Semantic Feature-Based Test Selection for Deep Neural Networks: A Frequency Domain Perspective

Zhouxian Jiang ; Honghui Li ; Xuetao Tian ; Rui Wang

Computer Science and Information Systems, Tome 21 (2024) no. 4.

Résumé

While deep neural networks (DNNs) have great potential for applications in security and safety-critical domains, their limited robustness to adversarial samples and out-of-distribution (OOD) samples raise significant concerns. In the software engineering community, significant efforts have been devoted to devising testing techniques that verify the robustness of DNNs. This paper investigates semantic feature-based test selection for DNNs from a frequency domain perspective and propose a novel method called SaFeTS. Specifically, we leverage saliency detection techniques, such as Fourier Phase Transform to extract semantic features from test cases. These features are then clustered to select diverse test cases to evaluate the robustness of DNNs and model retraining. Experiments on CIFAR-10 and SVHN datasets demonstrate that SaFeTS exposes more varied model errors compared to baseline methods. Further, retraining with SaFeTS-selected samples significantly improves adversarial and out-of-distribution robustness over state-of-the-art test selection methods.

Détail

Keywords: DNN testing, test selection, semantic feature, frequency domain, robustness