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Potential Issue with Member/Non-member Data Handling in Inference Code #3
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@ganyuhhutao I think it's one of my fault to write the code in confusing manner, but I think I did implemented in right way. The target model is trained on the testset of the CIFAR dataset, where data points are saved as Lines 46 to 47 in 5dc858b
Lines 64 to 69 in 5dc858b
Subsequently, the target model inferences on member vs non-member data. The model has never seen trainset of the CIFAR dataset before, so it is non-member data. For non-member data, Lines 53 to 64 in 5dc858b
Sorry if this part was confusing. If I were to write code now, I would have written as following: list_nonmember_indices = np.random.choice(len(trainset), len(pd.read_csv("./attack/train_indices.csv")["index"].to_list()) , replace=False) But in the end, I don't think there's a problem in the alignment of the code and the paper. |
If possible, can please @dokyungs give authorization to the repo back to me so that I can clean up issues and fix the code? |
Thank you for your explanation. |
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 fromtrain_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.
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