1 citations · 1 across the 2 of their papers we have counts for
7 papers
VTONGuard: Automatic Detection and Authentication of AI-Generated Virtual Try-On Content
Shengyi Wu, Yan Hong, Shengyao Chen +5
With the rapid advancement of generative AI, virtual try-on (VTON) systems are becoming increasingly common in e-commerce and digital entertainment. However, the growing realism of…
GAMMA: Generalizable Alignment via Multi-task and Manipulation-Augmented Training for AI-Generated Image Detection
Haozhen Yan, Yan Hong, Suning Lang +6
With generative models becoming increasingly sophisticated and diverse, detecting AI-generated images has become increasingly challenging. While existing AI-genereted Image detecto…
FW-VTON: Flattening-and-Warping for Person-to-Person Virtual Try-on
Zheng Wang, Xianbing Sun, Shengyi Wu +5
Traditional virtual try-on methods primarily focus on the garment-to-person try-on task, which requires flat garment representations. In contrast, this paper introduces a novel app…
DS-VTON: An Enhanced Dual-Scale Coarse-to-Fine Framework for Virtual Try-On
Xianbing Sun, Yan Hong, Jiahui Zhan +5
Despite recent progress, most existing virtual try-on methods still struggle to simultaneously address two core challenges: accurately aligning the garment image with the target hu…
Robustness in AI-Generated Detection: Enhancing Resistance to Adversarial Attacks
Sun Haoxuan, Hong Yan, Zhan Jiahui +6
The rapid advancement of generative image technology has introduced significant security concerns, particularly in the domain of face generation detection. This paper investigates…
Towards Explainable Fake Image Detection with Multi-Modal Large Language Models
Yikun Ji, Yan Hong, Jiahui Zhan +6
Progress in image generation raises significant public security concerns. We argue that fake image detection should not operate as a "black box". Instead, an ideal approach must en…