13 citations · 29 across the 5 of their papers we have counts for
4 papers
Defensive Patches for Robust Recognition in the Physical World
Jiakai Wang, Zixin Yin, Pengfei Hu +5
To operate in real-world high-stakes environments, deep learning systems have to endure noises that have been continuously thwarting their robustness. Data-end defense, which impro…
Towards Real-world X-ray Security Inspection: A High-Quality Benchmark and Lateral Inhibition Module for Prohibited Items Detection
Renshuai Tao, Yanlu Wei, Xiangjian Jiang +6
Prohibited items detection in X-ray images often plays an important role in protecting public safety, which often deals with color-monotonous and luster-insufficient objects, resul…
Dual Attention Suppression Attack: Generate Adversarial Camouflage in Physical World
Jiakai Wang, Aishan Liu, Zixin Yin +3
Deep learning models are vulnerable to adversarial examples. As a more threatening type for practical deep learning systems, physical adversarial examples have received extensive r…
Bias-based Universal Adversarial Patch Attack for Automatic Check-out
Aishan Liu, Jiakai Wang, Xianglong Liu +3
Adversarial examples are inputs with imperceptible perturbations that easily misleading deep neural networks(DNNs). Recently, adversarial patch, with noise confined to a small and…