7 citations · 7 across the 8 of their papers we have counts for
8 papers
Neural Encoding Detection is Not All You Need for Synthetic Speech Detection
Luca Cuccovillo, Xin Wang, Milica Gerhardt +1
This paper reviews the current state and emerging trends in synthetic speech detection. It outlines the main data-driven approaches, discusses the advantages and drawbacks of focus…
Challenging Vision-Language Models with Physically Deployable Multimodal Semantic Lighting Attacks
Yingying Zhao, Chengyin Hu, Qike Zhang +7
Vision-Language Models (VLMs) have shown remarkable performance, yet their security remains insufficiently understood. Existing adversarial studies focus almost exclusively on the…
OmniTrace: A Unified Framework for Generation-Time Source Attribution in Omni-Modal LLMs
Qianqi Yan, Yichen Guo, Ching-Chen Kuo +4
An omni-modal language model can answer a question about a multimodal document, a recording, or a video, but it cannot show which part of the input the answer came from. Asking the…
Deepfake Word Detection by Next-token Prediction using Fine-tuned Whisper
Hoan My Tran, Xin Wang, Wanying Ge +2
Deepfake speech utterances can be forged by replacing one or more words in a bona fide utterance with semantically different words synthesized with speech-generative models. While…
RhythmBERT: A Self-Supervised Language Model Based on Latent Representations of ECG Waveforms for Heart Disease Detection
Xin Wang, Burcu Ozek, Aruna Mohan +3
Electrocardiogram (ECG) analysis is crucial for diagnosing heart disease, but most self-supervised learning methods treat ECG as a generic time series, overlooking physiologic sema…
Audio Codec Augmentation for Robust Collaborative Watermarking of Speech Synthesis
Lauri Juvela, Xin Wang
Automatic detection of synthetic speech is becoming increasingly important as current synthesis methods are both near indistinguishable from human speech and widely accessible to t…
Collaborative Watermarking for Adversarial Speech Synthesis
Lauri Juvela, Xin Wang
Advances in neural speech synthesis have brought us technology that is not only close to human naturalness, but is also capable of instant voice cloning with little data, and is hi…
A comparison of recent waveform generation and acoustic modeling methods for neural-network-based speech synthesis
Xin Wang, Jaime Lorenzo-Trueba, Shinji Takaki +2
Recent advances in speech synthesis suggest that limitations such as the lossy nature of the amplitude spectrum with minimum phase approximation and the over-smoothing effect in ac…