A Multi-Level Strategy for Deepfake Content Moderation under EU Regulation
This work tackles the problem of enforcing deepfake transparency for online platforms under EU law, but it is incremental as it builds on existing methods without introducing new technical breakthroughs.
The paper addresses the challenge of deepfake content moderation under EU regulation, finding that individual methods are insufficient and proposing a multi-level strategy that combines existing methods for scalability and practicality.
The growing availability and use of deepfake technologies increases risks for democratic societies, e.g., for political communication on online platforms. The EU has responded with transparency obligations for providers and deployers of Artificial Intelligence (AI) systems and online platforms. This includes marking deepfakes during generation and labeling deepfakes when they are shared. However, the lack of industry and enforcement standards poses an ongoing challenge. Through a multivocal literature review, we summarize methods for marking, detecting, and labeling deepfakes and assess their effectiveness under EU regulation. Our results indicate that individual methods fail to meet regulatory and practical requirements. Therefore, we propose a multi-level strategy combining the strengths of existing methods. To account for the masses of content on online platforms, our multi-level strategy provides scalability and practicality via a simple scoring mechanism. At the same time, it is agnostic to types of deepfake technology and allows for context-specific risk weighting.