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.
翻译:随着边端装置上DNN的私营化部署,在设备上DNN的安全引起了重大关切。为了自动量化在设备上DNN的模型渗漏风险,我们提议NNREverse,这是第一个可以将DNNN从没有域知识的AI方案中逆转过来的以学习为基础的方法。NNEReve培训一种代表模式,以代表DNN层二元代码的语义。通过搜索我们数据库中最相似的功能,NNNREverse引用了给定函数二元代码的层类型。为了准确地代表组装指令的语义学,NNNREverse提出一个更精细的嵌入模式,以代表组装功能的文字和结构结构结构结构。