4 citations · 6 across the 13 of their papers we have counts for
13 papers
CoCoDiff: Correspondence-Consistent Diffusion Model for Fine-grained Style Transfer
Wenbo Nie, Zixiang Li, Renshuai Tao +3
Transferring visual style between images while preserving semantic correspondence between similar objects remains a central challenge in computer vision. While existing methods hav…
Taming Generative Synthetic Data for X-ray Prohibited Item Detection
Jialong Sun, Hongguang Zhu, Weizhe Liu +3
Training prohibited item detection models requires a large amount of X-ray security images, but collecting and annotating these images is time-consuming and laborious. To address d…
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…
Unsupervised Region-Based Image Editing of Denoising Diffusion Models
Zixiang Li, Yue Song, Renshuai Tao +3
Although diffusion models have achieved remarkable success in the field of image generation, their latent space remains under-explored. Current methods for identifying semantics wi…
Behavior Backdoor for Deep Learning Models
Jiakai Wang, Pengfei Zhang, Renshuai Tao +5
The various post-processing methods for deep-learning-based models, such as quantification, pruning, and fine-tuning, play an increasingly important role in artificial intelligence…
BGM: Background Mixup for X-ray Prohibited Items Detection
Weizhe Liu, Renshuai Tao, Hongguang Zhu +3
Current data-driven approaches for X-ray prohibited items detection remain under-explored, particularly in the design of effective data augmentations. Existing natural image augmen…
Dual-view X-ray Detection: Can AI Detect Prohibited Items from Dual-view X-ray Images like Humans?
Renshuai Tao, Haoyu Wang, Yuzhe Guo +5
To detect prohibited items in challenging categories, human inspectors typically rely on images from two distinct views (vertical and side). Can AI detect prohibited items from dua…
PAD-F: Prior-Aware Debiasing Framework for Long-Tailed X-ray Prohibited Item Detection
Haoyu Wang, Renshuai Tao, Wei Wang +1
Detecting prohibited items in X-ray security imagery is a challenging yet crucial task. With the rapid advancement of deep learning, object detection algorithms have been widely ap…
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…
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…