38 citations · 40 across the 2 of their papers we have counts for
4 papers
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…
"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…
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…
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…