74 citations · 88 across the 11 of their papers we have counts for
12 papers · 1 filter
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…
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.…
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…
Frequency-Aware Deepfake Detection: Improving Generalizability through Frequency Space Learning
Chuangchuang Tan, Yao Zhao, Shikui Wei +3
This research addresses the challenge of developing a universal deepfake detector that can effectively identify unseen deepfake images despite limited training data. Existing frequ…
Data-Independent Operator: A Training-Free Artifact Representation Extractor for Generalizable Deepfake Detection
Chuangchuang Tan, Ping Liu, RenShuai Tao +4
Recently, the proliferation of increasingly realistic synthetic images generated by various generative adversarial networks has increased the risk of misuse. Consequently, there is…
Forgery-aware Adaptive Transformer for Generalizable Synthetic Image Detection
Huan Liu, Zichang Tan, Chuangchuang Tan +3
In this paper, we study the problem of generalizable synthetic image detection, aiming to detect forgery images from diverse generative methods, e.g., GANs and diffusion models. Cu…
Rethinking the Up-Sampling Operations in CNN-based Generative Network for Generalizable Deepfake Detection
Chuangchuang Tan, Huan Liu, Yao Zhao +4
Recently, the proliferation of highly realistic synthetic images, facilitated through a variety of GANs and Diffusions, has significantly heightened the susceptibility to misuse. W…
CLE Diffusion: Controllable Light Enhancement Diffusion Model
Yuyang Yin, Dejia Xu, Chuangchuang Tan +3
Low light enhancement has gained increasing importance with the rapid development of visual creation and editing. However, most existing enhancement algorithms are designed to homo…
LID 2020: The Learning from Imperfect Data Challenge Results
Yunchao Wei, Shuai Zheng, Ming-Ming Cheng +32
Learning from imperfect data becomes an issue in many industrial applications after the research community has made profound progress in supervised learning from perfectly annotate…