169 citations · 614 across the 29 of their papers we have counts for
14 papers · 1 filter
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…
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…
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…
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…
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…
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…
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…