2 citations · 6 across the 10 of their papers we have counts for
10 papers
MetaSeal: Defending Against Image Attribution Forgery Through Content-Dependent Cryptographic Watermarks
Tong Zhou, Ruyi Ding, Gaowen Liu +5
The rapid growth of digital and AI-generated images has amplified the need for secure and verifiable methods of image attribution. While digital watermarking offers more robust pro…
ProDiF: Protecting Domain-Invariant Features to Secure Pre-Trained Models Against Extraction
Tong Zhou, Shijin Duan, Gaowen Liu +4
Pre-trained models are valuable intellectual property, capturing both domain-specific and domain-invariant features within their weight spaces. However, model extraction attacks th…
Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing
Ruyi Ding, Tong Zhou, Lili Su +3
Adapting pre-trained deep learning models to customized tasks has become a popular choice for developers to cope with limited computational resources and data volume. More specific…
AdaPI: Facilitating DNN Model Adaptivity for Efficient Private Inference in Edge Computing
Tong Zhou, Jiahui Zhao, Yukui Luo +4
Private inference (PI) has emerged as a promising solution to execute computations on encrypted data, safeguarding user privacy and model parameters in edge computing. However, exi…
Bileve: Securing Text Provenance in Large Language Models Against Spoofing with Bi-level Signature
Tong Zhou, Xuandong Zhao, Xiaolin Xu +1
Text watermarks for large language models (LLMs) have been commonly used to identify the origins of machine-generated content, which is promising for assessing liability when comba…
TBNet: A Neural Architectural Defense Framework Facilitating DNN Model Protection in Trusted Execution Environments
Ziyu Liu, Tong Zhou, Yukui Luo +1
Trusted Execution Environments (TEEs) have become a promising solution to secure DNN models on edge devices. However, the existing solutions either provide inadequate protection or…
MirrorNet: A TEE-Friendly Framework for Secure On-device DNN Inference
Ziyu Liu, Yukui Luo, Shijin Duan +2
Deep neural network (DNN) models have become prevalent in edge devices for real-time inference. However, they are vulnerable to model extraction attacks and require protection. Exi…
AutoReP: Automatic ReLU Replacement for Fast Private Network Inference
Hongwu Peng, Shaoyi Huang, Tong Zhou +11
The growth of the Machine-Learning-As-A-Service (MLaaS) market has highlighted clients' data privacy and security issues. Private inference (PI) techniques using cryptographic prim…
NNSplitter: An Active Defense Solution for DNN Model via Automated Weight Obfuscation
Tong Zhou, Yukui Luo, Shaolei Ren +1
As a type of valuable intellectual property (IP), deep neural network (DNN) models have been protected by techniques like watermarking. However, such passive model protection canno…
ObfuNAS: A Neural Architecture Search-based DNN Obfuscation Approach
Tong Zhou, Shaolei Ren, Xiaolin Xu
Malicious architecture extraction has been emerging as a crucial concern for deep neural network (DNN) security. As a defense, architecture obfuscation is proposed to remap the vic…