Improving model generalization by on-manifold adversarial augmentation in the frequency domain

Deep Neural Networks (DNNs) often suffer from performance drops when training and test data distributions differ. Ensuring model generalization for Out-Of-Distribution (OOD) data is crucial, but current models still struggle with accuracy on such data. Recent studies have shown that regular or off-manifold adversarial examples as data augmentation improve OOD generalization. Building on this, we provide theoretical validation that on-manifold adversarial examples can enhance OOD generalization even more. However, generating these examples is challenging due to the complexity of real manifolds. To address this, we propose AdvWavAug, an on-manifold adversarial data augmentation method using a Wavelet module. This approach, based on the AdvProp training framework, leverages wavelet transformation to project an image into the wavelet domain and modifies it within the estimated data manifold. Experiments on various models and datasets, including ImageNet and its distorted versions, show that our method significantly improves model generalization, especially for OOD data. • We introduce an on-manifold adversarial augmentation to improve generalization. • We theoretically prove that on-manifold augmentation improves OOD generalization. • We implement augmentation in the frequency domain to improve efficiency. • Experiments show the effectiveness of our method to improve OOD generalization.