Deep Neural Networks (DNN) are known to be vulnerable to adversarial samples,
the detection of which is crucial for the wide application of these DNN models.
Recently, a number of deep testing methods in software engineering were
proposed to find the vulnerability of DNN systems, and one of them, i.e., Model
Mutation Testing (MMT), was used to successfully detect various adversarial
samples generated by different kinds of adversarial attacks. However, the
mutated models in MMT are always huge in number (e.g., over 100 models) and
lack diversity (e.g., can be easily circumvented by high-confidence adversarial
samples), which makes it less efficient in real applications and less effective
in detecting high-confidence adversarial samples. In this study, we propose
Graph-Guided Testing (GGT) for adversarial sample detection to overcome these
aforementioned challenges. GGT generates pruned models with the guide of graph
characteristics, each of them has only about 5% parameters of the mutated model
in MMT, and graph guided models have higher diversity. The experiments on
CIFAR10 and SVHN validate that GGT performs much better than MMT with respect
to both effectiveness and efficiency.

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