13 citations · 13 across the 7 of their papers we have counts for
7 papers
BIAS-ID: A Framework for Analyzing Transformation Biases in AI-Generated Image Detectors
Jonas Ricker, Asja Fischer, Erwin Quiring
Given the surge of harmful AI-generated imagery online, reliably distinguishing authentic images from generated ones has become an urgent research topic. While many proposed detect…
Revisiting Neural Activation Coverage for Uncertainty Estimation
Benedikt Franke, Nils Förster, Frank Köster +3
Neural activation coverage (NAC) is a recently-proposed technique for out-of-distribution detection and generalization. We build upon this promising foundation and extend the metho…
Precision-Varying Prediction (PVP): Robustifying ASR systems against adversarial attacks
Matías Pizarro, Raghavan Narasimhan, Jonas Killian +1
With the increasing deployment of automated and agentic systems, ensuring the adversarial robustness of automatic speech recognition (ASR) models has become highly relevant. We obs…
SAMSEM -- A Generic and Scalable Approach for IC Metal Line Segmentation
Christian Gehrmann, Jonas Ricker, Simon Damm +5
In light of globalized hardware supply chains, the assurance of hardware components has gained significant interest, particularly in cryptographic applications and high-stakes scen…
Integrating uncertainty quantification into randomized smoothing based robustness guarantees
Sina Däubener, Kira Maag, David Krueger +1
Deep neural networks have proven to be extremely powerful, however, they are also vulnerable to adversarial attacks which can cause hazardous incorrect predictions in safety-critic…
Detecting Adversarial Attacks in Semantic Segmentation via Uncertainty Estimation: A Deep Analysis
Kira Maag, Roman Resner, Asja Fischer
Deep neural networks have demonstrated remarkable effectiveness across a wide range of tasks such as semantic segmentation. Nevertheless, these networks are vulnerable to adversari…
Uncertainty-weighted Loss Functions for Improved Adversarial Attacks on Semantic Segmentation
Kira Maag, Asja Fischer
State-of-the-art deep neural networks have been shown to be extremely powerful in a variety of perceptual tasks like semantic segmentation. However, these networks are vulnerable t…