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20232026
most citedPatchCraft: Exploring Texture Patch for Efficient AI-generated Image Detection

6 citations · 8 across the 28 of their papers we have counts for

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15 papers · 1 filter

cs.CV2026

Only Train Once: Uncertainty-Aware One-Class Learning for Face Authenticity Detection

Qingchao Jiang, Zhenxuan Hou, Zhiying Zhu +3

The rapid evolution of generative paradigms has enabled the creation of highly realistic imagery, which escalates the risks of identity fraud and the dissemination of disinformatio…

cs.CV2026

Evidence-based Decision Modeling for Synthetic Face Detection with Uncertainty-driven Active Learning

Qingchao Jiang, Zhenxuan Hou, Zhiying Zhu +3

With the rapid development of deep generative models, forged facial images are massively exploited for illegal activities. Although existing synthetic face detection methods have a…

cs.CV2025

Self-Supervised AI-Generated Image Detection: A Camera Metadata Perspective

Nan Zhong, Mian Zou, Yiran Xu +4

The proliferation of AI-generated imagery poses escalating challenges for multimedia forensics, yet many existing detectors depend on assumptions about the internals of specific ge…

cs.CV2025

SimuFreeMark: A Noise-Simulation-Free Robust Watermarking Against Image Editing

Yichao Tang, Mingyang Li, Di Miao +3

The advancement of artificial intelligence generated content (AIGC) has created a pressing need for robust image watermarking that can withstand both conventional signal processing…

cs.CV2025

MoFRR: Mixture of Diffusion Models for Face Retouching Restoration

Jiaxin Liu, Qichao Ying, Zhenxing Qian +4

The widespread use of face retouching on social media platforms raises concerns about the authenticity of face images. While existing methods focus on detecting face retouching, ho…

cs.CV2025

Adversarial Shallow Watermarking

Guobiao Li, Lei Tan, Yuliang Xue +4

Recent advances in digital watermarking make use of deep neural networks for message embedding and extraction. They typically follow the ``encoder-noise layer-decoder''-based archi…

cs.CV2025

An Exceptional Dataset For Rare Pancreatic Tumor Segmentation

Wenqi Li, Yingli Chen, Keyang Zhou +6

Pancreatic NEuroendocrine Tumors (pNETs) are very rare endocrine neoplasms that account for less than 5% of all pancreatic malignancies, with an incidence of only 1-1.5 cases per 1…

cs.CV2024

Exploring Depth Information for Detecting Manipulated Face Videos

Haoyue Wang, Sheng Li, Ji He +3

Face manipulation detection has been receiving a lot of attention for the reliability and security of the face images/videos. Recent studies focus on using auxiliary information or…

cs.CV2024

ScreenMark: Watermarking Arbitrary Visual Content on Screen

Xiujian Liang, Gaozhi Liu, Yichao Si +2

Digital watermarking has shown its effectiveness in protecting multimedia content. However, existing watermarking is predominantly tailored for specific media types, rendering them…

cs.CV2024

Are handcrafted filters helpful for attributing AI-generated images?

Jialiang Li, Haoyue Wang, Sheng Li +3

Recently, a vast number of image generation models have been proposed, which raises concerns regarding the misuse of these artificial intelligence (AI) techniques for generating fa…

cs.CV2024

Regeneration Based Training-free Attribution of Fake Images Generated by Text-to-Image Generative Models

Meiling Li, Zhenxing Qian, Xinpeng Zhang

Text-to-image generative models have recently garnered significant attention due to their ability to generate images based on prompt descriptions. While these models have shown pro…

cs.CV2024★ 1 cited

PROMPT-IML: Image Manipulation Localization with Pre-trained Foundation Models Through Prompt Tuning

Xuntao Liu, Yuzhou Yang, Qichao Ying +3

Deceptive images can be shared in seconds with social networking services, posing substantial risks. Tampering traces, such as boundary artifacts and high-frequency information, ha…

cs.CV2024

From Covert Hiding to Visual Editing: Robust Generative Video Steganography

Xueying Mao, Xiaoxiao Hu, Wanli Peng +5

Traditional video steganography methods are based on modifying the covert space for embedding, whereas we propose an innovative approach that embeds secret message within semantic…

cs.CV2023★ 1 cited

Steal My Artworks for Fine-tuning? A Watermarking Framework for Detecting Art Theft Mimicry in Text-to-Image Models

Ge Luo, Junqiang Huang, Manman Zhang +3

The advancement in text-to-image models has led to astonishing artistic performances. However, several studios and websites illegally fine-tune these models using artists' artworks…

cs.CV2023★ 6 cited

PatchCraft: Exploring Texture Patch for Efficient AI-generated Image Detection

Nan Zhong, Yiran Xu, Sheng Li +2

Recent generative models show impressive performance in generating photographic images. Humans can hardly distinguish such incredibly realistic-looking AI-generated images from rea…