3 citations · 3 across the 4 of their papers we have counts for
4 papers
Superpixel Attack: Enhancing Black-box Adversarial Attack with Image-driven Division Areas
Issa Oe, Keiichiro Yamamura, Hiroki Ishikura +2
Deep learning models are used in safety-critical tasks such as automated driving and face recognition. However, small perturbations in the model input can significantly change the…
Enhancing Quantum-ready QUBO-based Suppression for Object Detection with Appearance and Confidence Features
Keiichiro Yamamura, Toru Mitsutake, Hiroki Ishikura +3
Quadratic Unconstrained Binary Optimization (QUBO)-based suppression in object detection is known to have superiority to conventional Non-Maximum Suppression (NMS), especially for…
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…
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…