4 papers
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…
Devling into Adversarial Transferability on Image Classification: Review, Benchmark, and Evaluation
Xiaosen Wang, Zhijin Ge, Bohan Liu +5
Adversarial transferability refers to the capacity of adversarial examples generated on the surrogate model to deceive alternate, unexposed victim models. This property eliminates…
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…
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…