis in detecting copies depends on the information loss incurred by the document model used by applying flexible chunking and selection strategies they are better capable of detecting moderate forms of disguised plagiarism when compared to substring matching procedures intrinsic plagiarism detection using stylometry can overcome the boundaries of textual similarity to some extent by comparing linguistic similarity given that the stylistic differences between plagiarized and original segments are significant and can be identified reliably stylometry can help in identifying disguised and paraphrased plagiarism stylometric comparisons are likely to fail in cases where segments are strongly paraphrased to the point where they more closely resemble the personal writing style of the plagiarist or if a text was compiled by multiple authors the results of the international competitions on plagiarism detection held in 2009 2010 and 2011 6 42 43 as well as experiments performed by stein 34 indicate that stylometric analysis seems to work reliably only for document lengths of several thousand or tens of thousands of words which limits the applicability of the method to computer assisted plagiarism detection settings an increasing amount of research is performed on methods and systems capable of detecting translated plagiarism currently cross language plagiarism detection clpd is not viewed as a mature technology 44 and respective systems have not been able to achieve satisfying detection results in practice 41 citation based plagiarism detection using citation pattern analysis is capable of identifying stronger paraphrases and translations with higher success rates when compared to other detection approaches because it is independent of textual characteristics 27 30 however since citation pattern analysis depends on the availability of sufficient citation information it is limited to academic texts it remains inferior to text based approaches in detecting shorter plagiarized passages which are typical for cases of copy and paste or shake and paste plagiarism the latter refers to mixing slightly altered fragments from different sources 45 software edit the design of content similarity detection software for use with text documents is characterized by a number of factors 46 factor description and alternatives scope of search in the public internet using search engines institutional databases local system specific database citation needed analysis time delay between the time a document is submitted and the time when results are made available citation needed document capacity batch processing number of documents the system can process per unit of time citation needed check intensity how often and for which types of document fragments paragraphs sentences fixed length word sequences does the system query external resources such as search engines comparison algorithm type the algorithms that define the way the system uses to compare documents against each other citation needed precision and recall number of documents correctly flagged as plagiarized compared to the total number of flagged documents and to the total number of documents that were actually plagiarized high precision means that few false positives were found and high recall means that few false negatives were left undetected citation needed most large scale plagiarism detection systems use large internal databases in addition to other resources that grow with each additional document submitted for analysis however this feature is considered by some as a violation of student copyright citation needed in source code edit plagiarism in computer source code is also frequent and requires different tools than those used for text comparisons in document significant research has been dedicated to academic source code plagiarism 47 a distinctive aspect of source code plagiarism is that there are no essay mills such as can be found in traditional plagiarism since most programming assignments expect students to write programs with very specific requirements it is very difficult to find existing programs that already meet them since integrating external code is often harder than writing it from scratch most plagiarizing students choose to do so from their peers according to roy and cordy 48 source code similarity detection algorithms can be classified as based on either strings look for exact textual matches of segments for instance five word runs fast but can be confused by renaming identifiers tokens as with strings but using a lexer to convert the program into tokens first this discards whitespace comments and identifier names making the system more robust against simple text replacements most academic plagiarism detection systems work at this level using different algorithms to measure the similarity between token sequences parse trees build and compare parse trees this allows higher level similarities to be detected for instance tree comparison can normalize conditional statements and detect equivalent constructs as similar to each other program dependency graphs pdgs a pdg captures the actual flow of control in a program and allows much higher level equivalences to be located at a greater expense in complexity and calculation time metrics metrics capture scores of code segments according to certain criteria for instance the number of loops and conditionals or the number of different variables used metrics are simple to calculate and can be compared quickly but can also lead to false positives two fragments with the same scores on a set of metrics may do entirely different things hybrid approaches for instance parse trees suffix trees can combine the detection capability of parse trees with the speed afforded by suffix trees a type of string matching data structure the previous classification was developed for code refactoring and not for academic plagiarism detection an important goal of refactoring is to avoid duplicate code referred to as code clones in the literature the above approaches are effective against different levels of similarity low level similarity refers to identical text while high level similarity can be due to similar specifications in an academic setting when all students are expected to code to the same specifications functionally equivalent code with high level similarity is entirely expected and only low level similarity is considered as proof of cheating difference between plagiarism and copyright plagiarism and copyright are essential concepts in academic and creative writing that writers researchers and students have to understand although they may sound similar they are not different strategies can be used to address each of them 49 algorithms edit this section is an excerpt from duplicate code detecting edit a number of algorithms have been proposed to detect duplicate code for example baker s algorithm 50 rabin karp string search algorithm using abstract syntax trees 51 visual clone detection 52 count matrix clone detection 53 54 locality sensitive hashing anti unification 55 complications with the use of text matching software for plagiarism detection edit various complications have been documented with the use of text matching software when used for plagiarism detection one of the more prevalent concerns documented centers on the issue of intellectual property rights the basic argument is that materials must be added to a database in order for the tms to effectively determine a match but adding users materials to such a database may infringe on their intellectual property rights 56 57 the issue has been raised in a number of court cases an additional complication with the use of tms is that the software finds only precise matches to other text it does not pick up poorly paraphrased work for example or the practice of plagiarizing by use of sufficient word substitutions to elude detection software which is known as rogeting it also cannot evaluate whether the paraphrase genuinely reflects an original understanding or is an attempt to bypass detection 58 another complication with tms is its tendency to flag much more content than necessary including legitimate citations and paraphrasing making it difficult to find real cases of plagiarism 57 this issue arises because tms algorithms mainly look at surface level text similarities without considering the context of the writing 58 59 educators have raised concerns that reliance on tms may shift focus away from teaching proper citation and writing skills and may create an oversimplified view of plagiarism that disregards the nuances of student writing 60 as a result scholars argue that these false positives can cause fear in students and discourage them from using their authentic voice 56 see also edit artificial intelligence detection software software to detect ai generated content pages displaying short descriptions of redirect targets category plagiarism detectors comparison of anti plagiarism software locality sensitive hashing algorithmic technique using hashing nearest neighbor search optimization problem in computer science paraphrase detection automatic generation or recognition of paraphrased text pages displaying short descriptions of redirect targets kolmogorov complexity compression used to e...