TEN-GUARD: Tensor Decomposition for Backdoor Attack Detection in Deep
  Neural Networks
Khondoker Murad Hossain Tim Oates
As deep neural networks and the datasets used to train them get larger, the
default approach to integrating them into research and commercial projects is
to download a pre-trained model and fine tune it. But these models can have
uncertain provenance, opening up the possibility that they embed hidden
malicious behavior such as trojans or backdoors, where small changes to an
input (triggers) can cause the model to produce incorrect outputs (e.g., to
misclassify). This paper introduces a novel approach to backdoor detection that
uses two tensor decomposition methods applied to network activations. This has
a number of advantages relative to existing detection methods, including the
ability to analyze multiple models at the same time, working across a wide
variety of network architectures, making no assumptions about the nature of
triggers used to alter network behavior, and being computationally efficient.
We provide a detailed description of the detection pipeline along with results
on models trained on the MNIST digit dataset, CIFAR-10 dataset, and two
difficult datasets from NIST's TrojAI competition. These results show that our
method detects backdoored networks more accurately and efficiently than current
state-of-the-art methods.