Dataset Size Recovery from LoRA Weights
Model inversion and membership inference attacks aim to reconstruct and
verify the data which a model was trained on. However, they are not guaranteed
to find all training samples as they do not know the size of the training set.
In this paper, we introduce a new task: dataset size recovery, that aims to
determine the number of samples used to train a model, directly from its
weights. We then propose DSiRe, a method for recovering the number of images
used to fine-tune a model, in the common case where fine-tuning uses LoRA. We
discover that both the norm and the spectrum of the LoRA matrices are closely
linked to the fine-tuning dataset size; we leverage this finding to propose a
simple yet effective prediction algorithm. To evaluate dataset size recovery of
LoRA weights, we develop and release a new benchmark, LoRA-WiSE, consisting of
over 25000 weight snapshots from more than 2000 diverse LoRA fine-tuned models.
Our best classifier can predict the number of fine-tuning images with a mean
absolute error of 0.36 images, establishing the feasibility of this attack.
667e2f9ccef5b7fa35d65325
Mohammad Salama
claimed_verified
2024-06-28T08:02:45.409Z
654fe20f1b3d3092a1c95f69
/avatars/fc154a27c8e2e833f830208e05d66f5b.svg
Mohammad Salama
user
MoSalama98
667e2f9ccef5b7fa35d65326
Jonathan Kahana
admin_assigned
2024-06-28T13:19:03.199Z
6465fd33dac127ac80f0b334
/avatars/113f02c1b1f8d33d3487daa867afcd3f.svg
Jonathan Kahana
user
jonkahana
667e2f9ccef5b7fa35d65327
Eliahu Horwitz
admin_assigned
2024-06-28T13:19:09.094Z
630dd4218df86f1e5beb2ed7
https://cdn-avatars.huggingface.co/v1/production/uploads/630dd4218df86f1e5beb2ed7/fKvNWyWv6CVBdbXXUlrYv.jpeg
Eliahu Horwitz
user
Eliahu
667e2f9ccef5b7fa35d65328
Yedid Hoshen
admin_assigned
2024-06-28T13:19:14.192Z
646cfc3b4220471ca0c56b20
/avatars/19d6ab141ec2cd25c1c3b45fd8f69910.svg
Yedid Hoshen
user
yedid
Dataset Size Recovery from LoRA Weights
Model inversion and membership inference attacks aim to reconstruct and
verify the data which a model was trained on. However, they are not guaranteed
to find all training samples as they do not know the size of the training set.
In this paper, we introduce a new task: dataset size recovery, that aims to
determine the number of samples used to train a model, directly from its
weights. We then propose DSiRe, a method for recovering the number of images
used to fine-tune a model, in the common case where fine-tuning uses LoRA. We
discover that both the norm and the spectrum of the LoRA matrices are closely
linked to the fine-tuning dataset size; we leverage this finding to propose a
simple yet effective prediction algorithm. To evaluate dataset size recovery of
LoRA weights, we develop and release a new benchmark, LoRA-WiSE, consisting of
over 25000 weight snapshots from more than 2000 diverse LoRA fine-tuned models.
Our best classifier can predict the number of fine-tuning images with a mean
absolute error of 0.36 images, establishing the feasibility of this attack.
A method called DSiRe is introduced for estimating the dataset size used to fine-tune models using LoRA, leveraging norms and spectra of LoRA matrices.
model inversion membership inference dataset size recovery DSiRe LoRA fine-tuning LoRA-WiSE