6 papers
Semantic Visual Anomaly Detection and Reasoning in AI-Generated Images
Chuangchuang Tan, Xiang Ming, Jinglu Wang +5
The rapid advancement of AI-generated content (AIGC) has enabled the synthesis of visually convincing images; however, many such outputs exhibit subtle \textbf{semantic anomalies},…
ForenX: Towards Explainable AI-Generated Image Detection with Multimodal Large Language Models
Chuangchuang Tan, Jinglu Wang, Xiang Ming +4
Advances in generative models have led to AI-generated images visually indistinguishable from authentic ones. Despite numerous studies on detecting AI-generated images with classif…
Pay Less Attention to Deceptive Artifacts: Robust Detection of Compressed Deepfakes on Online Social Networks
Manyi Li, Renshuai Tao, Yufan Liu +5
With the rapid advancement of deep learning, particularly through generative adversarial networks (GANs) and diffusion models (DMs), AI-generated images, or ``deepfakes", have beco…
DCI: Dual-Conditional Inversion for Boosting Diffusion-Based Image Editing
Zixiang Li, Haoyu Wang, Wei Wang +3
Diffusion models have achieved remarkable success in image generation and editing tasks. Inversion within these models aims to recover the latent noise representation for a real or…
C2P-CLIP: Injecting Category Common Prompt in CLIP to Enhance Generalization in Deepfake Detection
Chuangchuang Tan, Renshuai Tao, Huan Liu +4
This work focuses on AIGC detection to develop universal detectors capable of identifying various types of forgery images. Recent studies have found large pre-trained models, such…
ODDN: Addressing Unpaired Data Challenges in Open-World Deepfake Detection on Online Social Networks
Renshuai Tao, Manyi Le, Chuangchuang Tan +3
Despite significant advances in deepfake detection, handling varying image quality, especially due to different compressions on online social networks (OSNs), remains challenging.…