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Deep neural networks (DNNs) are vulnerable to "backdoor" poisoning attacks,in which an adversary implants a secret trigger into an otherwise normallyfunctioning model. Detection of backdoors in trained models without access tothe training data or example triggers is an important open problem. In thispaper, we identify an interesting property of these models: adversarialperturbations transfer from image to image more readily in poisoned models thanin clean models. This holds for a variety of model and trigger types, includingtriggers that are not linearly separable from clean data. We use this featureto detect poisoned models in the TrojAI benchmark, as well as additionalmodels.

TOP: Backdoor Detection in Neural Networks via Transferability of Perturbation