Plagiarism detection

Plagiarism detection is the process of locating instances of plagiarism within a work or document. The widespread use of computers and the advent of the Internet has made it easier to plagiarize the work of others. Most cases of plagiarism are found in academia, where documents are typically essays or reports. However, plagiarism can be found in virtually any field, including novels, scientific papers, art designs, and source code.

Detection of plagiarism can be either manual or software-assisted. Manual detection requires substantial effort and excellent memory, and is impractical in cases where too many documents must be compared, or original documents are not available for comparison. Software-assisted detection allows vast collections of documents to be compared to each other, making successful detection much more likely.

The practice of plagiarizing by use of sufficient word substitutions to elude detection software is known as rogeting.

Computer-assisted plagiarism detection (CaPD) is an Information retrieval (IR) task supported by specialized IR systems, referred to as plagiarism detection systems (PDS).

Systems for text-plagiarism detection implement one of two generic detection approaches, one being external, the other being intrinsic. External detection systems compare a suspicious document with a reference collection, which is a set of documents assumed to be genuine. Based on a chosen document model and predefined similarity criteria, the detection task is to retrieve all documents that contain text that is similar to a degree above a chosen threshold to text in the suspicious document. Intrinsic PDS solely analyze the text to be evaluated without performing comparisons to external documents. This approach aims to recognize changes in the unique writing style of an author as an indicator for potential plagiarism. PDS are not capable of reliably identifying plagiarism without human judgment. Similarities are computed with the help of predefined document models and might represent false positives.

The figure below represents a classification of all detection approaches currently in use for computer-assisted plagiarism detection. The approaches are characterized by the type of similarity assessment they undertake: global or local. Global similarity assessment approaches use the characteristics taken from larger parts of the text or the document as a whole to compute similarity, while local methods only examine pre-selected text segments as input.

Fingerprinting is currently the most widely applied approach to plagiarism 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. 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. 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.

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