yonsei-sslab / mia Goto Github PK

Hi! I have a question about the way that training dataset of the attack model is formulated.

In the original paper, the dataset consists of three parts: label of the data, prediction vector, and whether the data is in the original training dataset.

However, in you implementation, the dataset consists of two parts: top k probabilities, and whether the data is in the original training dataset.

I wonder if this modification would lead to difference in the way that MIA works. I'm new to MIA, so I would appreciate it if you can help.

Hello, I've been reviewing the code and noticed a potential issue regarding the handling of member and non-member data in the inference.py script. It seems that the indices from train_indices.csv are being used as non-member data for inference, which may not align with the typical definitions used in MIA.

I hope this information is helpful, and I look forward to any clarification or updates you can provide on this matter.

Thank you for your attention to this issue.