Combating Online Misinformation Videos: Characterization, Detection, and Future Directions (2302.03242v3)

Abstract: With information consumption via online video streaming becoming increasingly popular, misinformation video poses a new threat to the health of the online information ecosystem. Though previous studies have made much progress in detecting misinformation in text and image formats, video-based misinformation brings new and unique challenges to automatic detection systems: 1) high information heterogeneity brought by various modalities, 2) blurred distinction between misleading video manipulation and nonmalicious artistic video editing, and 3) new patterns of misinformation propagation due to the dominant role of recommendation systems on online video platforms. To facilitate research on this challenging task, we conduct this survey to present advances in misinformation video detection. We first analyze and characterize the misinformation video from three levels including signals, semantics, and intents. Based on the characterization, we systematically review existing works for detection from features of various modalities to techniques for clue integration. We also introduce existing resources including representative datasets and useful tools. Besides summarizing existing studies, we discuss related areas and outline open issues and future directions to encourage and guide more research on misinformation video detection. The corresponding repository is at https://github.com/ICTMCG/Awesome-Misinfo-Video-Detection.

Overview of Video-Based Misinformation Detection: Characterization, Techniques, and Future Directions

The increasing prevalence of video consumption, with platforms like YouTube and TikTok attracting billions of users, underscores the emerging challenge of combating video-based misinformation. Videos not only amplify the virality of misinformation but also heighten its believability due to their multimodal nature. The paper entitled “Combating Online Misinformation Videos: Characterization, Detection, and Future Directions” presents a well-structured survey on this subject, providing a thorough analysis of the existing methodologies for detecting misinformation in online videos, while also offering insights for future research.

Characterization of Misinformation Videos

The paper first articulates a comprehensive characterization of misinformation videos. It categorizes the problem into three levels: signals, semantics, and intents. At the signal level, it examines the traces of manipulation that may manifest through digital signals due to either editing or generation procedures, with two primary methods of generation being editing and neural network-based synthesis. Semantically, misinformation videos often involve false semantic associations, either within a single modality or across disparate modalities. At the deepest level, the creator's intent—whether related to political, financial, or propagandist motives—plays a crucial role, significantly affecting user engagement patterns and social propagation methodologies.

Techniques for Misinformation Detection

The survey synthesizes various methodologies deployed across different layers of analysis:

1. Signal-level Detection: Strategies here are reminiscent of multimedia forensics, identifying digital signal traces resulting from editing (e.g., frame splicing) and generation (e.g., deepfake creation). Active detection involves pre-embedded identifiers like watermarks, while passive detection exploits intrinsic digital video characteristics such as compression artifacts or inter-frame inconsistencies.
2. Semantic-level Detection: Focusing on cross-modal semantics, these techniques attempt to uncover misleading semantic manipulation or alignments within video content and corresponding textual or audio information. Approaches often involve neural-based models to embed, compare, and contextualize semantic information across modalities.
3. Intent-level Detection: By exploiting intent-centric features drawn from social context, such as user engagement and uploader profiles, models can infer misleading intent. This approach leverages the propagation and engagement characteristics of videos among social networks to infer their veracity.

Moreover, techniques for integrating these clues are discussed, primarily via parallel methods (such as feature fusion) or sequential integration strategies. The authors also review several techniques for cross-modal correlation analysis, emphasizing the need to capture inconsistencies across video, text, and audio interactions.

Resources and Tools

Although the landscape for datasets remains sparsely populated, some significant contributions stand out, such as FVC, YouTubeAudit, and FakeSV, each facilitating research through varied instantiations of misinformation types from various platforms. Tools like deepfake detectors and reverse image searches are also notable resources, offering auxiliary data verification capabilities beyond direct model analysis.

Related Areas and Open Issues

The survey situates misinformation video detection within related domains like deception detection and harmful content identification. However, it draws a clear distinction based on the multimodal and dynamic nature of video misinformation. Notably, open issues like the transferability of models across diverse platforms and contexts, alongside challenges in explainability and cross-modal clue integration, suggest considerable areas for further exploration.

Future Directions

Looking ahead, the paper identifies promising avenues for advancements including improved inter-modality reasoning and tighter integration with recommendation systems to preemptively thwart misinformation propagation. Additionally, the emphasis on more granular analysis and explainability reflects a growing need for transparency in AI-driven solutions.

Conclusion

This survey paper effectively outlines the multifaceted challenges in detecting misinformation videos, presenting a cohesive framework that bridges current approaches with emerging opportunities for research. The comprehensive discussion from signal traces to social intents, coupled with an outlined pathway for future work, provides a robust foundation for academia and industry alike to build more resilient defenses against video-based misinformation.