leakaudit

Detects near-duplicate images across train/validation/test splits using perceptual hashing (dHash), reports the resulting leakage, and produces a corrected, leak-free split assignment.

Why

When building an image classification dataset, near-duplicate images (same photo re-saved, resized, lightly cropped, or recompressed) commonly end up split across train and test. This silently inflates test-set performance, since the model has effectively already seen a near-identical copy of the “unseen” example.

Install

# development version
remotes::install_github("anakincodex/leakaudit")

# from CRAN, once published
install.packages("leakaudit")

Usage

library(leakaudit)

hashes  <- compute_hashes(image_paths, split = split_labels)
grouped <- find_duplicate_groups(hashes, threshold = 5)
report  <- dhash_audit(grouped)
print(report)

clean <- clean_splits(grouped, priority = c("train", "val", "test"))

How this differs from other leakage packages

A few CRAN packages deal with “leakage” in machine learning workflows, but none of them work at the image level:

• leakr and bioLeak detect leakage in tabular/biomedical data (row overlap, target leakage, batch confounding). They operate on data frames of features and outcomes, not image files.
• OpenImageR provides general-purpose perceptual hashing (dhash(), phash()) but has no concept of dataset splits or leakage reporting.

leakaudit is specifically about auditing and fixing near-duplicate contamination across image dataset splits, and its dhash_audit() function is unrelated to bioLeak::audit_leakage(), which audits fitted models on tabular data via permutation testing.