arXiv:2606.03348v1 Announce Type: cross Abstract: Recent generative models can now produce visual artifacts with realistic embedded text and layouts, creating a new misinformation threat: synthetic credibility. We introduce SYNCRED-Bench, a benchmark of 600 AI-generated misinformation images balanced across six credible-form categories and seven fine-grained circulation styles, together with FP450, a real-image negative set for measuring false positives. Extensive evaluation shows that existing systems remain unreliable: under a 5% false-positive-rate constraint, 15 MLLMs achieve only 10.5% tr
The rapid advancement of generative AI models means they can now create highly convincing visual misinformation, necessitating immediate benchmarking to understand the new threat landscape.
The emergence of 'synthetic credibility' in AI-generated visual media poses a significant challenge to information integrity and public trust, with implications for media, cybersecurity, and societal stability.
The ability of generative AI to create believable text and layouts within images means traditional detection methods are insufficient, requiring new benchmarks and improved detection systems.
- · AI safety researchers
- · Misinformation detection platforms
- · Cybersecurity sector
- · Social media platforms relying on outdated moderation
- · Public trust in visual information
- · Generative AI companies if unchecked misuse intensifies
AI-generated visual misinformation becomes harder to detect at scale, increasing its prevalence.
Public skepticism towards all online visual content grows, eroding trust in legitimate news and information sources.
Governments and regulatory bodies respond with new legislation and enforcement to combat AI-driven disinformation, potentially impacting AI development and freedoms.
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Read at arXiv cs.AI