30 citations · 100 across the 18 of their papers we have counts for
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cs.CV2025
ViT-EnsembleAttack: Augmenting Ensemble Models for Stronger Adversarial Transferability in Vision Transformers
Hanwen Cao, Haobo Lu, Xiaosen Wang +1
Ensemble-based attacks have been proven to be effective in enhancing adversarial transferability by aggregating the outputs of models with various architectures. However, existing…
cs.CV2025
Disrupting Semantic and Abstract Features for Better Adversarial Transferability
Yuyang Luo, Xiaosen Wang, Zhijin Ge +1
Adversarial examples pose significant threats to deep neural networks (DNNs), and their property of transferability in the black-box setting has led to the emergence of transfer-ba…
cs.CV2025
Boosting the Local Invariance for Better Adversarial Transferability
Bohan Liu, Xiaosen Wang
Transfer-based attacks pose a significant threat to real-world applications by directly targeting victim models with adversarial examples generated on surrogate models. While numer…