  Attack vectors that compromise machine learning pipelines in the physical
world have been demonstrated in recent research, from perturbations to
architectural components. Building on this work, we illustrate the
self-obfuscation attack: attackers target a pre-processing model in the system,
and poison the training set of generative models to obfuscate a specific class
during inference. Our contribution is to describe, implement and evaluate a
generalized attack, in the hope of raising awareness regarding the challenge of
architectural robustness within the machine learning community.
