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Adversarial Example Detection Based on Improved GhostBustersopen access
- Issue Date
- Nov-2022
- Publisher
- Institute of Electronics Information Communication Engineers
- Keywords
- adversarial examples; convolutional neural network; Fourier transformation; image classification
- Citation
- IEICE Transactions on Information and Systems, v.E105D, no.11, pp.1921 - 1922
- Journal Title
- IEICE Transactions on Information and Systems
- Volume
- E105D
- Number
- 11
- Start Page
- 1921
- End Page
- 1922
- ISSN
- 0916-8532
- Abstract
- 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. © 2022 The Institute of Electronics, Information and Communication Engineers.
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