arXiv:2602.00209v1 Announce Type: new
Abstract: This paper presents a system for detecting fake audio-visual content (i.e., video deepfake), developed for Track 2 of the DDL Challenge. The proposed system employs a two-stage framework, comprising unimodal detection and multimodal score fusion. Specifically, it incorporates an audio deepfake detection module and an audio localization module to analyze and pinpoint manipulated segments in the audio stream. In parallel, an image-based deepfake detection and localization module is employed to process the visual modality. To effectively leverage complementary information across different modalities, we further propose a multimodal score fusion strategy that integrates the outputs from both audio and visual modules. Guided by a detailed analysis of the training and evaluation dataset, we explore and evaluate several score calculation and fusion strategies to improve system robustness. Overall, the final fusion-based system achieves an AUC of 0.87, an AP of 0.55, and an AR of 0.23 on the challenge test set, resulting in a final score of 0.5528.
Expert Commentary: Detecting Fake Audio-Visual Content
The development of a system for detecting fake audio-visual content, such as video deepfakes, is a critical step in combating the spread of misinformation and disinformation in today’s digital age. This paper presents a two-stage framework that combines audio and visual analysis to effectively identify manipulated segments in multimedia content.
Multi-Disciplinary Approach
This research exemplifies the multi-disciplinary nature of multimedia information systems by integrating techniques from audio processing, image analysis, and machine learning. The use of both unimodal and multimodal detection methods highlights the importance of considering multiple sources of data to improve the accuracy and robustness of the detection system.
Relation to Artificial Reality and Virtual Realities
Understanding and detecting fake audio-visual content is crucial in the context of augmented reality (AR) and virtual reality (VR) environments. As these technologies become more prevalent in various applications, including entertainment, education, and training, the ability to distinguish between real and manipulated content becomes increasingly important to ensure a seamless and authentic user experience.
Implications for Animations and Deepfake Technology
Deepfake technology, which uses artificial intelligence to create highly realistic but fake audio-visual content, has raised concerns about its potential misuse for spreading misinformation and manipulating public opinion. By developing advanced detection systems like the one proposed in this paper, researchers and practitioners can stay ahead of the curve and proactively address the challenges posed by deepfake animations.
Conclusion
In conclusion, the integration of audio and visual analysis in detecting fake audio-visual content represents a significant advancement in the field of multimedia information systems. This research sets the stage for future developments in combating deepfake technology and emphasizes the importance of a multi-disciplinary approach to address complex challenges in digital media manipulation.