CelebA-C: Digital Corruptions
Abstract
This dataset is part of the CelebA-C benchmark, a large-scale image corruption dataset derived from the CelebA face dataset for evaluating model robustness under distribution shift. This record contains the Digital-based corruption subset, including Contrast, Elastic_transform, JPEG_compression, Pixelate, Saturate and Spatter each generated at five severity levels on the CelebA test split. Images are stored in HDF5 format with lossless gzip compression and chunked storage to enable efficient loading and reproducible evaluation. The complete CelebA-C benchmark is released as four complementary datasets due to repository size constraints:(i) Celeba_blur.h5(ii) Celeba_noise.h5(iii) Celeba_digital.h5, and(iv) Celeba_weather.h5 (refer to the DOIs of the other three datasets in the Related Works section)Together, these four records constitute the complete CelebA-C dataset.
Fields of Study & Subjects
Adversarial Robustness in Machine Learning
Digital Media Forensic Detection
Generative Adversarial Networks and Image Synthesis
Publication details
- Journal / source
- Zenodo (CERN European Organization for Nuclear Research)
- Publication date
- 6 February 2026
- Language
- EN
- Document type
- Dataset