263 citations · 921 across the 54 of their papers we have counts for
19 papers · 1 filter
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…
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…
Generative Model Watermarking Suppressing High-Frequency Artifacts
Li Zhang, Yong Liu, Xinpeng Zhang +1
Protecting deep neural networks (DNNs) against intellectual property (IP) infringement has attracted an increasing attention in recent years. Recent advances focus on IP protection…
Decision-based iterative fragile watermarking for model integrity verification
Zhaoxia Yin, Heng Yin, Hang Su +2
Typically, foundation models are hosted on cloud servers to meet the high demand for their services. However, this exposes them to security risks, as attackers can modify them afte…
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…
Hiding Images in Deep Probabilistic Models
Haoyu Chen, Linqi Song, Zhenxing Qian +2
Data hiding with deep neural networks (DNNs) has experienced impressive successes in recent years. A prevailing scheme is to train an autoencoder, consisting of an encoding network…
Reversible Data hiding in Encrypted Domain with Public Key Embedding Mechanism
Yan Ke, Minqing Zhang, Xinpeng Zhang +2
Considering the prospects of public key embedding (PKE) mechanism in active forensics on the integrity or identity of ciphertext for distributed deep learning security, two reversi…
Robust and Imperceptible Black-box DNN Watermarking Based on Fourier Perturbation Analysis and Frequency Sensitivity Clustering
Yong Liu, Hanzhou Wu, Xinpeng Zhang
Recently, more and more attention has been focused on the intellectual property protection of deep neural networks (DNNs), promoting DNN watermarking to become a hot research topic…
Deniable Steganography
Yong Xu, Zhihua Xia, Zichi Wang +2
Steganography conceals the secret message into the cover media, generating a stego media which can be transmitted on public channels without drawing suspicion. As its countermeasur…
Imperceptible Backdoor Attack: From Input Space to Feature Representation
Nan Zhong, Zhenxing Qian, Xinpeng Zhang
Backdoor attacks are rapidly emerging threats to deep neural networks (DNNs). In the backdoor attack scenario, attackers usually implant the backdoor into the target model by manip…
Semantic-Preserving Linguistic Steganography by Pivot Translation and Semantic-Aware Bins Coding
Tianyu Yang, Hanzhou Wu, Biao Yi +2
Linguistic steganography (LS) aims to embed secret information into a highly encoded text for covert communication. It can be roughly divided to two main categories, i.e., modifica…
Generating Watermarked Adversarial Texts
Mingjie Li, Hanzhou Wu, Xinpeng Zhang
Adversarial example generation has been a hot spot in recent years because it can cause deep neural networks (DNNs) to misclassify the generated adversarial examples, which reveals…
Structural Watermarking to Deep Neural Networks via Network Channel Pruning
Xiangyu Zhao, Yinzhe Yao, Hanzhou Wu +1
In order to protect the intellectual property (IP) of deep neural networks (DNNs), many existing DNN watermarking techniques either embed watermarks directly into the DNN parameter…
Hiding Data Hiding
Hanzhou Wu, Gen Liu, Xinpeng Zhang
Data hiding is the art of hiding secret data into a cover object such as digital image for covert communication. In this paper, we make the first step towards hiding ``data hiding'…
Watermarking Graph Neural Networks by Random Graphs
Xiangyu Zhao, Hanzhou Wu, Xinpeng Zhang
Many learning tasks require us to deal with graph data which contains rich relational information among elements, leading increasing graph neural network (GNN) models to be deploye…
A Reversible Data hiding Scheme in Encrypted Domain for Secret Image Sharing based on Chinese Remainder Theorem
Yan Ke, Minqing Zhang, Xinpeng Zhang +3
Reversible data hiding in encrypted domain (RDH-ED) schemes based on symmetric or public key encryption are mainly applied to the security of end-to-end communication. Aimed at pro…
Computing in Covert Domain Using Data Hiding
Zhenxing Qian, Zichi Wang, Xinpeng Zhang
This paper proposes an idea of data computing in the covert domain (DCCD). We show that with information hiding some data computing tasks can be executed beneath the covers like im…
Lost in the Digital Wild: Hiding Information in Digital Activities
Shujun Li, Anthony T. S. Ho, Zichi Wang +1
This paper presents a new general framework of information hiding, in which the hidden information is embedded into a collection of activities conducted by selected human and compu…
Data hiding in Fingerprint Minutiae Template for Privacy Protection
Sheng Li, Xin Chen, Zhigao Zheng +3
In this paper, we propose a novel scheme for data hiding in the fingerprint minutiae template, which is the most popular in fingerprint recognition systems. Various strategies are…