Paper ID: 2201.09774 • Published Jan 24, 2022

Hiding Behind Backdoors: Self-Obfuscation Against Generative Models

Siddhartha Datta, Nigel Shadbolt
TL;DR
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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.