Adversary Resistance

AI/ML systems face a growing threat from adversaries aiming to bias or otherwise degrade inferential capability and operators’ decision-making abilities. The spectrum of attacks can range from the dramatic, like destroying a radar station supplying critical data to a threat analysis tool, to subtler subversions, such as putting stickers on stop signs to mislead a smart car’s visual perception system. The most insidious tampering tactics are those that introduce input noise that is imperceptible to humans, but can still significantly derail a model’s performance.

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At Galois, we research and develop advanced adversary resistance strategies, ensuring that AI/ML models still function as intended and remain trustworthy even under the most challenging conditions or attacks.

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Leveraging our team’s considerable skills in formal methods and the cognitive sciences, our approach encompasses both neuro-symbolic and bio-inspired techniques, training AI/ML models to better account for complex contextual factors and effectively filling gaps in reasoning that could otherwise lead to misguided actions or analysis.

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