8 citations · 9 across the 8 of their papers we have counts for
6 papers · 1 filter
Efficient and Effective Model Extraction
Hongyu Zhu, Wentao Hu, Sichu Liang +3
Model extraction aims to create a functionally similar copy from a machine learning as a service (MLaaS) API with minimal overhead, typically for illicit profit or as a precursor t…
Reliable Model Watermarking: Defending Against Theft without Compromising on Evasion
Hongyu Zhu, Sichu Liang, Wentao Hu +3
With the rise of Machine Learning as a Service (MLaaS) platforms,safeguarding the intellectual property of deep learning models is becoming paramount. Among various protective meas…
Revisiting the Information Capacity of Neural Network Watermarks: Upper Bound Estimation and Beyond
Fangqi Li, Haodong Zhao, Wei Du +1
To trace the copyright of deep neural networks, an owner can embed its identity information into its model as a watermark. The capacity of the watermark quantify the maximal volume…
Solving the Capsulation Attack against Backdoor-based Deep Neural Network Watermarks by Reversing Triggers
Fangqi Li, Shilin Wang, Yun Zhu
Backdoor-based watermarking schemes were proposed to protect the intellectual property of artificial intelligence models, especially deep neural networks, under the black-box setti…
Knowledge-Free Black-Box Watermark and Ownership Proof for Image Classification Neural Networks
Fangqi Li, Shilin Wang
Watermarking has become a plausible candidate for ownership verification and intellectual property protection of deep neural networks. Regarding image classification neural network…
Secure Watermark for Deep Neural Networks with Multi-task Learning
Fangqi Li, Shilin Wang
Deep neural networks are playing an important role in many real-life applications. After being trained with abundant data and computing resources, a deep neural network model provi…