2 papers
cs.CV2026
PPU-Bench:Real World Benchmark for Personalized Partial Unlearning in Vision Language Models
Jiahui Guang, Zexun Zhan, Zhenlin Xu +5
Multimodal Large Language Models (MLLMs) may memorize sensitive cross-modal information during pretraining. However, existing MLLM unlearning benchmarks rely on synthetic knowledge…
cs.CV2023
FAT: Feature-Focusing Adversarial Training via Disentanglement of Natural and Perturbed Patterns
Yaguan Qian, Chenyu Zhao, Zhaoquan Gu +5
Deep neural networks (DNNs) are vulnerable to adversarial examples crafted by well-designed perturbations. This could lead to disastrous results on critical applications such as se…