Evaluating the Robustness and Generalizability of D-LeDe Using Multiple Automotive Datasets

Journal article, 2026

Data leakage, where semantically or visually similar samples exist across training and test splits, continues to threaten the reliability of object detection benchmarks. The D-LeDe method was recently proposed as a statistical technique for detecting data leakage, showing promise in initial applications. This paper extends the D-LeDe method to evaluate its robustness and generalizability through a focused experimental study. We revisit the KITTI dataset which was previously identified as leakage-prone and introduce a new application of D-LeDe on SODA10M. The core investigation centers on whether visually similar images, measured using perceptual hashing, are the primary cause of data leakage indications captured by D-LeDe. To this end, we progressively remove visually similar image pairs from the test sets of both datasets and observe changes in the Relative Increase Rate, the key decision metric of D-LeDe. Results show that even after removing about 50% of similar images from the KITTI test set, D-LeDe continues to detect data leakage, suggesting other forms of redundancy or latent factors causing the leakage. In contrast, SODA10M consistently remains leakage-free across all levels of image pair removal. These findings reinforce the reliability of D-LeDe in detecting non-obvious forms of data leakage, underscoring its applicability as a diagnostic tool for dataset integrity in object detection workflows.