  The risk of hardware Trojans being inserted at various stages of chip
production has increased in a zero-trust fabless era. To counter this, various
machine learning solutions have been developed for the detection of hardware
Trojans. While most of the focus has been on either a statistical or deep
learning approach, the limited number of Trojan-infected benchmarks affects the
detection accuracy and restricts the possibility of detecting zero-day Trojans.
To close the gap, we first employ generative adversarial networks to amplify
our data in two alternative representation modalities, a graph and a tabular,
ensuring that the dataset is distributed in a representative manner. Further,
we propose a multimodal deep learning approach to detect hardware Trojans and
evaluate the results from both early fusion and late fusion strategies. We also
estimate the uncertainty quantification metrics of each prediction for
risk-aware decision-making. The outcomes not only confirms the efficacy of our
proposed hardware Trojan detection method but also opens a new door for future
studies employing multimodality and uncertainty quantification to address other
hardware security challenges.
