DMRA: Model Usability Detection Scheme Against Model-Reuse Attacks in the Internet of Things

Internet of Things (IoT) devices can utilize deep learning (DL) to boost their intelligence, but also suffer from the long model training process. IoT devices thus may reuse public pretrained models to expedite the training through transfer learning. However, pretrained models may be subject to model-reuse attacks initiated by malicious DL servers, causing models to misclassify targeted data, which poses a threat to the security of IoT devices. In this work, we propose a new model usability detection scheme, the defense against model-reuse attacks (DMRAs), suitable for IoT scenarios. DMRA employs a variant of Lagrange’s mean value theorem to reverse-check the model, which is computationally efficient, thus, suitable for resource-constrained devices. Experimental evaluations on different data sets first validate that model-reuse attacks can attack models in federated learning. And, then demonstrate that DMRA detects such insidious attacks with up to 80% success rate at a lightweight computational cost.