29 citations · 92 across the 18 of their papers we have counts for
8 papers · 1 filter
Gradient Masking and the Underestimated Robustness Threats of Differential Privacy in Deep Learning
Franziska Boenisch, Philip Sperl, Konstantin Böttinger
An important problem in deep learning is the privacy and security of neural networks (NNs). Both aspects have long been considered separately. To date, it is still poorly understoo…
Defending Against Adversarial Denial-of-Service Data Poisoning Attacks
Nicolas M. Müller, Simon Roschmann, Konstantin Böttinger
Data poisoning is one of the most relevant security threats against machine learning and data-driven technologies. Since many applications rely on untrusted training data, an attac…
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…
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…
: 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'…
Side-Channel Aware Fuzzing
Philip Sperl, Konstantin Böttinger
Software testing is becoming a critical part of the development cycle of embedded devices, enabling vulnerability detection. A well-studied approach of software testing is fuzz-tes…
Stack Overflow Considered Harmful? The Impact of Copy&Paste on Android Application Security
Felix Fischer, Konstantin Böttinger, Huang Xiao +4
Online programming discussion platforms such as Stack Overflow serve as a rich source of information for software developers. Available information include vibrant discussions and…