This academic paper discusses the role of data augmentation in improving the transferability of adversarial examples (AEs) in machine learning. The authors systematically studied 46 augmentation techniques and their effects…
Continue readingThe article presents a novel graph Spectral Alignment (SPA) framework for unsupervised domain adaptation (UDA) in machine learning. The SPA framework aims to extend the in-domain model to distinctive target…
Continue readingThe article is an extensive survey on the transferability of adversarial examples in Deep Neural Networks (DNNs). It discusses the vulnerability of DNNs to adversarial examples – inputs that can…
Continue readingThis paper evaluates the robustness of transformers in the face of adversarial samples in cybersecurity applications. The authors fine-tuned a set of transformer models, Convolutional Neural Network (CNN), and hybrid…
Continue readingThe paper presents a novel approach for facial privacy protection that uses adversarial latent codes in a pretrained generative model’s low-dimensional manifold. The method is designed to generate “naturalistic” images…
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