Backdoor Suppression in Neural Networks using Input Fuzzing and Majority Voting

While inference is needed at the edge, training is typically done at the cloud. Therefore, data necessary for training a model, as well as the trained model, have to be transmitted back and forth between the edge and the cloud training infrastructure. This creates significant security issues, including the inclusion of a backdoor sent to the user without the user's knowledge. This article presents an approach where a trained model can still operate as expected, irrespective of the presence of such a backdoor.