Dataset Size Recovery from LoRA Weights
<|reference_start|>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.<|reference_end|>
@article{salama2024dataset,
    title={Dataset Size Recovery from LoRA Weights},
    author={Mohammad Salama and Jonathan Kahana and Eliahu Horwitz and Yedid
  Hoshen},
    journal={arXiv preprint arXiv:2406.19395},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.19395},
    primaryClass={cs.CV}
}