The paper discusses about the problem of Authorship analysis, different types of authorship analysis’s such as authorship attribution, authorship identification, authorship profiling, plagiarism detection. It also addresses the issues in Indian language text. Keywords— Authorship attribution, authorship profiling, plagiarism detection, text classification.

Paraphrase plagiarism is one of the difficult challenges facing plagiarism detection systems. Paraphrasing occur when texts are lexically or syntactically altered to look different, but retain their original meaning. Most plagiarism detection systems (many of which are commercial based) are designed to detect word co-occurrences and light modifications, but are unable to detect severe semantic ...

Current research in the field of automatic plagiarism detection for text documents focuses on algorithms that compare plagiarized documents against potential original documents. Though these approaches perform well in identifying copied or even modified passages, they assume a closed world: a reference collection must be given against which a plagiarized document can be compared. This raises th...

There exist many plagiarism detection tools to uncover plagiarized codes by analyzing the similarity of source codes. To measure how reliable those plagiarism detection tools are, we developed a tool named Code ObfuscAtion Tool (COAT) that takes a program source code as input and produces another source code that is exactly equivalent to the input source code in their functional behaviors but w...

The article gives an overview of the plagiarism domain, with focus on academic plagiarism. defines plagiarism, explains origin term, as well related terms. It identifies extent domain and then focuses subdomain text documents, for which it current classifications taxonomies proposes a more comprehensive classification according to several criteria: their purpose, technical implementation, conse...

This paper presents a new approach for Persian plagiarism detection. This approach uses a graph structure as well as one of the graph similarity methods (iterative methods) for similarity detection of two Persian documents. In this approach, documents are represented by a graph with specified length, then each part of suspicious document is compared to that of the source document. The graph is ...

As plagiarisers become cleverer, plagiarism detection becomes harder. Plagiarisers will find new ways to obfuscate the plagiarized passages so that humans and automatic plagiarism detectors are not able to point them out. So, a plagiarism detection system needs to be robust enough to detect plagiarism, no matter what obfuscation techniques have been applied. Our system attempts to do the same b...

Plagiarism, the unacknowledged reuse of text, does not end at language boundaries. Cross-language plagiarism occurs if a text is translated from a fragment written in a different language and no proper citation is provided. Regardless of the change of language, the contents and, in particular, the ideas remain the same. Whereas different methods for the detection of monolingual plagiarism have ...

This paper explains how collaborative efforts in terms of technology and content, can help improve plagiarism detection and prevention. It presents a web service oriented architecture, which utilizes the collective strength of various search engines, context matching algorithms and indexing contributed by users. The proposed framework is an open source tool, yet it is extremely efficient and ef...

Today, Plagiarism has become a menace. Every journal editor or conference organizers has to deal with this problem. Simply Copying or rephrasing of text without giving due credit to the original author has become more common. This is considered to be an Intellectual Property Theft. We are developing a Plagiarism Detection Tool which would deal with this problem. In this paper we discuss the com...