In this paper, we introduce the TrojAI software framework, an open source set
of Python tools capable of generating triggered (poisoned) datasets and
associated deep learning (DL) models with trojans at scale. We utilize the
developed framework to generate a large set of trojaned MNIST classifiers, as
well as demonstrate the capability to produce a trojaned reinforcement-learning
model using vector observations. Results on MNIST show that the nature of the
trigger, training batch size, and dataset poisoning percentage all affect
successful embedding of trojans. We test Neural Cleanse against the trojaned
MNIST models and successfully detect anomalies in the trained models
approximately $18\%$ of the time. Our experiments and workflow indicate that
the TrojAI software framework will enable researchers to easily understand the
effects of various configurations of the dataset and training hyperparameters
on the generated trojaned deep learning model, and can be used to rapidly and
comprehensively test new trojan detection methods.

本文介绍了 TrojAI 软件框架，它是一套开源的 Python 工具，可以生成带有木马的数据集和相应的深度学习模型，以及演示了利用该框架生成基于 MNIST 的带有木马的分类器和使用矢量观测生成带有木马的强化学习模型的能力。同时，该框架还能用于快速全面测试新的木马检测方法。