Search Results for author:
Found 13 papers, 9 papers with code
Whispers in the Machine: Confidentiality in LLM-integrated Systems
Specifically, malicious tools can exploit vulnerabilities in the LLM itself to manipulate the model and compromise the data of other services, raising the question of how private data can be protected in the context of LLM integrations.
$σ$-zero: Gradient-based Optimization of $\ell_0$-norm Adversarial Examples
Evaluating the adversarial robustness of deep networks to gradient-based attacks is challenging.
A Representative Study on Human Detection of Artificially Generated Media Across Countries
To further understand which factors influence people's ability to detect generated media, we include personal variables, chosen based on a literature review in the domains of deepfake and fake news research.
LLM-Deliberation: Evaluating LLMs with Interactive Multi-Agent Negotiation Games
There is a growing interest in using Large Language Models (LLMs) as agents to tackle real-world tasks that may require assessing complex situations.
On the Limitations of Model Stealing with Uncertainty Quantification Models
We realize that during training, the models tend to have similar predictions, indicating that the network diversity we wanted to leverage using uncertainty quantification models is not (high) enough for improvements on the model stealing task.
CodeLMSec Benchmark: Systematically Evaluating and Finding Security Vulnerabilities in Black-Box Code Language Models
We evaluate the effectiveness of our approach by examining code language models in generating high-risk security weaknesses.
WaveFake: A Data Set to Facilitate Audio Deepfake Detection
Deep generative modeling has the potential to cause significant harm to society.
Dompteur: Taming Audio Adversarial Examples
In this paper we propose a different perspective: We accept the presence of adversarial examples against ASR systems, but we require them to be perceivable by human listeners.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
VenoMave: Targeted Poisoning Against Speech Recognition
In a more realistic scenario, when the target audio waveform is played over the air in different rooms, VENOMAVE maintains a success rate of up to 73. 3%.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Detecting Adversarial Examples for Speech Recognition via Uncertainty Quantification
The neural networks for uncertainty quantification simultaneously diminish the vulnerability to the attack, which is reflected in a lower recognition accuracy of the malicious target text in comparison to a standard hybrid ASR system.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Leveraging Frequency Analysis for Deep Fake Image Recognition
Based on this analysis, we demonstrate how the frequency representation can be used to identify deep fake images in an automated way, surpassing state-of-the-art methods.
Imperio: Robust Over-the-Air Adversarial Examples for Automatic Speech Recognition Systems
In this paper, we demonstrate the first algorithm that produces generic adversarial examples, which remain robust in an over-the-air attack that is not adapted to the specific environment.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Adversarial Attacks Against Automatic Speech Recognition Systems via Psychoacoustic Hiding
We use this backpropagation to learn the degrees of freedom for the adversarial perturbation of the input signal, i. e., we apply a psychoacoustic model and manipulate the acoustic signal below the thresholds of human perception.
Cryptography and Security Sound Audio and Speech Processing
