Search Results for author:
Found 5 papers, 3 papers with code
Enhancing Real-World Adversarial Patches through 3D Modeling of Complex Target Scenes
We use the framework to create a patch for an everyday scene and evaluate its performance using a novel evaluation process that ensures that our results are reproducible in both the digital space and the real world.
Stop Bugging Me! Evading Modern-Day Wiretapping Using Adversarial Perturbations
Our results in the real world suggest that our approach is a feasible solution for privacy protection.
Taking Over the Stock Market: Adversarial Perturbations Against Algorithmic Traders
In this study, we present a realistic scenario in which an attacker influences algorithmic trading systems by using adversarial learning techniques to manipulate the input data stream in real time.
Not All Datasets Are Born Equal: On Heterogeneous Data and Adversarial Examples
We, however, argue that machine learning models trained on heterogeneous tabular data are as susceptible to adversarial manipulations as those trained on continuous or homogeneous data such as images.
N-BaIoT: Network-based Detection of IoT Botnet Attacks Using Deep Autoencoders
The proliferation of IoT devices which can be more easily compromised than desktop computers has led to an increase in the occurrence of IoT based botnet attacks.
