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Adversarial Example Detection Based on Improved GhostBusters

2022; Institute of Electronics, Information and Communication Engineers; Volume: E105.D; Issue: 11 Linguagem: Inglês

10.1587/transinf.2022ngl0005

1745-1361

Hyunghoon KIM, Jiwoo SHIN, Hyo Jin Jo,

Anomaly Detection Techniques and Applications

In various studies of attacks on autonomous vehicles (AVs), a phantom attack in which advanced driver assistance system (ADAS) misclassifies a fake object created by an adversary as a real object has been proposed. In this paper, we propose F-GhostBusters, which is an improved version of GhostBusters that detects phantom attacks. The proposed model uses a new feature, i.e, frequency of images. Experimental results show that F-GhostBusters not only improves the detection performance of GhostBusters but also can complement the accuracy against adversarial examples.