Localizing Speech Deepfakes Beyond Transitions via Segment-Aware Learning
Abstract
Localizing partial deepfake audio, where only segments of speech are manipulated, remains challenging due to the subtle and scattered nature of these modifications. Existing approaches typically rely on frame-level predictions to identify spoofed segments, and some recent methods improve performance by concentrating on the transitions between real and fake audio. However, we observe that these models tend to over-rely on boundary artifacts while neglecting the manipulated content that follows. We argue that effective localization requires understanding the entire segments beyond just detecting transitions. Thus, we propose Segment-Aware Learning (SAL), a framework that encourages models to focus on the internal structure of segments. SAL introduces two core techniques: Segment Positional Labeling, which provides fine-grained frame supervision based on relative position within a segment; and Cross-Segment Mixing, a data augmentation method that generates diverse segment patterns. Experiments across multiple deepfake localization datasets show that SAL consistently achieves strong performance in both in-domain and out-of-domain settings, with notable gains in non-boundary regions and reduced reliance on transition artifacts. The code is available at https://github.com/SentryMao/SAL.
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 paper presents a significant advancement in the localization of speech deepfakes through the introduction of Segment-Aware Learning, which effectively shifts the focus from transition artifacts to the internal characteristics of audio segments. This innovative approach, supported by rigorous experimentation and analysis, demonstrates a meaningful contribution to the field of audio deepfake detection.
Methodology
The proposed Segment-Aware Learning (SAL) framework introduces two innovative techniques: Segment Positional Labeling (SPL) and Cross-Segment Mixing (CSM). SPL enhances the model's ability to learn from the internal structure of audio segments by providing fine-grained positional labels, thus reducing the reliance on boundary artifacts. CSM diversifies training data by mixing segments from different utterances, which helps in generalization and mitigates overfitting. This dual approach is well-justified and addresses the shortcomings of existing methods that focus primarily on transition detection.
Results & evaluation
The experiments are comprehensive, utilizing multiple datasets (PartialSpoof, HAD, and LlamaPartialSpoof) to validate the effectiveness of the SAL framework. The results demonstrate that SAL outperforms existing methods, particularly in non-boundary regions, and achieves state-of-the-art performance on the PartialSpoof benchmark. The ablation studies provide insight into the contributions of each component, reinforcing the importance of SPL and CSM in improving model performance.
Reproducibility
The paper provides sufficient implementation details, including architecture choices, training configurations, and evaluation metrics. The availability of the code on GitHub enhances reproducibility, allowing other researchers to replicate the experiments and validate the findings.
Limitations
While the study shows strong performance across various datasets, it does not address potential limitations such as the model's performance on highly sophisticated deepfake audio that may not conform to the training data distributions. Additionally, the reliance on specific datasets may limit the generalizability of the findings to broader real-world scenarios.
Broader impact
The advancements in detecting partial deepfake audio have significant implications for security and authentication in various applications, including digital media, communications, and law enforcement. The proposed methods could enhance the robustness of audio verification systems, contributing to the fight against misinformation and audio-based fraud. The paper presents a significant advancement in the localization of speech deepfakes through the introduction of Segment-Aware Learning, which effectively shifts the focus from transition artifacts to the internal characteristics of audio segments. This innovative approach, supported by rigorous experimentation and analysis, demonstrates a meaningful contribution to the field of audio deepfake detection.
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