  The increasing importance of both deep neural networks (DNNs) and cloud
services for training them means that bad actors have more incentive and
opportunity to insert backdoors to alter the behavior of trained models. In
this paper, we introduce a novel method for backdoor detection that extracts
features from pre-trained DNN's weights using independent vector analysis (IVA)
followed by a machine learning classifier. In comparison to other detection
techniques, this has a number of benefits, such as not requiring any training
data, being applicable across domains, operating with a wide range of network
architectures, not assuming the nature of the triggers used to change network
behavior, and being highly scalable. We discuss the detection pipeline, and
then demonstrate the results on two computer vision datasets regarding image
classification and object detection. Our method outperforms the competing
algorithms in terms of efficiency and is more accurate, helping to ensure the
safe application of deep learning and AI.
