Humans cannot reliably distinguish LLM-generated news from human-written news across multiple models, with domain expertise providing only modest help and fatigue reducing accuracy over time.
Blessing or curse? a survey on the impact of generative ai on fake news
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The proposed steganography-based attribution system with CLIP multimodal fusion achieves robust watermarking under distortions and 0.99 AUC-ROC for harm detection, enabling traceable AI content accountability.
This survey introduces the C5 Interaction Model as a unifying taxonomy to synthesize proactive detection methods for GenAI-enabled adversarial narratives across socio-technical and computational research streams.
citing papers explorer
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Can Humans Tell? A Dual-Axis Study of Human Perception of LLM-Generated News
Humans cannot reliably distinguish LLM-generated news from human-written news across multiple models, with domain expertise providing only modest help and fatigue reducing accuracy over time.
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Toward Accountable AI-Generated Content on Social Platforms: Steganographic Attribution and Multimodal Harm Detection
The proposed steganography-based attribution system with CLIP multimodal fusion achieves robust watermarking under distortions and 0.99 AUC-ROC for harm detection, enabling traceable AI content accountability.
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Generative AI and Digital Ecosystem Resilience: A Proactive Lifecycle-Based Survey
This survey introduces the C5 Interaction Model as a unifying taxonomy to synthesize proactive detection methods for GenAI-enabled adversarial narratives across socio-technical and computational research streams.