Listening Deepfake Detection: A New Perspective Beyond Speaking-Centric Forgery Analysis
Abstract
Existing deepfake detection research has primarily focused on scenarios where the manipulated subject is actively speaking, i.e., generating fabricated content by altering the speaker's appearance or voice. However, in realistic interaction settings, attackers often alternate between falsifying speaking and listening states to mislead their targets, thereby enhancing the realism and persuasiveness of the scenario. Although the detection of 'listening deepfakes' remains largely unexplored and is hindered by a scarcity of both datasets and methodologies, the relatively limited quality of synthesized listening reactions presents an excellent breakthrough opportunity for current deepfake detection efforts. In this paper, we present the task of Listening Deepfake Detection (LDD). We introduce ListenForge, the first dataset specifically designed for this task, constructed using five Listening Head Generation (LHG) methods. To address the distinctive characteristics of listening forgeries, we propose MANet, a Motion-aware and Audio-guided Network that captures subtle motion inconsistencies in listener videos while leveraging speaker's audio semantics to guide cross-modal fusion. Extensive experiments demonstrate that existing Speaking Deepfake Detection (SDD) models perform poorly in listening scenarios. In contrast, MANet achieves significantly superior performance on ListenForge. Our work highlights the necessity of rethinking deepfake detection beyond the traditional speaking-centric paradigm and opens new directions for multimodal forgery analysis in interactive communication settings. The dataset and code are available at https://anonymous.4open.science/r/LDD-B4CB.
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 makes a significant contribution by introducing the task of Listening Deepfake Detection and presenting a novel dataset and detection architecture. The methodology and experimental results highlight the potential for advancing deepfake detection techniques, emphasizing the importance of considering both speaking and listening behaviors in interactive scenarios.
Methodology
The paper introduces a novel task of Listening Deepfake Detection (LDD) and presents a comprehensive methodology through the development of the ListenForge dataset and the Motion-aware and Audio-guided Network (MANet). The methodology effectively captures the unique characteristics of listening forgeries by leveraging both visual and audio modalities. The integration of motion-aware and audio-guided modules demonstrates a thoughtful approach to addressing the challenges posed by listener behavior synthesis. The proposed architecture is well-structured, with clear delineation of components that enhance detection capabilities, particularly in capturing subtle motion inconsistencies and semantic correlations.
Results & evaluation
The experimental setup is robust, utilizing the newly constructed ListenForge dataset alongside existing benchmarks to evaluate the performance of MANet against traditional Speaking Deepfake Detection (SDD) models. The results indicate a significant performance improvement, with MANet achieving high AUC scores. The comparative analysis with baseline models and ablation studies provides a thorough understanding of the contributions of each component in the proposed architecture. However, the paper could benefit from additional qualitative analyses or user studies to further validate the effectiveness of the proposed methods.
Reproducibility
The implementation details are provided, including the architecture used for visual and audio feature extraction, training parameters, and dataset construction. However, the lack of a publicly accessible code repository or demo limits the reproducibility of the results. The authors mention that the dataset and code are available at an anonymous URL, which may hinder transparency until the repository is made public.
Limitations
One limitation is the reliance on the quality of the synthesized listener behaviors, which may still exhibit artifacts that could affect detection performance. Additionally, the dataset, while pioneering, may not encompass the full diversity of real-world listening scenarios, potentially limiting the generalizability of the findings. The paper also does not address the potential ethical implications of deepfake technology, which could be a critical aspect of future work.
Broader impact
This research opens new avenues for deepfake detection, particularly in interactive communication settings, which are increasingly relevant in today's digital landscape. The findings could have significant implications for security and privacy, especially in applications involving video conferencing and social media. By addressing the underexplored area of listening deepfakes, the work contributes to a more comprehensive understanding of multimodal forgery analysis. The paper makes a significant contribution by introducing the task of Listening Deepfake Detection and presenting a novel dataset and detection architecture. The methodology and experimental results highlight the potential for advancing deepfake detection techniques, emphasizing the importance of considering both speaking and listening behaviors in interactive scenarios.
Author of this paper? Send a correction.