arXiv subjects

Yixuan Xiao

Publications and source records attributed to Yixuan Xiao.

5 recordsLinked to original sources

arXiv2026

What Affects the Performance of Fake Audio Detection? Analyzing Factors in a Continual Learning Setting

The increasing sophistication of deepfake audio generation technologies makes it important to develop robust fake audio detection systems that can adapt over time. This study examines how various factors impact the performance of detection systems in a continual learning setting. We focus on factors such as attacker architectures, attackers' training datasets, speaker diversity, and task order. We evaluate the performance of three detection models trained with four different strategies, including direct fine-tuning, one-class classification, random replay, and Learning without Forgetting. Results show that artifacts from the fake audios might arise from the attackers' training datasets, and simply changing attacker architectures does not sufficiently challenge detection systems. Moreover, task order and speaker diversity can significantly influence performance, with varying degrees of sensitivity across different detection models and training strategies. These insights underline the need for careful consideration of these factors when developing robust detection systems.

cs.SD↗

arXiv2026

Layer-Wise Decision Fusion for Fake Audio Detection Using XLS-R

Recent fake audio detection methods often leverage large speech models to achieve robust speech representations. These models are typically very deep, providing multiple layer-wise representations. However, current works often rely solely on single layer representation or feature fusion to extract one utterance-level representation for decision making. These methods risk underutilizing rich information from multiple layers and might induce feature collapse. We propose a novel layer-wise decision fusion method that applies fusion after per-layer decision making and achieves the best cross-dataset performance on In-the-Wild dataset (EER 6.90%) compared to other strong baselines. Our model design also makes the model more transparent, allowing us to conduct detailed analysis to reveal the underlying mechanism of decision making.

cs.SD↗

arXiv2026

Evidence Subspace Projection: Measuring How Much Evidence Explains Deepfake Detection in Self-Supervised Speech Models

Self-supervised learning (SSL) models are widely used as feature extractors for state-of-the-art audio deepfake detection, but it remains unclear how to directly and quantitatively connect what SSL models capture to detection decisions. To address this gap, we propose Evidence Subspace Projection, a method that represents both evidence factors (e.g., attack category, codec, gender, transmission) and authenticity labels in a shared space constructed from SSL models' neuron activation patterns. By projecting the decision vector onto each evidence subspace, we obtain a scalar ratio that quantifies the explanatory power of each evidence type. We evaluate SSL models in raw, fine-tuned, and post-trained settings on multiple datasets. The results confirm findings from established studies, validating the proposed method, and reveal new insights into model behavior.

cs.SD↗

arXiv2026

DeepFense: A Unified, Modular, and Extensible Framework for Robust Deepfake Audio Detection

Speech deepfake detection is a well-established research field with different models, datasets, and training strategies. However, the lack of standardized implementations and evaluation protocols limits reproducibility, benchmarking, and comparison across studies. In this work, we present DeepFense, a comprehensive, open-source PyTorch toolkit integrating the latest architectures, loss functions, and augmentation pipelines, alongside over 100 recipes. Using DeepFense, we conducted a large-scale evaluation of more than 400 models. Our findings reveal that while carefully curated training data improves cross-domain generalization, the choice of pre-trained front-end feature extractor dominates overall performance variance. Crucially, we show severe biases in high-performing models regarding audio quality, speaker gender, and language. DeepFense is expected to facilitate real-world deployment with the necessary tools to address equitable training data selection and front-end fine-tuning.

cs.SD↗

arXiv2026

How to Label Resynthesized Audio: The Dual Role of Neural Audio Codecs in Audio Deepfake Detection

Since Text-to-Speech systems typically don't produce waveforms directly, recent spoof detection studies use resynthesized waveforms from vocoders and neural audio codecs to simulate an attacker. Unlike vocoders, which are specifically designed for speech synthesis, neural audio codecs were originally developed for compressing audio for storage and transmission. However, their ability to discretize speech also sparked interest in language-modeling-based speech synthesis. Owing to this dual functionality, codec resynthesized data may be labeled as either bonafide or spoof. So far, very little research has addressed this issue. In this study, we present a challenging extension of the ASVspoof 5 dataset constructed for this purpose. We examine how different labeling choices affect detection performance and provide insights into labeling strategies.

cs.SD↗