  Deep neural networks (DNNs) are prominent due to their superior performance
in many fields. The deep-learning-as-a-service (DLaaS) paradigm enables
individuals and organizations (clients) to outsource their DNN learning tasks
to the cloud-based platforms. However, the DLaaS server may return incorrect
DNN models due to various reasons (e.g., Byzantine failures). This raises the
serious concern of how to verify if the DNN models trained by potentially
untrusted DLaaS servers are indeed correct. To address this concern, in this
paper, we design VeriDL, a framework that supports efficient correctness
verification of DNN models in the DLaaS paradigm. The key idea of VeriDL is the
design of a small-size cryptographic proof of the training process of the DNN
model, which is associated with the model and returned to the client. Through
the proof, VeriDL can verify the correctness of the DNN model returned by the
DLaaS server with a deterministic guarantee and cheap overhead. Our experiments
on four real-world datasets demonstrate the efficiency and effectiveness of
VeriDL.
