arXiv:2606.11200v1 Announce Type: new Abstract: Generative AI has enabled the creation of photorealistic images and videos that are increasingly disseminated on social media, often used for spam, misinformation, manipulation, and fraud. Existing AI-generated content (AIGC) detection methods face challenges including poor generalization to new generation models, reliance on single modalities, and lack of interpretable explanations. We present our pipeline that mitigates these issues by continuously curating diverse multi-modal social media data and training a compact vision-language model for d
The rapid proliferation of sophisticated generative AI models and their misuse on social media necessitates advanced detection methods, with this research addressing current limitations.
Effective detection of AI-generated content is critical for combating misinformation, fraud, and maintaining trust in digital information, impacting social stability and platform integrity.
This research introduces a more robust and generalizable approach for identifying faked content across various modalities, making it harder for malicious actors to spread AI-generated disinformation.
- · Social media platforms
- · Truth verification organizations
- · Cybersecurity firms
- · General public
- · Misinformation networks
- · Fraudsters
- · Generative AI models (misused)
- · State-sponsored disinformation campaigns
Improved detection tools will make it harder to spread AI-generated misinformation at scale on social media platforms.
Increased trust in digital information could lead to more robust online discourse and reduced impact of disinformation campaigns.
An arms race between content generators and detectors could accelerate, leading to increasingly sophisticated AI techniques on both sides, potentially requiring regulatory intervention.
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Read at arXiv cs.CL