Endong Tong — accepted AI-conference papers

2026

FRBAT: Conditionally-Visible Physical Backdoor Attack via Fluorescence

Yalun Wu, Liu Liu, Endong Tong, Yingxiao Xiang, Xiaoting Lyu, Zhen Han +1

AAAI 2026technical

Deep neural networks are increasingly vulnerable to physically deployable backdoor attacks, which manipulate real-world objects to induce targeted model failures. However, current physical backdoor attacks predominantly rely on perpetually visible triggers appended to target objects. These methods i

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2023

Robust Reinforcement Learning via Progressive Task Sequence

Yike Li, Yunzhe Tian, Endong Tong, Wenjia Niu, Jiqiang Liu

IJCAI 2023poster

Robust reinforcement learning (RL) has been a challenging problem due to the gap between simulation and the real world. Existing efforts typically address the robust RL problem by solving a max-min problem. The main idea is to maximize the cumulative reward under the worst-possible perturbations. Ho…

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