论文标题
使用深神经树架构窃取黑盒功能
Stealing Black-Box Functionality Using The Deep Neural Tree Architecture
论文作者
论文摘要
本文通过引入名为“深神经树（DNTS）”的机器学习（ML）体系结构来克服黑盒模型的功能迈出了重大步骤。这种新的体系结构可以学会分开黑框模型的不同任务，并克隆其特定于任务的行为。我们建议使用主动学习算法训练DNT，以获得更快，更有效的训练。与先前的工作相反，我们仅根据输入输出交互研究一个复杂的“受害者”黑盒模型，与此同时，攻击者和受害者模型可能具有完全不同的内部体系结构。攻击者是一种基于ML的算法，而受害者是一个通常未知的模块，例如多功能数字芯片，复杂的模拟电路，机械系统，软件逻辑或这些混合物。受过训练的DNT模块不仅可以充当攻击的模块，而且还可以为克隆模型提供一定程度的解释性，这是由于所提出的架构的类似树状的性质。
This paper makes a substantial step towards cloning the functionality of black-box models by introducing a Machine learning (ML) architecture named Deep Neural Trees (DNTs). This new architecture can learn to separate different tasks of the black-box model, and clone its task-specific behavior. We propose to train the DNT using an active learning algorithm to obtain faster and more sample-efficient training. In contrast to prior work, we study a complex "victim" black-box model based solely on input-output interactions, while at the same time the attacker and the victim model may have completely different internal architectures. The attacker is a ML based algorithm whereas the victim is a generally unknown module, such as a multi-purpose digital chip, complex analog circuit, mechanical system, software logic or a hybrid of these. The trained DNT module not only can function as the attacked module, but also provides some level of explainability to the cloned model due to the tree-like nature of the proposed architecture.
