activity
20112026
most citedPoisoning Attacks to Graph-Based Recommender Systems

217 citations · 1.8k across the 154 of their papers we have counts for

collaborators
Showing 2024Show all

29 papers · 1 filter

cs.CR2024★ 1 cited

AI-generated Image Detection: Passive or Watermark?

Moyang Guo, Yuepeng Hu, Zhengyuan Jiang +4

While text-to-image models offer numerous benefits, they also pose significant societal risks. Detecting AI-generated images is crucial for mitigating these risks. Detection method…

cs.CV2024

Automatically Generating Visual Hallucination Test Cases for Multimodal Large Language Models

Zhongye Liu, Hongbin Liu, Yuepeng Hu +2

Visual hallucination (VH) occurs when a multimodal large language model (MLLM) generates responses with incorrect visual details for prompts. Existing methods for generating VH tes…

cs.CR2024★ 3 cited

Enhancing Prompt Injection Attacks to LLMs via Poisoning Alignment

Zedian Shao, Hongbin Liu, Jaden Mu +1

Prompt injection attack, where an attacker injects a prompt into the original one, aiming to make an Large Language Model (LLM) follow the injected prompt to perform an attacker-ch…

cs.CL2024

StringLLM: Understanding the String Processing Capability of Large Language Models

Xilong Wang, Hao Fu, Jindong Wang +1

String processing, which mainly involves the analysis and manipulation of strings, is a fundamental component of modern computing. Despite the significant advancements of large lan…

cs.CR2024

Evaluating LLM-based Personal Information Extraction and Countermeasures

Yupei Liu, Yuqi Jia, Jinyuan Jia +1

Automatically extracting personal information -- such as name, phone number, and email address -- from publicly available profiles at a large scale is a stepstone to many other sec…

cs.CR2024★ 4 cited

Byzantine-Robust Decentralized Federated Learning

Minghong Fang, Zifan Zhang, Hairi +5

Federated learning (FL) enables multiple clients to collaboratively train machine learning models without revealing their private training data. In conventional FL, the system foll…