67 citations · 171 across the 50 of their papers we have counts for
15 papers · 1 filter
Model Copyright Protection in Buyer-seller Environment
Yusheng Guo, Nan Zhong, Zhenxing Qian +1
Training a deep neural network (DNN) requires a high computational cost. Buying models from sellers with a large number of computing resources has become prevailing. However, the b…
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…
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…
Securing Fixed Neural Network Steganography
Zicong Luo, Sheng Li, Guobiao Li +2
Image steganography is the art of concealing secret information in images in a way that is imperceptible to unauthorized parties. Recent advances show that is possible to use a fix…
Physical Invisible Backdoor Based on Camera Imaging
Yusheng Guo, Nan Zhong, Zhenxing Qian +1
Backdoor attack aims to compromise a model, which returns an adversary-wanted output when a specific trigger pattern appears yet behaves normally for clean inputs. Current backdoor…
DRAW: Defending Camera-shooted RAW against Image Manipulation
Xiaoxiao Hu, Qichao Ying, Zhenxing Qian +2
RAW files are the initial measurement of scene radiance widely used in most cameras, and the ubiquitously-used RGB images are converted from RAW data through Image Signal Processin…
RetouchingFFHQ: A Large-scale Dataset for Fine-grained Face Retouching Detection
Qichao Ying, Jiaxin Liu, Sheng Li +3
The widespread use of face retouching filters on short-video platforms has raised concerns about the authenticity of digital appearances and the impact of deceptive advertising. To…
Towards Deep Network Steganography: From Networks to Networks
Guobiao Li, Sheng Li, Meiling Li +2
With the widespread applications of the deep neural network (DNN), how to covertly transmit the DNN models in public channels brings us the attention, especially for those trained…
StyleStegan: Leak-free Style Transfer Based on Feature Steganography
Xiujian Liang, Bingshan Liu, Qichao Ying +2
In modern social networks, existing style transfer methods suffer from a serious content leakage issue, which hampers the ability to achieve serial and reversible stylization, ther…
Generative Steganographic Flow
Ping Wei, Ge Luo, Qi Song +3
Generative steganography (GS) is a new data hiding manner, featuring direct generation of stego media from secret data. Existing GS methods are generally criticized for their poor…
Generative Steganography Diffusion
Ping Wei, Qing Zhou, Zichi Wang +3
Generative steganography (GS) is an emerging technique that generates stego images directly from secret data. Various GS methods based on GANs or Flow have been developed recently.…
Multi-modal Fake News Detection on Social Media via Multi-grained Information Fusion
Yangming Zhou, Yuzhou Yang, Qichao Ying +2
The easy sharing of multimedia content on social media has caused a rapid dissemination of fake news, which threatens society's stability and security. Therefore, fake news detecti…
Steganography of Steganographic Networks
Guobiao Li, Sheng Li, Meiling Li +2
Steganography is a technique for covert communication between two parties. With the rapid development of deep neural networks (DNN), more and more steganographic networks are propo…
Improved CNN Prediction Based Reversible Data Hiding
Yingqiang Qiu, Wanli Peng, Xiaodan Lin +2
This letter proposes an improved CNN predictor (ICNNP) for reversible data hiding (RDH) in images, which consists of a feature extraction module, a pixel prediction module, and a c…
Trojaning semi-supervised learning model via poisoning wild images on the web
Le Feng, Zhenxing Qian, Sheng Li +1
Wild images on the web are vulnerable to backdoor (also called trojan) poisoning, causing machine learning models learned on these images to be injected with backdoors. Most previo…