3 papers
cs.CV2026
REFORGE: Multi-modal Attacks Reveal Vulnerable Concept Unlearning in Image Generation Models
Yong Zou, Haoran Li, Fanxiao Li +5
Recent progress in image generation models (IGMs) enables high-fidelity content creation but also amplifies risks, including the reproduction of copyrighted content and the generat…
cs.LG2023
Model Inversion Attacks on Homogeneous and Heterogeneous Graph Neural Networks
Renyang Liu, Wei Zhou, Jinhong Zhang +3
Recently, Graph Neural Networks (GNNs), including Homogeneous Graph Neural Networks (HomoGNNs) and Heterogeneous Graph Neural Networks (HeteGNNs), have made remarkable progress in…
cs.CV2023
AFLOW: Developing Adversarial Examples under Extremely Noise-limited Settings
Renyang Liu, Jinhong Zhang, Haoran Li +3
Extensive studies have demonstrated that deep neural networks (DNNs) are vulnerable to adversarial attacks. Despite the significant progress in the attack success rate that has bee…