| --- |
| pretty_name: ImpMIA Data |
| tags: |
| - membership-inference |
| - privacy |
| - image-classification |
| - cifar10 |
| - cifar100 |
| - cinic10 |
| - arxiv:2510.10625 |
| license: other |
| --- |
| |
| # ImpMIA Data |
|
|
| Data for [ImpMIA: Leveraging Implicit Bias for Membership Inference Attack](https://arxiv.org/abs/2510.10625). |
|
|
| ## Layout |
|
|
| ```text |
| data/ |
| no_auxiliary_knowledge/{cifar10,cifar100,cinic10}/ |
| full_auxiliary_knowledge/cinic10/ |
| ``` |
|
|
| Each directory contains `x_train.npy`, `y_train.npy`, `x_test.npy`, and `y_test.npy`. Images are `uint8` arrays in NHWC format; labels are `int64`. |
|
|
| The No-Auxiliary-Knowledge candidate pools combine 30,000 target-distribution images with auxiliary images from CINIC-10, Open Images, or generated data. Their first 30,000 rows form the target-training source pool. The Full-Auxiliary-Knowledge CINIC-10 pool contains 50,000 CINIC-10 images. |
|
|
| ## Licensing |
|
|
| See `LICENSES.md` and the Open Images attribution tables under `metadata/`. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @article{golbari2026impmia, |
| title = {ImpMIA: Leveraging Implicit Bias for Membership Inference Attack}, |
| author = {Golbari, Yuval and Wasserman, Navve and Vardi, Gal and Irani, Michal}, |
| journal = {Transactions on Machine Learning Research}, |
| year = {2026} |
| } |
| ``` |
|
|
