5 papers
Enhancing Security in Deep Reinforcement Learning: A Comprehensive Survey on Adversarial Attacks and Defenses
Wu Yichao, Wang Yirui, Ding Panpan +3
With the wide application of deep reinforcement learning (DRL) techniques in complex fields such as autonomous driving, intelligent manufacturing, and smart healthcare, how to impr…
Generating Transferrable Adversarial Examples via Local Mixing and Logits Optimization for Remote Sensing Object Recognition
Chun Liu, Hailong Wang, Bingqian Zhu +5
Deep Neural Networks (DNNs) are vulnerable to adversarial attacks, posing significant security threats to their deployment in remote sensing applications. Research on adversarial a…
Adversarial Patch Attack for Ship Detection via Localized Augmentation
Chun Liu, Panpan Ding, Zheng Zheng +5
Current ship detection techniques based on remote sensing imagery primarily rely on the object detection capabilities of deep neural networks (DNNs). However, DNNs are vulnerable t…
Boosting Adversarial Transferability for Hyperspectral Image Classification Using 3D Structure-invariant Transformation and Weighted Intermediate Feature Divergence
Chun Liu, Bingqian Zhu, Tao Xu +5
Deep Neural Networks (DNNs) are vulnerable to adversarial attacks, which pose security challenges to hyperspectral image (HSI) classification based on DNNs. Numerous adversarial at…
CPM R-CNN: Calibrating Point-guided Misalignment in Object Detection
Bin Zhu, Qing Song, Lu Yang +3
In object detection, offset-guided and point-guided regression dominate anchor-based and anchor-free method separately. Recently, point-guided approach is introduced to anchor-base…