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.
@article{arxiv.2205.10364,
title = {Learning to Reverse DNNs from AI Programs Automatically},
author = {Simin Chen and Hamed Khanpour and Cong Liu and Wei Yang},
journal= {arXiv preprint arXiv:2205.10364},
year = {2022}
}