Author

Junhao Dong

Nanyang Technological University

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Oct 2026

Flexible Targeted Adversarial Alignment with Frequency-based Visual Focus for Attacking Large Vision-Language Models.

Although Large Vision-Language Models (LVLMs) have shown great capabilities across various downstream tasks, they are proven to be vulnerable to adversarial examples, thus resulting in critical safety issues. Existing adversarial attacks against LVLMs predominantly rely on uniform, image-wise perturbations, while large...

Dai-Zong Liu, Tian-Yao Luo, Junhao Dong et al. · IEEE Transactions on Pattern... · 0 citations

Preprint Aug 2026

EvTrajGS: Accurate and Efficient 3D Gaussian Splatting from Unposed Event Streams

This paper presents EvTrajGS, an accurate and efficient 3D Gaussian Splatting framework for unposed event streams that enables reliable joint pose-scene optimization initialized from coarse pose priors, eliminating the need for computationally expensive SLAM-style pipelines.

Zixuan Chen, Jiakai Zhang, Junhao Dong et al. · 0 citations

Sep 2026

Forbid Your Attention: Fooling Multimodal Large Language Models by Selectively Removing Intrinsic Focus in Spectral Domain

Multimodal large language models (MLLMs) have extended the capability of large language models (LLMs) to process more contextual multimodal information, showing remarkable progress in diverse realistic multimodal applications. Despite their strong perception and reasoning abilities, recent studies reveal that MLLMs rem...

Dai-Zong Liu, Junhao Dong, Zhi-Yuan Ma et al. · IEEE transactions on multime... · 0 citations

Review Aug 2026

Adversarial Attacks for Good: A Survey of Proactive Protection across the Visual Content Lifecycle

This survey examines the protective paradigm that has grown around this intervention point, and finds that most protections are still validated mainly against static or weakly adaptive adversaries, while evidence beyond controlled benchmarks remains scarce.

Jiaming Zhang, Bo-Yang Chen, Zhe-Rui Li et al. · 0 citations

Jul 2026

Unifying Adversarially Robust Model Experts in Vision-Language Models

A collaborative adversarial fine-tuning framework that maintains multiple experts during training, enables knowledge exchange through embedding-space harmonization, and consolidates the learned knowledge into a single unified robust model.

Nguyen Duc Thai, Junhao Dong, Sua Qi Rong et al. · arXiv.org · 0 citations

Conference Open access Sep 2026

Understanding and Exploiting Phase Sensitivity for Attacking Large Vision–Language Models

This paper proposes a novel LVLM attack method, called BadPhase with further backdoor designs, to implant adversarial phase as triggers into any image inputs via data poisoning so as to control the LVLMs’ predictions and finds that LVLMs are sensitive to the phase-aware image structure.

Dai-Zong Liu, Junhao Dong, Xiang Fang et al. · Proceedings of the Thirty-Fi... · 0 citations