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Due to data's unavailability or large size, and the high computational andhuman labor costs of training machine learning models, it is a common practiceto rely on open source pre-trained models whenever possible. However, thispractice is worry some from the security perspective. Pre-trained models can beinfected with Trojan attacks, in which the attacker embeds a trigger in themodel such that the model's behavior can be controlled by the attacker when thetrigger is present in the input. In this paper, we present our preliminary workon a novel method for Trojan model detection. Our method creates a signaturefor a model based on activation optimization. A classifier is then trained todetect a Trojan model given its signature. Our method achieves state of the artperformance on two public datasets.

Trojan Model Detection Using Activation Optimization