76 citations · 91 across the 11 of their papers we have counts for
11 papers
AdvSV: An Over-the-Air Adversarial Attack Dataset for Speaker Verification
Li Wang, Jiaqi Li, Yuhao Luo +7
It is known that deep neural networks are vulnerable to adversarial attacks. Although Automatic Speaker Verification (ASV) built on top of deep neural networks exhibits robust perf…
Differential Aggregation against General Colluding Attackers
Rong Du, Qingqing Ye, Yue Fu +4
Local Differential Privacy (LDP) is now widely adopted in large-scale systems to collect and analyze sensitive data while preserving users' privacy. However, almost all LDP protoco…
Tracing the Origin of Adversarial Attack for Forensic Investigation and Deterrence
Han Fang, Jiyi Zhang, Yupeng Qiu +3
Deep neural networks are vulnerable to adversarial attacks. In this paper, we take the role of investigators who want to trace the attack and identify the source, that is, the part…
Security of Deep Reinforcement Learning for Autonomous Driving: A Survey
Ambra Demontis, Srishti Gupta, Maura Pintor +6
Reinforcement learning (RL) enables agents to learn optimal behaviors through interaction with their environment and has been increasingly deployed in safety-critical applications,…
Mitigating Adversarial Attacks by Distributing Different Copies to Different Users
Jiyi Zhang, Han Fang, Wesley Joon-Wie Tann +3
Machine learning models are vulnerable to adversarial attacks. In this paper, we consider the scenario where a model is distributed to multiple buyers, among which a malicious buye…
Backdoor Pre-trained Models Can Transfer to All
Lujia Shen, Shouling Ji, Xuhong Zhang +6
Pre-trained general-purpose language models have been a dominating component in enabling real-world natural language processing (NLP) applications. However, a pre-trained model wit…
Thief, Beware of What Get You There: Towards Understanding Model Extraction Attack
Xinyi Zhang, Chengfang Fang, Jie Shi
Model extraction increasingly attracts research attentions as keeping commercial AI models private can retain a competitive advantage. In some scenarios, AI models are trained prop…
A-FMI: Learning Attributions from Deep Networks via Feature Map Importance
An Zhang, Xiang Wang, Chengfang Fang +3
Gradient-based attribution methods can aid in the understanding of convolutional neural networks (CNNs). However, the redundancy of attribution features and the gradient saturation…
Where Does the Robustness Come from? A Study of the Transformation-based Ensemble Defence
Chang Liao, Yao Cheng, Chengfang Fang +1
This paper aims to provide a thorough study on the effectiveness of the transformation-based ensemble defence for image classification and its reasons. It has been empirically show…
Publishing Location Dataset Differential Privately with Isotonic Regression
Chengfang Fang, Ee-Chien Chang
We consider the problem of publishing location datasets, in particular 2D spatial pointsets, in a differentially private manner. Many existing mechanisms focus on frequency counts…
Securing Interactive Sessions Using Mobile Device through Visual Channel and Visual Inspection
Chengfang Fang, Ee-Chien Chang
Communication channel established from a display to a device's camera is known as visual channel, and it is helpful in securing key exchange protocol. In this paper, we study how v…