9 papers
Deep Reinforcement Learning for Backup Strategies against Adversaries
Pascal Debus, Nicolas Müller, Konstantin Böttinger
Many defensive measures in cyber security are still dominated by heuristics, catalogs of standard procedures, and best practices. Considering the case of data backup strategies, we…
Towards Resistant Audio Adversarial Examples
Tom Dörr, Karla Markert, Nicolas M. Müller +1
Adversarial examples tremendously threaten the availability and integrity of machine learning-based systems. While the feasibility of such attacks has been observed first in the do…
Optimizing Information Loss Towards Robust Neural Networks
Philip Sperl, Konstantin Böttinger
Neural Networks (NNs) are vulnerable to adversarial examples. Such inputs differ only slightly from their benign counterparts yet provoke misclassifications of the attacked NNs. Th…
Data Poisoning Attacks on Regression Learning and Corresponding Defenses
Nicolas Michael Müller, Daniel Kowatsch, Konstantin Böttinger
Adversarial data poisoning is an effective attack against machine learning and threatens model integrity by introducing poisoned data into the training dataset. So far, it has been…
: Activation Anomaly Analysis
Philip Sperl, Jan-Philipp Schulze, Konstantin Böttinger
Inspired by recent advances in coverage-guided analysis of neural networks, we propose a novel anomaly detection method. We show that the hidden activation values contain informati…
DLA: Dense-Layer-Analysis for Adversarial Example Detection
Philip Sperl, Ching-Yu Kao, Peng Chen +1
In recent years Deep Neural Networks (DNNs) have achieved remarkable results and even showed super-human capabilities in a broad range of domains. This led people to trust in DNNs'…