169 citations · 614 across the 29 of their papers we have counts for
31 papers
Post-breach Recovery: Protection against White-box Adversarial Examples for Leaked DNN Models
Shawn Shan, Wenxin Ding, Emily Wenger +2
Server breaches are an unfortunate reality on today's Internet. In the context of deep neural network (DNN) models, they are particularly harmful, because a leaked model gives an a…
Assessing Privacy Risks from Feature Vector Reconstruction Attacks
Emily Wenger, Francesca Falzon, Josephine Passananti +2
In deep neural networks for facial recognition, feature vectors are numerical representations that capture the unique features of a given face. While it is known that a version of…
SoK: Anti-Facial Recognition Technology
Emily Wenger, Shawn Shan, Haitao Zheng +1
The rapid adoption of facial recognition (FR) technology by both government and commercial entities in recent years has raised concerns about civil liberties and privacy. In respon…
Poison Forensics: Traceback of Data Poisoning Attacks in Neural Networks
Shawn Shan, Arjun Nitin Bhagoji, Haitao Zheng +1
In adversarial machine learning, new defenses against attacks on deep learning systems are routinely broken soon after their release by more powerful attacks. In this context, fore…
"Hello, It's Me": Deep Learning-based Speech Synthesis Attacks in the Real World
Emily Wenger, Max Bronckers, Christian Cianfarani +4
Advances in deep learning have introduced a new wave of voice synthesis tools, capable of producing audio that sounds as if spoken by a target speaker. If successful, such tools in…
Towards Performance Clarity of Edge Video Analytics
Zhujun Xiao, Zhengxu Xia, Haitao Zheng +2
Edge video analytics is becoming the solution to many safety and management tasks. Its wide deployment, however, must first address the tension between inference accuracy and resou…
A Real-time Defense against Website Fingerprinting Attacks
Shawn Shan, Arjun Nitin Bhagoji, Haitao Zheng +1
Anonymity systems like Tor are vulnerable to Website Fingerprinting (WF) attacks, where a local passive eavesdropper infers the victim's activity. Current WF attacks based on deep…
Backdoor Attacks Against Deep Learning Systems in the Physical World
Emily Wenger, Josephine Passananti, Arjun Bhagoji +3
Backdoor attacks embed hidden malicious behaviors into deep learning models, which only activate and cause misclassifications on model inputs containing a specific trigger. Existin…
Blacklight: Scalable Defense for Neural Networks against Query-Based Black-Box Attacks
Huiying Li, Shawn Shan, Emily Wenger +3
Deep learning systems are known to be vulnerable to adversarial examples. In particular, query-based black-box attacks do not require knowledge of the deep learning model, but can…
Fawkes: Protecting Privacy against Unauthorized Deep Learning Models
Shawn Shan, Emily Wenger, Jiayun Zhang +3
Today's proliferation of powerful facial recognition systems poses a real threat to personal privacy. As Clearview.ai demonstrated, anyone can canvas the Internet for data and trai…
Trimming Mobile Applications for Bandwidth-Challenged Networks in Developing Regions
Qinge Xie, Qingyuan Gong, Xinlei He +4
Despite continuous efforts to build and update network infrastructure, mobile devices in developing regions continue to be constrained by limited bandwidth. Unfortunately, this coi…
"How do urban incidents affect traffic speed?" A Deep Graph Convolutional Network for Incident-driven Traffic Speed Prediction
Qinge Xie, Tiancheng Guo, Yang Chen +3
Accurate traffic speed prediction is an important and challenging topic for transportation planning. Previous studies on traffic speed prediction predominately used spatio-temporal…
Piracy Resistant Watermarks for Deep Neural Networks
Huiying Li, Emily Wenger, Shawn Shan +2
As companies continue to invest heavily in larger, more accurate and more robust deep learning models, they are exploring approaches to monetize their models while protecting their…
Regula Sub-rosa: Latent Backdoor Attacks on Deep Neural Networks
Yuanshun Yao, Huiying Li, Haitao Zheng +1
Recent work has proposed the concept of backdoor attacks on deep neural networks (DNNs), where misbehaviors are hidden inside "normal" models, only to be triggered by very specific…
Understanding the Effectiveness of Ultrasonic Microphone Jammer
Yuxin Chen, Huiying Li, Steven Nagels +4
Recent works have explained the principle of using ultrasonic transmissions to jam nearby microphones. These signals are inaudible to nearby users, but leverage "hardware nonlinear…
Gotta Catch 'Em All: Using Honeypots to Catch Adversarial Attacks on Neural Networks
Shawn Shan, Emily Wenger, Bolun Wang +3
Deep neural networks (DNN) are known to be vulnerable to adversarial attacks. Numerous efforts either try to patch weaknesses in trained models, or try to make it difficult or cost…
Wireless Side-Lobe Eavesdropping Attacks
Yanzi Zhu, Ying Ju, Bolun Wang +3
Millimeter-wave wireless networks offer high throughput and can (ideally) prevent eavesdropping attacks using narrow, directional beams. Unfortunately, imperfections in physical ha…
Addressing Training Bias via Automated Image Annotation
Zhujun Xiao, Yanzi Zhu, Yuxin Chen +3
Build accurate DNN models requires training on large labeled, context specific datasets, especially those matching the target scenario. We believe advances in wireless localization…
Et Tu Alexa? When Commodity WiFi Devices Turn into Adversarial Motion Sensors
Yanzi Zhu, Zhujun Xiao, Yuxin Chen +4
Our work demonstrates a new set of silent reconnaissance attacks, which leverages the presence of commodity WiFi devices to track users inside private homes and offices, without co…
Automated Crowdturfing Attacks and Defenses in Online Review Systems
Yuanshun Yao, Bimal Viswanath, Jenna Cryan +2
Malicious crowdsourcing forums are gaining traction as sources of spreading misinformation online, but are limited by the costs of hiring and managing human workers. In this paper,…
Graph Watermarks
Xiaohan Zhao, Qingyun Liu, Lin Zhou +2
From network topologies to online social networks, many of today's most sensitive datasets are captured in large graphs. A significant challenge facing owners of these datasets is…
Crowds on Wall Street: Extracting Value from Social Investing Platforms
Gang Wang, Tianyi Wang, Bolun Wang +4
For decades, the world of financial advisors has been dominated by large investment banks such as Goldman Sachs. In recent years, user-contributed investment services such as Seeki…
Shortest Paths in Microseconds
Rachit Agarwal, Matthew Caesar, P. Brighten Godfrey +1
Computing shortest paths is a fundamental primitive for several social network applications including socially-sensitive ranking, location-aware search, social auctions and social…
Shortest Paths in Less Than a Millisecond
Rachit Agarwal, Matthew Caesar, P. Brighten Godfrey +1
We consider the problem of answering point-to-point shortest path queries on massive social networks. The goal is to answer queries within tens of milliseconds while minimizing the…