ResearchTrend.AI
EVADE: Multimodal Benchmark for Evasive Content Detection in E-Commerce Applications
v1v2 (latest)

EVADE: Multimodal Benchmark for Evasive Content Detection in E-Commerce Applications

Ancheng Xu
Zhihao Yang
Junlin Li
Guanghu Yuan
Longze Chen
Liang Yan
Jiehui Zhou
Zhen Qin
Hengyun Chang
Hamid Alinejad-Rokny
Bo Zheng
Min Yang
    AAML
ArXiv (abs)PDFHTML

Papers citing "EVADE: Multimodal Benchmark for Evasive Content Detection in E-Commerce Applications"

5 / 5 papers shown
Title
Bypassing Prompt Injection and Jailbreak Detection in LLM Guardrails
Bypassing Prompt Injection and Jailbreak Detection in LLM Guardrails
William Hackett
Lewis Birch
Stefan Trawicki
N. Suri
Peter Garraghan
66
4
0
15 Apr 2025
InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models
InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models
Jinguo Zhu
Weiyun Wang
Zhe Chen
Ziwei Liu
Shenglong Ye
...
Dahua Lin
Yu Qiao
Jifeng Dai
Wenhai Wang
Wei Wang
MLLMVLM
219
132
1
14 Apr 2025
Data Augmentation for Fake Reviews Detection in Multiple Languages and Multiple Domains
Data Augmentation for Fake Reviews Detection in Multiple Languages and Multiple Domains
Ming Liu
Massimo Poesio
86
1
0
09 Apr 2025
A Comprehensive Survey of Large Language Models and Multimodal Large Language Models in Medicine
A Comprehensive Survey of Large Language Models and Multimodal Large Language Models in Medicine
Hanguang Xiao
Feizhong Zhou
Xianglong Liu
Tianqi Liu
Zhipeng Li
Xin Liu
Xiaoxuan Huang
AILawLM&MALRM
137
30
0
31 Dec 2024
Hallucination of Multimodal Large Language Models: A Survey
Hallucination of Multimodal Large Language Models: A Survey
Zechen Bai
Pichao Wang
Tianjun Xiao
Tong He
Zongbo Han
Zheng Zhang
Mike Zheng Shou
VLMLRM
245
197
0
29 Apr 2024

We use cookies and other tracking technologies to improve your browsing experience on our website, to show you personalized content and targeted ads, to analyze our website traffic, and to understand where our visitors are coming from. See our policy.