3 citations · 3 across the 2 of their papers we have counts for
2 papers
cs.LG2024
Enhancing Output Diversity Improves Conjugate Gradient-based Adversarial Attacks
Keiichiro Yamamura, Issa Oe, Hiroki Ishikura +1
Deep neural networks are vulnerable to adversarial examples, and adversarial attacks that generate adversarial examples have been studied in this context. Existing studies imply th…
cs.LG2022★ 3 cited
Diversified Adversarial Attacks based on Conjugate Gradient Method
Keiichiro Yamamura, Haruki Sato, Nariaki Tateiwa +5
Deep learning models are vulnerable to adversarial examples, and adversarial attacks used to generate such examples have attracted considerable research interest. Although existing…