TRACE: Training-Free Partial Audio Deepfake Detection via Embedding Trajectory Analysis of Speech Foundation Models
Abstract
Partial audio deepfakes, where synthesized segments are spliced into genuine recordings, are particularly deceptive because most of the audio remains authentic. Existing detectors are supervised: they require frame-level annotations, overfit to specific synthesis pipelines, and must be retrained as new generative models emerge. We argue that this supervision is unnecessary. We hypothesize that speech foundation models implicitly encode a forensic signal: genuine speech forms smooth, slowly varying embedding trajectories, while splice boundaries introduce abrupt disruptions in frame-level transitions. Building on this, we propose TRACE (Training-free Representation-based Audio Countermeasure via Embedding dynamics), a training-free framework that detects partial audio deepfakes by analyzing the first-order dynamics of frozen speech foundation model representations without any training, labeled data, or architectural modification. We evaluate TRACE on four benchmarks that span two languages using six speech foundation models. In PartialSpoof, TRACE achieves 8.08% EER, competitive with fine-tuned supervised baselines. In LlamaPartialSpoof, the most challenging benchmark featuring LLM-driven commercial synthesis, TRACE surpasses a supervised baseline outright (24.12% vs. 24.49% EER) without any target-domain data. These results show that temporal dynamics in speech foundation models provide an effective, generalize signal for training-free audio forensics.
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 TRACE, a training-free framework for detecting partial audio deepfakes by analyzing the dynamics of speech foundation model embeddings. This work represents a significant advancement in audio forensics, offering a novel methodology that challenges traditional supervised detection approaches and opens new avenues for research in the field.
Methodology
The proposed TRACE framework introduces a novel approach to detecting partial audio deepfakes without the need for training or labeled data. By analyzing the first-order dynamics of frozen speech foundation model representations, the methodology cleverly leverages the inherent properties of genuine speech versus manipulated audio. This is a significant departure from traditional supervised methods, showcasing a fresh perspective on audio forensics. However, the paper could benefit from a more detailed explanation of the embedding trajectory analysis and its computational efficiency.
Results & evaluation
The experiments are well-structured, evaluating TRACE on four benchmarks across two languages and using six different speech foundation models. The results demonstrate competitive performance against fine-tuned supervised baselines, particularly in challenging scenarios like LlamaPartialSpoof. However, the paper lacks comprehensive details on the datasets used, such as their sizes and the specific characteristics of the audio samples, which would enhance the understanding of the evaluation's robustness.
Reproducibility
The paper does not provide sufficient details regarding the implementation of TRACE, such as the specific configurations of the speech foundation models used or the exact procedures for embedding trajectory analysis. This lack of detail may hinder reproducibility, as other researchers may struggle to replicate the results without clear guidelines or code availability.
Limitations
One limitation is the reliance on the performance of existing speech foundation models, which may vary in quality and robustness. Additionally, while the training-free approach is innovative, it may not generalize well to all forms of audio manipulation beyond the tested benchmarks. The paper also does not address potential adversarial attacks against the proposed detection method.
Broader impact
The implications of TRACE are significant for the field of audio forensics, particularly in combating misinformation and enhancing the integrity of audio content. The training-free nature of the method could facilitate its adoption in real-world applications where rapid detection is critical, such as in media verification and security. However, further exploration of its applicability across diverse audio manipulation techniques is necessary. The main contribution of this paper is the introduction of TRACE, a training-free framework for detecting partial audio deepfakes by analyzing the dynamics of speech foundation model embeddings. This work represents a significant advancement in audio forensics, offering a novel methodology that challenges traditional supervised detection approaches and opens new avenues for research in the field.
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