  Deep neural networks (DNNs) have been found vulnerable to backdoor attacks,
raising security concerns about their deployment in mission-critical
applications. There are various approaches to detect backdoor attacks, however
they all make certain assumptions about the target attack to be detected and
require equal and huge numbers of clean and backdoor samples for training,
which renders these detection methods quite limiting in real-world
circumstances.
  This study proposes a novel one-class classification framework called
One-class Graph Embedding Classification (OCGEC) that uses GNNs for model-level
backdoor detection with only a little amount of clean data. First, we train
thousands of tiny models as raw datasets from a small number of clean datasets.
Following that, we design a ingenious model-to-graph method for converting the
model's structural details and weight features into graph data. We then
pre-train a generative self-supervised graph autoencoder (GAE) to better learn
the features of benign models in order to detect backdoor models without
knowing the attack strategy. After that, we dynamically combine the GAE and
one-class classifier optimization goals to form classification boundaries that
distinguish backdoor models from benign models.
  Our OCGEC combines the powerful representation capabilities of graph neural
networks with the utility of one-class classification techniques in the field
of anomaly detection. In comparison to other baselines, it achieves AUC scores
of more than 98% on a number of tasks, which far exceeds existing methods for
detection even when they rely on a huge number of positive and negative
samples. Our pioneering application of graphic scenarios for generic backdoor
detection can provide new insights that can be used to improve other backdoor
defense tasks. Code is available at https://github.com/jhy549/OCGEC.
