333 citations · 1.3k across the 71 of their papers we have counts for
10 papers · 1 filter
Efficient Global String Kernel with Random Features: Beyond Counting Substructures
Lingfei Wu, Ian En-Hsu Yen, Siyu Huo +5
Analysis of large-scale sequential data has been one of the most crucial tasks in areas such as bioinformatics, text, and audio mining. Existing string kernels, however, either (i)…
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…
Attend To Count: Crowd Counting with Adaptive Capacity Multi-scale CNNs
Zhikang Zou, Yu Cheng, Xiaoye Qu +3
Crowd counting is a challenging task due to the large variations in crowd distributions. Previous methods tend to tackle the whole image with a single fixed structure, which is una…
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…
A Truthful FPTAS Mechanism for Emergency Demand Response in Colocation Data Centers
Jianhai Chen, Deshi Ye, Shouling Ji +3
Demand response (DR) is not only a crucial solution to the demand side management but also a vital means of electricity market in maintaining power grid reliability, sustainability…
V-Fuzz: Vulnerability-Oriented Evolutionary Fuzzing
Yuwei Li, Shouling Ji, Chenyang Lv +4
Fuzzing is a technique of finding bugs by executing a software recurrently with a large number of abnormal inputs. Most of the existing fuzzers consider all parts of a software equ…
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…
Adversarial Examples Versus Cloud-based Detectors: A Black-box Empirical Study
Xurong Li, Shouling Ji, Meng Han +4
Deep learning has been broadly leveraged by major cloud providers, such as Google, AWS and Baidu, to offer various computer vision related services including image classification,…