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leakaudit: Detect and Audit Train/Test Leakage from Near-Duplicate Images

Detects near-duplicate images across dataset splits using perceptual hashing, reports the resulting train/validation/test contamination, and produces a corrected, leak-free split assignment. Intended for machine learning researchers who need to verify that image classification splits do not share near-duplicate samples across partitions before reporting model metrics.

Version: 0.1.0
Depends: R (≥ 4.1)
Imports: magick
Suggests: testthat (≥ 3.0.0), knitr, rmarkdown
Published: 2026-07-29
DOI: 10.32614/CRAN.package.leakaudit
Author: Kartik Patel [aut, cre], Samruddhi Amol Shah [aut], BitandByte [cph]
Maintainer: Kartik Patel <kartikpatel.id at gmail.com>
BugReports: https://github.com/anakincodex/leakaudit/issues
License: MIT + file LICENSE
URL: https://github.com/anakincodex/leakaudit
NeedsCompilation: no
Materials: README, NEWS
CRAN checks: leakaudit results

Documentation:

Reference manual: leakaudit.html , leakaudit.pdf
Vignettes: Getting started with leakaudit (source, R code)

Downloads:

Package source: leakaudit_0.1.0.tar.gz
Windows binaries: r-devel: leakaudit_0.1.0.zip, r-release: leakaudit_0.1.0.zip, r-oldrel: leakaudit_0.1.0.zip
macOS binaries: r-release (arm64): leakaudit_0.1.0.tgz, r-oldrel (arm64): leakaudit_0.1.0.tgz, r-release (x86_64): leakaudit_0.1.0.tgz, r-oldrel (x86_64): leakaudit_0.1.0.tgz

Linking:

Please use the canonical form https://CRAN.R-project.org/package=leakaudit to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.