  With the privatization deployment of DNNs on edge devices, the security of
on-device DNNs has raised significant concern. To quantify the model leakage
risk of on-device DNNs automatically, we propose NNReverse, the first
learning-based method which can reverse DNNs from AI programs without domain
knowledge. NNReverse trains a representation model to represent the semantics
of binary code for DNN layers. By searching the most similar function in our
database, NNReverse infers the layer type of a given function's binary code. To
represent assembly instructions semantics precisely, NNReverse proposes a more
fine-grained embedding model to represent the textual and structural-semantic
of assembly functions.
