DeepFense: A Unified, Modular, and Extensible Framework for Robust Deepfake Audio Detection
Abstract
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.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The main contribution of this paper is the introduction of DeepFense, a comprehensive, modular, and extensible framework for robust deepfake audio detection that facilitates reproducible research and addresses critical biases in model performance. This work significantly advances the field by providing a standardized toolkit that enhances the ability to benchmark and compare deepfake detection models effectively.
Methodology
The methodology presented in DeepFense is robust and well-structured, focusing on creating a modular and extensible framework for deepfake audio detection. The use of a configuration-driven design allows for easy experimentation and reproducibility, which is a significant advancement in the field. The integration of over 400 models and 100 recipes enhances the toolkit's utility for researchers. The modular architecture facilitates the isolation of algorithmic innovations from implementation artifacts, which is critical for accurate benchmarking.
Results & evaluation
The experimental evaluation is extensive, covering a large-scale comparison of 400 models across 13 datasets, which is a notable strength of the paper. The results provide valuable insights into the impact of front-end feature extractors, back-end architectures, and training datasets on model performance. The findings regarding biases in model performance based on audio quality, speaker gender, and language are particularly important for ensuring equitable AI systems.
Reproducibility
The paper emphasizes reproducibility through its open-source nature and the provision of a comprehensive toolkit that allows other researchers to replicate experiments easily. The use of a single YAML file for experiment configuration is a strong point, as it simplifies the process of sharing and reproducing results.
Limitations
While the paper presents a significant advancement, it acknowledges limitations such as the lack of a multi-dataset training pipeline and the focus solely on detection tasks. These limitations suggest areas for future research, including the need for more comprehensive training strategies that can mitigate biases.
Broader impact
The implications of this work are substantial, particularly in the context of increasing concerns about deepfake technology and its potential misuse. By providing a standardized toolkit for deepfake detection, DeepFense can help improve the robustness of systems used in real-world applications, thereby enhancing security and trust in voice biometric systems. The main contribution of this paper is the introduction of DeepFense, a comprehensive, modular, and extensible framework for robust deepfake audio detection that facilitates reproducible research and addresses critical biases in model performance. This work significantly advances the field by providing a standardized toolkit that enhances the ability to benchmark and compare deepfake detection models effectively.
Author of this paper? Send a correction.