5 accepted papers
Adversarial examples pose serious threats to deep neural networks (DNNs), revealing fundamental vulnerabilities in model robustness. However, most existing adversarial attacks directly manipulate densely activated and highly redundant feature representations, which often leads to overfitting on surr…
Transfer-based attacks pose a significant security threat to deep neural networks (DNNs), due to their strong performance on unseen models in real-world black-box scenarios. Building on this, feature importance-based attacks further improve the transferability of adversarial examples by effectively…
Vision transformers (ViTs) perform exceptionally well in various computer vision tasks but remain vulnerable to adversarial attacks. Recent studies have shown that the transferability of adversarial examples exists for CNNs, and the same holds true for ViTs. However, existing ViT attacks aggressivel…
Deep neural networks (DNNs) are vulnerable to highly transferable adversarial attacks. Especially many studies have shown that sparse attacks pose a significant threat to DNNs on account of their exceptional imperceptibility. Current sparse attack methods mostly limit only the magnitude and number o…
Aiming at crafting a single universal adversarial perturbation (UAP) to fool CNN models for various data samples, universal attack enables a more efficient and accurate evaluation for the robustness of CNN models. Early universal attacks craft UAPs depending on data priors. For more practical applic…