41 citations · 112 across the 26 of their papers we have counts for
15 papers · 1 filter
Decision Uncertainty-Oriented Watermarking (DUOW): Securing Multi-Agent Routing Against Policy Cloning
Ziming Zhang, Li Li, Guorui Feng +3
Routing policies in Multi-Agent Systems (MAS) represent valuable intellectual property, yet their exposure through execution logs creates significant risks of unauthorized policy c…
Trigger Where It Hurts: Unveiling Hidden Backdoors through Sensitivity with Sensitron
Gejian Zhao, Hanzhou Wu, Xinpeng Zhang
Backdoor attacks pose a significant security threat to natural language processing (NLP) systems, but existing methods lack explainable trigger mechanisms and fail to quantitativel…
ShadowCoT: Cognitive Hijacking for Stealthy Reasoning Backdoors in LLMs
Gejian Zhao, Hanzhou Wu, Xinpeng Zhang +1
Chain-of-Thought (CoT) enhances an LLM's ability to perform complex reasoning tasks, but it also introduces new security issues. In this work, we present ShadowCoT, a novel backdoo…
Transferable Watermarking to Self-supervised Pre-trained Graph Encoders by Trigger Embeddings
Xiangyu Zhao, Hanzhou Wu, Xinpeng Zhang
Recent years have witnessed the prosperous development of Graph Self-supervised Learning (GSSL), which enables to pre-train transferable foundation graph encoders. However, the eas…
StegGuard: Fingerprinting Self-supervised Pre-trained Encoders via Secrets Embeder and Extractor
Xingdong Ren, Tianxing Zhang, Hanzhou Wu +3
In this work, we propose StegGuard, a novel fingerprinting mechanism to verify the ownership of the suspect pre-trained encoder using steganography. A critical perspective in StegG…
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…
Robust and Lossless Fingerprinting of Deep Neural Networks via Pooled Membership Inference
Hanzhou Wu
Deep neural networks (DNNs) have already achieved great success in a lot of application areas and brought profound changes to our society. However, it also raises new security prob…
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…
Verifying Integrity of Deep Ensemble Models by Lossless Black-box Watermarking with Sensitive Samples
Lina Lin, Hanzhou Wu
With the widespread use of deep neural networks (DNNs) in many areas, more and more studies focus on protecting DNN models from intellectual property (IP) infringement. Many existi…
Autoregressive Linguistic Steganography Based on BERT and Consistency Coding
Xiaoyan Zheng, Hanzhou Wu
Linguistic steganography (LS) conceals the presence of communication by embedding secret information into a text. How to generate a high-quality text carrying secret information is…
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…