5 papers
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…
EIRES:Training-free AI-Generated Image Detection via Edit-Induced Reconstruction Error Shift
Wan Jiang, Jing Yan, Xiaojing Chen +4
Diffusion models have recently achieved remarkable photorealism, making it increasingly difficult to distinguish real images from generated ones, raising significant privacy and se…
Adversarially Robust AI-Generated Image Detection for Free: An Information Theoretic Perspective
Ruixuan Zhang, He Wang, Zhengyu Zhao +4
Rapid advances in Artificial Intelligence Generated Images (AIGI) have facilitated malicious use, such as forgery and misinformation. Therefore, numerous methods have been proposed…
TASAR: Transfer-based Attack on Skeletal Action Recognition
Yunfeng Diao, Baiqi Wu, Ruixuan Zhang +5
Skeletal sequence data, as a widely employed representation of human actions, are crucial in Human Activity Recognition (HAR). Recently, adversarial attacks have been proposed in t…
Boosting Adversarial Transferability for Skeleton-based Action Recognition via Exploring the Model Posterior Space
Yunfeng Diao, Baiqi Wu, Ruixuan Zhang +3
Skeletal motion plays a pivotal role in human activity recognition (HAR). Recently, attack methods have been proposed to identify the universal vulnerability of skeleton-based HAR(…