Basic Plagiarism Detection

Motivation

Online plagiarism detection tools usually come with a few constraints. It could be a paid-only service, the number of characters to check could be artificially limited, etc. This tool aims to fill a gap where:

• Plagiarism cases are usually simple copy-paste jobs of a few text phrases with minor edits,
• Paying for an online tool is unpalatable,
• The source texts that might be copied from can be put together manually by the user into a few files (i.e. the Internet is not automatically searched by the tool), or the only concern is people copying from each other, and
• Running a command-line tool is simple enough for the user

Table of Contents

• Motivation
• Table of Contents
• Philosophy
• Definitions
• Project Objectives
  • Hard Objectives
  • Soft (Optimization) Objectives
  • Anti-Objectives
• Project Status
• Installation Options
  • Binary Release
  • Cargo Install
  • Building from source
• Usage
• Technical Details
  • Defining Plagiarism
  • Choosing n, s and M

Philosophy

This tool is really only to catch amateur attempts at plagiarism. Whatever your opinion is on plagiarism tools, we can probably agree that lazy copy-paste jobs of chunks of text should probably not be respected. Therefore, this tool is designed to make detecting these situations easier.

Definitions

• Untrusted: Something that might be plagiarised
• Trusted: Something that is definitely not plagiarised, but might be a source used in plagiarism

Project Objectives

Hard Objectives

• Detect potential cases of plagiarism between multiple untrusted strings (intra-source plagiarism)
• Detect potential cases of plagiarism between trusted source strings and untrusted strings (external-source plagiarism)

Soft (Optimization) Objectives

1. Minimizing false positive and false negative detection rates
2. Fastest possible detection speed without compromising objective 1.

Anti-Objectives

• The project does not aim to search the Internet automatically for potential plagiarism sources. These sources have to be assembled by users manually into text files in the trusted folder.
• This is not a tool for code plagiarism detection!. Please refer to Stanford's MOSS instead.

Project Status

• All options are usable in the executable, and the equal metric is quite fast at detecting copy-paste plagiarism of a few words.
• The lev metric is too slow for large datasets, but promises more fine-grained control over how different two phrases can be.
• The current output format is both in HTML and in the terminal. In the HTML output, both texts are displayed side-by-side, with all detected plagiarized phrases being highlighted in bold. The percent of plagiarism detected (# plagiarized words / all words * 100%) is also indicated for each source pair. The goal is for all of this to be color-coded, which is a project priority.

Installation Options

Binary Release

Download a binary from the Releases page. Currently only x86_64-unknown-linux-gnu targets are supported.

Cargo Install

1. Install the rust language toolchain (https://www.rust-lang.org/tools/install).
2. Run cargo install plagiarism-basic
3. The executable should be installed from crates.io automatically by cargo.

Building from source

1. The previous two options will likely distribute an outdated version of this tool. For the most up-to-date code, building from source is recommended.
2. Install the rust language toolchain (https://www.rust-lang.org/tools/install).
3. git clone this repository to a folder of your choice.
4. Run cargo build --release in that folder.
5. The target/release folder will contain the plagiarism-basic executable to be used.

Usage

Some setup is required:

1. Two folders need to be created anywhere, a "trusted" folder and an "untrusted" folder.
2. The "trusted" folder may contain any number of files in its top level directory. Each file will be treated as a separate trusted source of text. This is where you might put the text of the top 10 Google search results, for e.g.
3. The "untrusted" folder may contain any number of files in its top level directory. Each file will be treated as a separate untrusted source of text. This is where you would put each separate "submission" from a student, for e.g.
4. The files in both folders must only contain UTF-8 interpretable text. The name of the file will be used in the output of the program, so naming the files appropriately is a good idea.
5. After these steps are done, the plagiarism-basic executable can be run and the path to these folders can be specified in the arguments to the executable.

$ ./plagiarism-basic -h
Basic Plagiarism Checker v0.1
Sriram Sami (@frizensami on GitHub)
Checks for plagiarism using very basic metrics between different text files

USAGE:
    plagiarism-basic -m <metric> -n <sensitivity> -s <similarity> -t <trusted-directory> -u <untrusted-directory>

FLAGS:
    -h, --help       Prints help information
    -V, --version    Prints version information

OPTIONS:
    -m <metric>                     Sets the metric (function) used for similarity testing. Equal checks that both
                                    strings are equal, and lev uses the Levenshtein distance [possible values: equal,
                                    lev]
    -n <sensitivity>                Sets the number of words required to form a unit of plagiarism checking
    -s <similarity>                 Sets the threshold value for plagiarism to be detected by a chosen metric
    -t <trusted-directory>          Sets the directory containing trusted text files. Each file will be treated as a
                                    separate possible plagiarism source text.
    -u <untrusted-directory>        Sets the directory containing untrusted text files. Each file will be treated as a
                                    separate submission by a separate person.

Technical Details

Defining Plagiarism

Informally, two strings that are long enough and with the same number of words that are "similar enough" by a chosen metric are considered to be plagiarised.

Formally:

• Two separate strings (s1 and s2) consisting of words (a sequence of characters without a space) are considered plagiarised if:
• Both have l words
  • Where l < some user-chosen sensitivity value n
• Where a metric M and similarity value s produces M(s1, s2) <= s
• Subject to pre-processing of
  • Removing CR + LF
  • Removing extra spaces (only one space between words)
  • Converting all letters to lowercase
  • Removing all non alphanumeric characters

Choosing n, s and M

• n is a user-chosen value to indicate how many words a string needs to be before being considered for plagiarism
• s is a user-chosen value to indicate how similar the strings have to be before being considered for plagiarism
• M is the metric used to evaluate the strings for similarity. They can be one of the following
  • equal: checks if the strings are equal, ignores s value. Uses hashed set intersections, very fast.
  • lev: uses the Levenshtein distance between the words, uses the s value. Compares between all combinations of string fragments, very slow at the moment.