This paper utilizes Reinforcement Learning (RL) as a means to automate the
Hardware Trojan (HT) insertion process to eliminate the inherent human biases
that limit the development of robust HT detection methods. An RL agent explores
the design space and finds circuit locations that are best for keeping inserted
HTs hidden. To achieve this, a digital circuit is converted to an environment
in which an RL agent inserts HTs such that the cumulative reward is maximized.
Our toolset can insert combinational HTs into the ISCAS-85 benchmark suite with
variations in HT size and triggering conditions. Experimental results show that
the toolset achieves high input coverage rates (100\% in two benchmark
circuits) that confirms its effectiveness. Also, the inserted HTs have shown a
minimal footprint and rare activation probability.

本文利用强化学习 (Reinforcement Learning，RL) 自动化硬件特洛伊（Hardware Trojan, HT）嵌入过程，以消除制约 HT 检测方法成果的人为偏见。作者开发的工具集可以将组合 HT 插入 ISCAS-85 基准套件，具备 HT 大 小和触发条件的变化性，并证明了其效果高，输入覆盖率高达 100％，所插入的 HT 具有最小的体积和罕有的激活概率。