title: Dataset Size Recovery from LoRA Weights
summary: 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.
paper_authors._id: 667e2f9ccef5b7fa35d65325
paper_authors.name: Mohammad Salama
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-06-28T08:02:45.409Z
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paper_authors.fullname: Mohammad Salama
paper_authors.type: user
paper_authors.user: MoSalama98
paper_authors._id: 667e2f9ccef5b7fa35d65326
paper_authors.name: Jonathan Kahana
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-06-28T13:19:03.199Z
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paper_authors.avatarUrl: /avatars/113f02c1b1f8d33d3487daa867afcd3f.svg
paper_authors.fullname: Jonathan Kahana
paper_authors.type: user
paper_authors.user: jonkahana
paper_authors._id: 667e2f9ccef5b7fa35d65327
paper_authors.name: Eliahu Horwitz
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-06-28T13:19:09.094Z
paper_authors._id: 630dd4218df86f1e5beb2ed7
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paper_authors.fullname: Eliahu Horwitz
paper_authors.type: user
paper_authors.user: Eliahu
paper_authors._id: 667e2f9ccef5b7fa35d65328
paper_authors.name: Yedid Hoshen
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-06-28T13:19:14.192Z
paper_authors._id: 646cfc3b4220471ca0c56b20
paper_authors.avatarUrl: /avatars/19d6ab141ec2cd25c1c3b45fd8f69910.svg
paper_authors.fullname: Yedid Hoshen
paper_authors.type: user
paper_authors.user: yedid
paper_title: Dataset Size Recovery from LoRA Weights
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: model inversion membership inference dataset size recovery DSiRe LoRA fine-tuning LoRA-WiSE