4 citations · 15 across the 8 of their papers we have counts for
8 papers
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…
Unified Frequency-Assisted Transformer Framework for Detecting and Grounding Multi-Modal Manipulation
Huan Liu, Zichang Tan, Qiang Chen +3
Detecting and grounding multi-modal media manipulation (DGM^4) has become increasingly crucial due to the widespread dissemination of face forgery and text misinformation. In this…
Group Pose: A Simple Baseline for End-to-End Multi-person Pose Estimation
Huan Liu, Qiang Chen, Zichang Tan +9
In this paper, we study the problem of end-to-end multi-person pose estimation. State-of-the-art solutions adopt the DETR-like framework, and mainly develop the complex decoder, e.…