Plausible Adversarial Attacks on Direct Parameter Inference Models in Astrophysics by Benjamin Horowitz et al. on Tuesday 29 November

In this abstract we explore the possibility of introducing biases in physical

parameter inference models from adversarial-type attacks. In particular, we

inject small amplitude systematics into inputs to a mixture density networks

tasked with inferring cosmological parameters from observed data. The

systematics are constructed analogously to white-box adversarial attacks. We

find that the analysis network can be tricked into spurious detection of new

physics in cases where standard cosmological estimators would be insensitive.

This calls into question the robustness of such networks and their utility for

reliably detecting new physics.

arXiv: http://arxiv.org/abs/http://arxiv.org/abs/2211.14788v1