67 citations · 171 across the 50 of their papers we have counts for
14 papers · 1 filter
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…
Federated Learning with Label-Masking Distillation
Jianghu Lu, Shikun Li, Kexin Bao +3
Federated learning provides a privacy-preserving manner to collaboratively train models on data distributed over multiple local clients via the coordination of a global server. In…
Privacy-Preserving Student Learning with Differentially Private Data-Free Distillation
Bochao Liu, Jianghu Lu, Pengju Wang +4
Deep learning models can achieve high inference accuracy by extracting rich knowledge from massive well-annotated data, but may pose the risk of data privacy leakage in practical d…
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…
Revocable Backdoor for Deep Model Trading
Yiran Xu, Nan Zhong, Zhenxing Qian +1
Deep models are being applied in numerous fields and have become a new important digital product. Meanwhile, previous studies have shown that deep models are vulnerable to backdoor…
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…
Cover-separable Fixed Neural Network Steganography via Deep Generative Models
Guobiao Li, Sheng Li, Zhenxing Qian +1
Image steganography is the process of hiding secret data in a cover image by subtle perturbation. Recent studies show that it is feasible to use a fixed neural network for data emb…
Search, Examine and Early-Termination: Fake News Detection with Annotation-Free Evidences
Yuzhou Yang, Yangming Zhou, Qichao Ying +2
Pioneer researches recognize evidences as crucial elements in fake news detection apart from patterns. Existing evidence-aware methods either require laborious pre-processing proce…
Online Prompt Pricing based on Combinatorial Multi-Armed Bandit and Hierarchical Stackelberg Game
Meiling Li, Hongrun Ren, Haixu Xiong +2
Generation models have shown promising performance in various tasks, making trading around machine learning models possible. In this paper, we aim at a novel prompt trading scenari…
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…
Purified and Unified Steganographic Network
Guobiao Li, Sheng Li, Zicong Luo +2
Steganography is the art of hiding secret data into the cover media for covert communication. In recent years, more and more deep neural network (DNN)-based steganographic schemes…
Object-oriented backdoor attack against image captioning
Meiling Li, Nan Zhong, Xinpeng Zhang +2
Backdoor attack against image classification task has been widely studied and proven to be successful, while there exist little research on the backdoor attack against vision-langu…
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…
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…