A Multi-Level Strategy for Deepfake Content Moderation under EU Regulation

TL;DR

This paper proposes a scalable, multi-level strategy for deepfake detection and labeling that aligns with EU regulations, addressing current method limitations and enhancing practical content moderation.

Contribution

It introduces a novel multi-level approach combining existing methods with a simple scoring system for effective deepfake moderation under EU rules.

Findings

Individual methods are insufficient for regulatory compliance

The multi-level strategy improves scalability and effectiveness

The approach is technology-agnostic and adaptable to context

Abstract

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.