most citedMLAAD: The Multi-Language Audio Anti-Spoofing Dataset

5 citations · 6 across the 2 of their papers we have counts for

collaborators

6 papers

cs.SD20265 cited

MLAAD: The Multi-Language Audio Anti-Spoofing Dataset

Nicolas M. Müller, Piotr Kawa, Wei Herng Choong +6

This paper presents the Multi-Language Audio Anti-Spoofing Dataset (MLAAD), version 10: a dataset of synthetic audio to train and evaluate audio deepfake detection models. It featu…

cs.CR20261 cited

DeePen: Penetration Testing for Audio Deepfake Detection

Nicolas Müller, Piotr Kawa, Adriana Stan +5

Deepfakes - manipulated or forged audio and video media - pose significant security risks to individuals, organizations, and society at large. To address these challenges, machine…

cs.CR2026

You Told Me to Do It: Measuring Instructional Text-induced Private Data Leakage in LLM Agents

Ching-Yu Kao, Xinfeng Li, Shenyu Dai +4

High-privilege LLM agents that autonomously process external documentation are increasingly trusted to automate tasks by reading and executing project instructions, yet they are gr…

cs.CR2026

Security-by-Design for LLM-Based Code Generation: Leveraging Internal Representations for Concept-Driven Steering Mechanisms

Maximilian Wendlinger, Daniel Kowatsch, Konstantin Böttinger +1

Large Language Models (LLMs) show remarkable capabilities in understanding natural language and generating complex code. However, as practitioners adopt CodeLLMs for increasingly c…

cs.LG2025

GRASPED: Graph Anomaly Detection using Autoencoder with Spectral Encoder and Decoder (Full Version)

Wei Herng Choong, Jixing Liu, Ching-Yu Kao +1

Graph machine learning has been widely explored in various domains, such as community detection, transaction analysis, and recommendation systems. In these applications, anomaly de…

cs.SD2025

Replay Attacks Against Audio Deepfake Detection

Nicolas Müller, Piotr Kawa, Wei-Herng Choong +5

We show how replay attacks undermine audio deepfake detection: By playing and re-recording deepfake audio through various speakers and microphones, we make spoofed samples appear a…