3 accepted papers
Many studies have demonstrated the vulnerability of Deep Neural Networks (DNNs) to adversarial attacks. While numerous research efforts have proposed high-performance adversarial attacks and defenses, there is a lack of research regarding the detection of defenses used by models. We have observed th…
The ability of deep neural networks (DNNs) come from extracting and interpreting features from the data provided. By exploiting intermediate features in DNNs instead of relying on hard labels, we craft adversarial perturbation that generalize more effectively, boosting black-box transferability. The…
Adversarial transferability enables black-box attacks on unknown victim deep neural networks (DNNs), rendering attacks viable in real-world scenarios. Current transferable attacks create adversarial perturbation over the entire image, resulting in excessive noise that overfit the source model. Conce…