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Exploiting Frequency Spectrum of Adversarial Images for General Robustness
In recent years, there has been growing concern over the vulnerability of convolutional neural networks (CNNs) to image perturbations.
Adversarial amplitude swap towards robust image classifiers
Extensive experiments revealed that the images generated by combining the amplitude spectrum of adversarial images and the phase spectrum of clean images accommodates moderate and general perturbations, and training with these images equips a CNN classifier with more general robustness, performing well under both common corruptions and adversarial perturbations.
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