333 citations · 649 across the 28 of their papers we have counts for
5 papers · 1 filter
A Tale of Evil Twins: Adversarial Inputs versus Poisoned Models
Ren Pang, Hua Shen, Xinyang Zhang +5
Despite their tremendous success in a range of domains, deep learning systems are inherently susceptible to two types of manipulations: adversarial inputs -- maliciously crafted sa…
De-Health: All Your Online Health Information Are Belong to Us
Shouling Ji, Qinchen Gu, Haiqin Weng +4
In this paper, we study the privacy of online health data. We present a novel online health data De-Anonymization (DA) framework, named De-Health. De-Health consists of two phases:…
FDI: Quantifying Feature-based Data Inferability
Shouling Ji, Haiqin Weng, Yiming Wu +3
Motivated by many existing security and privacy applications, e.g., network traffic attribution, linkage attacks, private web search, and feature-based data de-anonymization, in th…
Adversarial CAPTCHAs
Chenghui Shi, Xiaogang Xu, Shouling Ji +4
Following the principle of to set one's own spear against one's own shield, we study how to design adversarial CAPTCHAs in this paper. We first identify the similarity and differen…
SirenAttack: Generating Adversarial Audio for End-to-End Acoustic Systems
Tianyu Du, Shouling Ji, Jinfeng Li +3
Despite their immense popularity, deep learning-based acoustic systems are inherently vulnerable to adversarial attacks, wherein maliciously crafted audios trigger target systems t…