SocialDF: Benchmark Dataset and Detection Model for Mitigating Harmful Deepfake Content on Social Media Platforms
This addresses the security challenge of misinformation from deepfakes on social media platforms, representing an incremental advancement with a novel method for a known bottleneck.
The authors tackled the problem of detecting harmful deepfakes on social media by introducing SocialDF, a curated dataset reflecting real-world challenges, and a novel LLM-based multi-factor detection approach that combines facial recognition, speech transcription, and multi-agent LLM verification; their method achieved improved detection accuracy, though no specific numbers were provided in the abstract.
The rapid advancement of deep generative models has significantly improved the realism of synthetic media, presenting both opportunities and security challenges. While deepfake technology has valuable applications in entertainment and accessibility, it has emerged as a potent vector for misinformation campaigns, particularly on social media. Existing detection frameworks struggle to distinguish between benign and adversarially generated deepfakes engineered to manipulate public perception. To address this challenge, we introduce SocialDF, a curated dataset reflecting real-world deepfake challenges on social media platforms. This dataset encompasses high-fidelity deepfakes sourced from various online ecosystems, ensuring broad coverage of manipulative techniques. We propose a novel LLM-based multi-factor detection approach that combines facial recognition, automated speech transcription, and a multi-agent LLM pipeline to cross-verify audio-visual cues. Our methodology emphasizes robust, multi-modal verification techniques that incorporate linguistic, behavioral, and contextual analysis to effectively discern synthetic media from authentic content.