2 citations · 2 across the 6 of their papers we have counts for
5 papers · 1 filter
Diversifying Counterattacks: Orthogonal Exploration for Robust CLIP Inference
Chengze Jiang, Minjing Dong, Xinli Shi +1
Vision-language pre-training models (VLPs) demonstrate strong multimodal understanding and zero-shot generalization, yet remain vulnerable to adversarial examples, raising concerns…
Revisiting Adversarial Training under Hyperspectral Image
Weihua Zhang, Chengze Jiang, Minjing Dong +5
Recent studies have shown that deep learning-based hyperspectral image (HSI) classification models are highly vulnerable to adversarial attacks, posing significant security risks.…
Survey of Adversarial Robustness in Multimodal Large Language Models
Chengze Jiang, Zhuangzhuang Wang, Minjing Dong +1
Multimodal Large Language Models (MLLMs) have demonstrated exceptional performance in artificial intelligence by facilitating integrated understanding across diverse modalities, in…
Improving Fast Adversarial Training via Self-Knowledge Guidance
Chengze Jiang, Junkai Wang, Minjing Dong +5
Adversarial training has achieved remarkable advancements in defending against adversarial attacks. Among them, fast adversarial training (FAT) is gaining attention for its ability…
Improving Fast Adversarial Training Paradigm: An Example Taxonomy Perspective
Jie Gui, Chengze Jiang, Minjing Dong +4
While adversarial training is an effective defense method against adversarial attacks, it notably increases the training cost. To this end, fast adversarial training (FAT) is prese…