24 citations · 59 across the 12 of their papers we have counts for
12 papers
Differentially Private Natural Gradient Descent
Pan Li, Kai Chen, Shuai Chang +3
Under a fixed privacy budget, the utility of differentially private (DP) training is ultimately determined by its optimization efficiency. Standard first-order DP optimizers such a…
MEA-Defender: A Robust Watermark against Model Extraction Attack
Peizhuo Lv, Hualong Ma, Kai Chen +6
Recently, numerous highly-valuable Deep Neural Networks (DNNs) have been trained using deep learning algorithms. To protect the Intellectual Property (IP) of the original owners ov…
A Model Stealing Attack Against Multi-Exit Networks
Li Pan, Lv Peizhuo, Chen Kai +3
Compared to traditional neural networks with a single output channel, a multi-exit network has multiple exits that allow for early outputs from the model's intermediate layers, thu…
Boosting Neural Networks to Decompile Optimized Binaries
Ying Cao, Ruigang Liang, Kai Chen +1
Decompilation aims to transform a low-level program language (LPL) (eg., binary file) into its functionally-equivalent high-level program language (HPL) (e.g., C/C++). It is a core…
A Novel Membership Inference Attack against Dynamic Neural Networks by Utilizing Policy Networks Information
Pan Li, Peizhuo Lv, Shenchen Zhu +2
Unlike traditional static deep neural networks (DNNs), dynamic neural networks (NNs) adjust their structures or parameters to different inputs to guarantee accuracy and computation…
SSL-WM: A Black-Box Watermarking Approach for Encoders Pre-trained by Self-supervised Learning
Peizhuo Lv, Pan Li, Shenchen Zhu +9
Recent years have witnessed tremendous success in Self-Supervised Learning (SSL), which has been widely utilized to facilitate various downstream tasks in Computer Vision (CV) and…
Invisible Backdoor Attacks Using Data Poisoning in the Frequency Domain
Chang Yue, Peizhuo Lv, Ruigang Liang +1
With the broad application of deep neural networks (DNNs), backdoor attacks have gradually attracted attention. Backdoor attacks are insidious, and poisoned models perform well on…
Semantics-Recovering Decompilation through Neural Machine Translation
Ruigang Liang, Ying Cao, Peiwei Hu +2
Decompilation transforms low-level program languages (PL) (e.g., binary code) into high-level PLs (e.g., C/C++). It has been widely used when analysts perform security analysis on…
DBIA: Data-free Backdoor Injection Attack against Transformer Networks
Peizhuo Lv, Hualong Ma, Jiachen Zhou +4
Recently, transformer architecture has demonstrated its significance in both Natural Language Processing (NLP) and Computer Vision (CV) tasks. Though other network models are known…
HufuNet: Embedding the Left Piece as Watermark and Keeping the Right Piece for Ownership Verification in Deep Neural Networks
Peizhuo Lv, Pan Li, Shengzhi Zhang +4
Due to the wide use of highly-valuable and large-scale deep neural networks (DNNs), it becomes crucial to protect the intellectual property of DNNs so that the ownership of dispute…
SoK: A Modularized Approach to Study the Security of Automatic Speech Recognition Systems
Yuxuan Chen, Jiangshan Zhang, Xuejing Yuan +4
With the wide use of Automatic Speech Recognition (ASR) in applications such as human machine interaction, simultaneous interpretation, audio transcription, etc., its security prot…
Seeing isn't Believing: Practical Adversarial Attack Against Object Detectors
Yue Zhao, Hong Zhu, Ruigang Liang +3
In this paper, we presented systematic solutions to build robust and practical AEs against real world object detectors. Particularly, for Hiding Attack (HA), we proposed the featur…