Search Results for author:
Found 15 papers, 10 papers with code
AI-Generated Faces in the Real World: A Large-Scale Case Study of Twitter Profile Images
Recent advances in the field of generative artificial intelligence (AI) have blurred the lines between authentic and machine-generated content, making it almost impossible for humans to distinguish between such media.
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.
No more Reviewer #2: Subverting Automatic Paper-Reviewer Assignment using Adversarial Learning
In this paper, we show that this automation can be manipulated using adversarial learning.
Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection
Large Language Models (LLMs) are increasingly being integrated into various applications.
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.
Towards the Detection of Diffusion Model Deepfakes
However, relatively little attention has been paid to the detection of DM-generated images, which is critical to prevent adverse impacts on our society.
[RE] CNN-generated images are surprisingly easy to spot...for now
This work evaluates the reproducibility of the paper "CNN-generated images are surprisingly easy to spot... for now" by Wang et al. published at CVPR 2020.
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
Efficient Calculation of Adversarial Examples for Bayesian Neural Networks
In this paper we propose to efficiently attack Bayesian neural networks with adversarial examples calculated for a deterministic network with parameters given by the mean of the posterior distribution.
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
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.
(Un)informed Consent: Studying GDPR Consent Notices in the Field
We also show that the wide-spread practice of nudging has a large effect on the choices users make.
Human-Computer Interaction Computers and Society
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
We Value Your Privacy ... Now Take Some Cookies: Measuring the GDPR's Impact on Web Privacy
We categorized all observed cookie consent notices and evaluated 16 common implementations with respect to their technical realization of cookie consent.
Computers and Society
