  Document understanding models are increasingly employed by companies to
supplant humans in processing sensitive documents, such as invoices, tax
notices, or even ID cards. However, the robustness of such models to privacy
attacks remains vastly unexplored. This paper presents CDMI, the first
reconstruction attack designed to extract sensitive fields from the training
data of these models. We attack LayoutLM and BROS architectures, demonstrating
that an adversary can perfectly reconstruct up to 4.1% of the fields of the
documents used for fine-tuning, including some names, dates, and invoice
amounts up to six-digit numbers. When our reconstruction attack is combined
with a membership inference attack, our attack accuracy escalates to 22.5%. In
addition, we introduce two new end-to-end metrics and evaluate our approach
under various conditions: unimodal or bimodal data, LayoutLM or BROS backbones,
four fine-tuning tasks, and two public datasets (FUNSD and SROIE). We also
investigate the interplay between overfitting, predictive performance, and
susceptibility to our attack. We conclude with a discussion on possible
defenses against our attack and potential future research directions to
construct robust document understanding models.
