  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.
