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Found 10 papers, 8 papers with code
Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning
Unfortunately, we show that any privacy-preserving collaborative deep learning is susceptible to a powerful attack that we devise in this paper.
PassGAN: A Deep Learning Approach for Password Guessing
State-of-the-art password guessing tools, such as HashCat and John the Ripper, enable users to check billions of passwords per second against password hashes.
Unleashing the Tiger: Inference Attacks on Split Learning
We investigate the security of Split Learning -- a novel collaborative machine learning framework that enables peak performance by requiring minimal resources consumption.
Reducing Bias in Modeling Real-world Password Strength via Deep Learning and Dynamic Dictionaries
Password security hinges on an in-depth understanding of the techniques adopted by attackers.
Eluding Secure Aggregation in Federated Learning via Model Inconsistency
Indeed, the use of secure aggregation prevents the server from learning the value and the source of the individual model updates provided by the users, hampering inference and data attribution attacks.
Watermarks in the Sand: Impossibility of Strong Watermarking for Generative Models
To prove this result, we introduce a generic efficient watermark attack; the attacker is not required to know the private key of the scheme or even which scheme is used.
Universal Neural-Cracking-Machines: Self-Configurable Password Models from Auxiliary Data
Specifically, the model uses deep learning to capture the correlation between the auxiliary data of a group of users (e. g., users of a web application) and their passwords.
Interpretable Probabilistic Password Strength Meters via Deep Learning
Probabilistic password strength meters have been proved to be the most accurate tools to measure password strength.
Hacking Smart Machines with Smarter Ones: How to Extract Meaningful Data from Machine Learning Classifiers
Machine Learning (ML) algorithms are used to train computers to perform a variety of complex tasks and improve with experience.
WHAT ARE GANS USEFUL FOR?
In this paper, we noticed that even though GANs might not be able to generate samples from the underlying distribution (or we cannot tell at least), they are capturing some structure of the data in that high dimensional space.
