48 citations · 106 across the 6 of their papers we have counts for
6 papers
On the Permanence of Backdoors in Evolving Models
Huiying Li, Arjun Nitin Bhagoji, Yuxin Chen +2
Existing research on training-time attacks for deep neural networks (DNNs), such as backdoors, largely assume that models are static once trained, and hidden backdoors trained into…
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…
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…