Darren Yu Yang

About

Darren Yu Yang is a prominent researcher in the field of adversarial machine learning and computer vision, with a particular focus on the security and robustness of deep learning systems. His work addresses critical vulnerabilities in state-of-the-art object detectors, such as YOLO, which are widely deployed in autonomous vehicles, video surveillance, and intelligent robotics. Yang’s most cited paper, “Building Towards 'Invisible Cloak': Robust Physical Adversarial Attack on YOLO Object Detector” (2018, 16 citations), introduces a novel method for crafting physical adversarial patches that can fool object detection models in real-world scenarios. This contribution is significant for demonstrating that deep learning-based AI systems, despite their high accuracy, remain susceptible to carefully designed perturbations—a finding with profound implications for safety-critical applications. By bridging the gap between digital and physical adversarial attacks, Yang has advanced our understanding of model robustness and inspired further research into defensive mechanisms. His work underscores the urgency of developing more resilient AI architectures, making him a key voice in the ongoing dialogue about trustworthy artificial intelligence.

Research Focus

Key Achievements

Top Papers

1. 1
   Building Towards "Invisible Cloak": Robust Physical Adversarial Attack on YOLO Object Detector

   16 citations · 2018

Key Collaborators

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