detection procedure consisted of automatic scanning of manuscripts using plagiarism detection software (eTBLAST and CrossCheck) and manual verification of manuscripts suspected of having been plagiarized (more than 10% text similarity). They found that 11% of manuscripts were plagiarized, 8% were true plagiarism, and 3% were self-plagiarism. The Korean Journal of family medicine (KJFM) has also...

This report discusses in detail methods of plagiarism and its detection in both natural and programming languages. The increase of material now available in electronic form and improved access to this via the Internet is allowing, with greater ease than ever before, plagiarism that is either intentional or unintentional. Due to increased availability of On-line material, people checking for pla...

Plagiarism is a widespread problem in assessment tasks; in computing courses, students often plagiarise source code. For all but the smallest classes, manual detection of such plagiarism is impractical, and, while automated tools are available, none has been applied to detect inter-lingual plagiarism, where source code is copied from one language to another. In this work, we propose a novel app...

Plagiarism has always been a concern in many sectors, particularly in education. With the sharp rise in the number of electronic resources available online, an increasing number of plagiarism cases has been observed in recent years. As the amount of source materials is vast, the use of plagiarism detection tools has become the norm to aid the investigation of possible plagiarism cases. This pap...

In recent years there have been important advances in the field of automatic plagiarism detection. One variant is cross-language plagiarism detection, which tries to detect plagiarism between documents in different languages. Most of the existing approaches to this task make use of statistical dictionaries to deal with the translations of words in the documents. A statistical dictionary provide...

The present paper introduces the first corpus for the evaluation of Arabic intrinsic plagiarism detection. The corpus consists of 1024 artificial suspicious documents in which 2833 plagiarism cases have been inserted automatically from source documents.

Plagiarism detection systems are known for years in the university community. However, most of the existing detectors for the natural language texts use rather simple comparison methods that make the instances of plagiarism easy to hide. The software, designed for plagiarism detection in computer programs, utilizes far more advanced techniques. We propose a method, which adds functionalities si...