EdgeAIGuard: Agentic LLMs for Minor Protection in Digital Spaces
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866915302917275648 |
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| author | Mujtaba, Ghulam Khowaja, Sunder Ali Dev, Kapal |
| author_facet | Mujtaba, Ghulam Khowaja, Sunder Ali Dev, Kapal |
| contents | Social media has become integral to minors' daily lives and is used for various purposes, such as making friends, exploring shared interests, and engaging in educational activities. However, the increase in screen time has also led to heightened challenges, including cyberbullying, online grooming, and exploitations posed by malicious actors. Traditional content moderation techniques have proven ineffective against exploiters' evolving tactics. To address these growing challenges, we propose the EdgeAIGuard content moderation approach that is designed to protect minors from online grooming and various forms of digital exploitation. The proposed method comprises a multi-agent architecture deployed strategically at the network edge to enable rapid detection with low latency and prevent harmful content targeting minors. The experimental results show the proposed method is significantly more effective than the existing approaches. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_00092 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | EdgeAIGuard: Agentic LLMs for Minor Protection in Digital Spaces Mujtaba, Ghulam Khowaja, Sunder Ali Dev, Kapal Computers and Society Artificial Intelligence Cryptography and Security Social media has become integral to minors' daily lives and is used for various purposes, such as making friends, exploring shared interests, and engaging in educational activities. However, the increase in screen time has also led to heightened challenges, including cyberbullying, online grooming, and exploitations posed by malicious actors. Traditional content moderation techniques have proven ineffective against exploiters' evolving tactics. To address these growing challenges, we propose the EdgeAIGuard content moderation approach that is designed to protect minors from online grooming and various forms of digital exploitation. The proposed method comprises a multi-agent architecture deployed strategically at the network edge to enable rapid detection with low latency and prevent harmful content targeting minors. The experimental results show the proposed method is significantly more effective than the existing approaches. |
| title | EdgeAIGuard: Agentic LLMs for Minor Protection in Digital Spaces |
| topic | Computers and Society Artificial Intelligence Cryptography and Security |
| url | https://arxiv.org/abs/2503.00092 |