DFALLM: Achieving Generalizable Multitask Deepfake Detection by Optimizing Audio LLM Components
Abstract
Audio deepfake detection has recently garnered public concern due to its implications for security and reliability. Traditional deep learning methods have been widely applied to this task but often lack generalisability when confronted with newly emerging spoofing techniques and more tasks such as spoof attribution recognition rather than simple binary classification. In principle, Large Language Models (LLMs) are considered to possess the needed generalisation capabilities. However, previous research on Audio LLMs (ALLMs) indicates a generalization bottleneck in audio deepfake detection performance, even when sufficient data is available. Consequently, this study investigates the model architecture and examines the effects of the primary components of ALLMs, namely the audio encoder and the text-based LLM. Our experiments demonstrate that the careful selection and combination of audio encoders and text-based LLMs are crucial for unlocking the deepfake detection potential of ALLMs. We further propose an ALLM structure capable of generalizing deepfake detection abilities to out-of-domain spoofing tests and other deepfake tasks, such as spoof positioning and spoof attribution recognition. Our proposed model architecture achieves state-of-the-art (SOTA) performance across multiple datasets, including ASVSpoof2019, InTheWild, and Demopage, with accuracy reaching up to 95.76% on average, and exhibits competitive capabilities in other deepfake detection tasks such as attribution, and localisation compared to SOTA audio understanding models. Data and codes are provided in supplementary materials.
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 DFALLM, a framework that optimizes audio LLM components for generalizable multitask deepfake detection. This work significantly advances the field by addressing the limitations of traditional models and demonstrating the effectiveness of a unified approach to tackle multiple deepfake-related tasks.
Methodology
The methodology is robust, focusing on the systematic investigation of audio encoders and textual LLMs within the proposed DFALLM framework. The authors effectively establish a clear hypothesis regarding the bottlenecks in generalization and provide a structured approach to validate their claims through component analysis and multitask validation. The use of distinct prompts for different tasks showcases a thoughtful design that enhances the model's adaptability.
Results & evaluation
The experiments are comprehensive, utilizing multiple datasets for both in-domain and out-of-domain testing. The results demonstrate significant performance improvements with the proposed architecture, achieving state-of-the-art results across various tasks. However, the paper could benefit from more detailed statistical analysis of the results, including confidence intervals and significance testing.
Reproducibility
The paper provides a good level of detail regarding the training and implementation of the models, including hyperparameters and training strategies. However, the absence of a publicly available code repository or demo limits the reproducibility of the results. Clear links to datasets and training scripts would enhance this aspect.
Limitations
The paper does not address potential limitations in terms of computational resources required for training the proposed models, which may hinder accessibility for researchers with limited resources. Additionally, the evaluation primarily focuses on specific datasets, which may not fully represent the diversity of real-world audio deepfakes.
Broader impact
The implications of this research are significant, considering the growing concern over audio deepfakes in security and misinformation contexts. The proposed model could enhance detection capabilities across various applications, including media verification and forensic analysis. However, the potential for misuse of deepfake technology remains a critical concern that should be addressed in future work. The main contribution of this paper is the introduction of DFALLM, a framework that optimizes audio LLM components for generalizable multitask deepfake detection. This work significantly advances the field by addressing the limitations of traditional models and demonstrating the effectiveness of a unified approach to tackle multiple deepfake-related tasks.
Author of this paper? Send a correction.