Search Results for author:
Found 13 papers, 5 papers with code
Salsa Fresca: Angular Embeddings and Pre-Training for ML Attacks on Learning With Errors
Our architecture improvements enable scaling to larger-dimension LWE problems: this work is the first instance of ML attacks recovering sparse binary secrets in dimension $n=1024$, the smallest dimension used in practice for homomorphic encryption applications of LWE where sparse binary secrets are proposed.
SALSA PICANTE: a machine learning attack on LWE with binary secrets
However, this attack assumes access to millions of eavesdropped LWE samples and fails at higher Hamming weights or dimensions.
Data Isotopes for Data Provenance in DNNs
With only query access to a trained model and no knowledge of the model training process, or control of the data labels, a user can apply statistical hypothesis testing to detect if a model has learned the spurious features associated with their isotopes by training on the user's data.
SALSA: Attacking Lattice Cryptography with Transformers
Currently deployed public-key cryptosystems will be vulnerable to attacks by full-scale quantum computers.
Natural Backdoor Datasets
Research on physical backdoors is limited by access to large datasets containing real images of physical objects co-located with targets of classification.
Assessing Privacy Risks from Feature Vector Reconstruction Attacks
In deep neural networks for facial recognition, feature vectors are numerical representations that capture the unique features of a given face.
SoK: Anti-Facial Recognition Technology
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.
"Hello, It's Me": Deep Learning-based Speech Synthesis Attacks in the Real World
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.
Backdoor Attacks Against Deep Learning Systems in the Physical World
A critical question remains unanswered: can backdoor attacks succeed using physical objects as triggers, thus making them a credible threat against deep learning systems in the real world?
Blacklight: Scalable Defense for Neural Networks against Query-Based Black-Box Attacks
In particular, query-based black-box attacks do not require knowledge of the deep learning model, but can compute adversarial examples over the network by submitting queries and inspecting returns.
Fawkes: Protecting Privacy against Unauthorized Deep Learning Models
In this paper, we propose Fawkes, a system that helps individuals inoculate their images against unauthorized facial recognition models.
Piracy Resistant Watermarks for Deep Neural Networks
We empirically show that our proposed watermarks achieve piracy resistance and other watermark properties, over a wide range of tasks and models.
Gotta Catch 'Em All: Using Honeypots to Catch Adversarial Attacks on Neural Networks
Attackers' optimization algorithms gravitate towards trapdoors, leading them to produce attacks similar to trapdoors in the feature space.
