5 accepted papers
As Visual Language Models (VLMs) continue to evolve, they have demonstrated increasingly sophisticated logical reasoning capabilities and multimodal thought generation, opening doors to widespread applications. However, this advancement raises serious concerns about content security, particularly wh…
Although previous studies have proposed backdoor attacks in Federated Learning (FL), the introduced triggers in these works are easily detectable by human eyes. Besides, this paper also shows that traditional invisible centralized backdoor attacks struggle to work well in FL scenarios. To address th…
Despite the tremendous success of deep neural networks (DNNs) across various fields, their susceptibility to potential backdoor attacks seriously threatens their application security, particularly in safety-critical or security-sensitive ones. Given this growing threat, there is a pressing need for…
Network pruning has been extensively studied in model compression to reduce neural networks’ memory, latency, and computation cost. However, the pruned networks still suffer from the threat posed by adversarial examples, limiting the broader application of the pruned networks in safety-critical appl…
Previous researches have demonstrated deep learning models' vulnerabilities to decision-based adversarial attacks, which craft adversarial examples based solely on information from output decisions (top-1 labels). However, existing decision-based attacks have two major limitations, i.e., expensive q…