141 citations · 177 across the 5 of their papers we have counts for
7 papers
Almost for Free: Crafting Adversarial Examples with Convolutional Image Filters
Alexander Warnecke, Konrad Rieck
Adversarial examples in machine learning are typically generated using gradients, obtained either directly through access to the model or approximated via queries to it. In this pa…
Manipulating Feature Visualizations with Gradient Slingshots
Dilyara Bareeva, Marina M. -C. Höhne, Alexander Warnecke +5
Feature Visualization (FV) is a widely used technique for interpreting concepts learned by Deep Neural Networks (DNNs), which synthesizes input patterns that maximally activate a g…
Evil from Within: Machine Learning Backdoors through Hardware Trojans
Alexander Warnecke, Julian Speith, Jan-Niklas Möller +2
Backdoors pose a serious threat to machine learning, as they can compromise the integrity of security-critical systems, such as self-driving cars. While different defenses have bee…
Machine Unlearning of Features and Labels
Alexander Warnecke, Lukas Pirch, Christian Wressnegger +1
Removing information from a machine learning model is a non-trivial task that requires to partially revert the training process. This task is unavoidable when sensitive data, such…
Dos and Don'ts of Machine Learning in Computer Security
Daniel Arp, Erwin Quiring, Feargus Pendlebury +5
With the growing processing power of computing systems and the increasing availability of massive datasets, machine learning algorithms have led to major breakthroughs in many diff…
Real-Time Radar-Based Gesture Detection and Recognition Built in an Edge-Computing Platform
Yuliang Sun, Tai Fei, Xibo Li +3
In this paper, a real-time signal processing frame-work based on a 60 GHz frequency-modulated continuous wave (FMCW) radar system to recognize gestures is proposed. In order to imp…
Evaluating Explanation Methods for Deep Learning in Security
Alexander Warnecke, Daniel Arp, Christian Wressnegger +1
Deep learning is increasingly used as a building block of security systems. Unfortunately, neural networks are hard to interpret and typically opaque to the practitioner. The machi…