3 citations · 5 across the 6 of their papers we have counts for
5 papers · 1 filter
Locate-Then-Examine: Grounded Region Reasoning Improves Detection of AI-Generated Images
Yikun Ji, Yan Hong, Bowen Deng +5
The rapid growth of AI-generated imagery has blurred the boundary between real and synthetic content, raising practical concerns for digital integrity. Vision-language models (VLMs…
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…
Interpretable and Reliable Detection of AI-Generated Images via Grounded Reasoning in MLLMs
Yikun Ji, Hong Yan, Jun Lan +5
The rapid advancement of image generation technologies intensifies the demand for interpretable and robust detection methods. Although existing approaches often attain high accurac…
COCO-Inpaint: A Benchmark for Detecting and Localizing Inpainting-Based Image Manipulations
Haozhen Yan, Yan Hong, Jiahui Zhan +5
Recent advances in image manipulation have enabled highly photorealistic content generation, but also lowered the barrier to arbitrary editing, raising concerns about multimedia au…
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…