11 papers
When Detectors Forget Forensics: Blocking Semantic Shortcuts for Generalizable AI-Generated Image Detection
Chao Shuai, Shaojing Fan, Chenlin Zou +6
The growing realism of generative models has blurred the boundary between real and synthetic content, posing significant challenges to reliable AI-generated image detection. Althou…
Breaking the Generator Barrier: Disentangled Representation for Generalizable AI-Text Detection
Xiao Pu, Zepeng Cheng, Lin Yuan +2
As large language models (LLMs) generate text that increasingly resembles human writing, the subtle cues that distinguish AI-generated content from human-written content become inc…
Combating Pattern and Content Bias: Adversarial Feature Learning for Generalized AI-Generated Image Detection
Haifeng Zhang, Qinghui He, Xiuli Bi +3
In recent years, the rapid development of generative artificial intelligence technology has significantly lowered the barrier to creating high-quality fake images, posing a serious…
Leveraging Arbitrary Data Sources for AI-Generated Image Detection Without Sacrificing Generalization
Qinghui He, Haifeng Zhang, Xiuli Bi +3
The accelerating advancement of generative models has introduced new challenges for detecting AI-generated images, especially in real-world scenarios where novel generation techniq…
Select, Hypothesize and Verify: Towards Verified Neuron Concept Interpretation
ZeBin Ji, Yang Hu, Xiuli Bi +2
It is essential for understanding neural network decisions to interpret the functionality (also known as concepts) of neurons. Existing approaches describe neuron concepts by gener…
SSR: Semantic and Spatial Rectification for CLIP-based Weakly Supervised Segmentation
Xiuli Bi, Die Xiao, Junchao Fan +1
In recent years, Contrastive Language-Image Pretraining (CLIP) has been widely applied to Weakly Supervised Semantic Segmentation (WSSS) tasks due to its powerful cross-modal seman…