Abstract
Deep neural networks have shown significant progress in biometric applications. Deep learning networks are particularly vulnerable to Adversarial examples where adversarial examples are manipulated input data. The adversarial attacks make the biometric system to fail in terms of performance. An effective defensive mechanism against adversarial attacks is introduced in the proposed work which is used to detect adversarial iris examples. The proposed defensive mechanism is based on Discrete Wavelet Transform (DWT) which examines the high and mid spectrum of wavelet sub bands. The model then recreates the various denoised versions of the iris images based on DWT. The U-net based Deep convolutional architecture is used for further classification. The proposed process is tested by classifying adversarial iris images affected by various adversarial attacks such as FGSM, Deepfool and iGSM methods. An experimental analysis on a benchmark iris image database, namely IITD, generates excellent results with an average accuracy of 94 percent. Experiment results show that the proposed strategy performs better in detecting adversarial attacks than other state of art defensive models.
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Meenakshi, K., Maragatham, G. AdvIris: a hybrid approach to detecting adversarial iris examples using wavelet transform. Int J Speech Technol 25, 435–441 (2022). https://doi.org/10.1007/s10772-022-09967-8
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DOI: https://doi.org/10.1007/s10772-022-09967-8