arXiv:2408.12558v1 Announce Type: new
Abstract: With the rapid development of deepfake technology, especially the deep audio fake technology, misinformation detection on the social media scene meets a great challenge. Social media data often contains multimodal information which includes audio, video, text, and images. However, existing multimodal misinformation detection methods tend to focus only on some of these modalities, failing to comprehensively address information from all modalities. To comprehensively address the various modal information that may appear on social media, this paper constructs a comprehensive multimodal misinformation detection framework. By employing corresponding neural network encoders for each modality, the framework can fuse different modality information and support the multimodal misinformation detection task. Based on the constructed framework, this paper explores the importance of the audio modality in multimodal misinformation detection tasks on social media. By adjusting the architecture of the acoustic encoder, the effectiveness of different acoustic feature encoders in the multimodal misinformation detection tasks is investigated. Furthermore, this paper discovers that audio and video information must be carefully aligned, otherwise the misalignment across different audio and video modalities can severely impair the model performance.
Multimodal Misinformation Detection in the Era of Deepfakes
The rapid development of deepfake technology has brought about significant challenges in detecting misinformation on social media platforms. Traditional methods of misinformation detection often focus on individual modalities like text or images, failing to comprehensively address the multimodal nature of social media data. In this context, a comprehensive multimodal misinformation detection framework has been proposed to address the diverse range of modalities that may appear on social media.
The framework employs neural network encoders for each modality, enabling the fusion of information from different modalities and facilitating effective multimodal misinformation detection. This is particularly crucial as social media data often incorporates information from various sources, such as audio, video, text, and images. Hence, by integrating corresponding neural network encoders for each modality, the framework ensures a holistic approach to misinformation detection.
This research paper focuses on the significance of the audio modality within the multimodal misinformation detection framework. By investigating the effectiveness of different acoustic feature encoders in the framework, the authors emphasize the importance of audio information in identifying fake content on social media. Additionally, the paper highlights the necessity of aligning audio and video information accurately. Failure to ensure alignment can severely impair the performance of the framework and compromise its ability to detect misinformation accurately.
The multidisciplinary nature of the concepts discussed in this paper is worth exploring. The study combines elements from multimedia information systems, animations, artificial reality, augmented reality, and virtual realities. With deepfake technology being a prominent aspect of the research, the paper touches upon the ethical and societal implications of misinformation in the digital age. The frameworks and methodologies developed in this study can have wide-ranging applications in various fields, including journalism, media, and cybersecurity.
In conclusion, the construction of a comprehensive multimodal misinformation detection framework that integrates various neural network encoders is a significant step towards combating deepfake-driven misinformation on social media platforms. By emphasizing the importance of the audio modality and the need for accurate alignment of audio and video information, this research contributes significantly to the development of effective detection methods in the era of deepfakes.