58 citations · 361 across the 36 of their papers we have counts for
6 papers · 1 filter
AdvSPADE: Realistic Unrestricted Attacks for Semantic Segmentation
Guangyu Shen, Chengzhi Mao, Junfeng Yang +1
Due to the inherent robustness of segmentation models, traditional norm-bounded attack methods show limited effect on such type of models. In this paper, we focus on generating unr…
Rebuttal to Berger et al., TOPLAS 2019
Baishakhi Ray, Prem Devanbu, Vladimir Filkov
Berger et al., published in TOPLAS 2019, is a critique of our 2014 FSE conference abstract and its archival version, the 2017 CACM paper: A Large-Scale Study of Programming Languag…
Metric Learning for Adversarial Robustness
Chengzhi Mao, Ziyuan Zhong, Junfeng Yang +2
Deep networks are well-known to be fragile to adversarial attacks. We conduct an empirical analysis of deep representations under the state-of-the-art attack method called PGD, and…
ConEx: Efficient Exploration of Big-Data System Configurations for Better Performance
Rahul Krishna, Chong Tang, Kevin Sullivan +1
Configuration space complexity makes the big-data software systems hard to configure well. Consider Hadoop, with over nine hundred parameters, developers often just use the default…
Neutaint: Efficient Dynamic Taint Analysis with Neural Networks
Dongdong She, Yizheng Chen, Abhishek Shah +2
Dynamic taint analysis (DTA) is widely used by various applications to track information flow during runtime execution. Existing DTA techniques use rule-based taint-propagation, wh…
Testing DNN Image Classifiers for Confusion & Bias Errors
Yuchi Tian, Ziyuan Zhong, Vicente Ordonez +2
Image classifiers are an important component of today's software, from consumer and business applications to safety-critical domains. The advent of Deep Neural Networks (DNNs) is t…