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Ideas:
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This paper proposes an AI-driven parental control system operating at the network edge. Traditional methods, like keyword blocking, are easily circumvented. Our system uses AI to analyze network traffic, identifying harmful content based on text, images, and context. This allows for more robust and adaptive protection, minimizing restrictions on legitimate content. We discuss the system architecture, AI training, and evaluation metrics, while also addressing ethical and privacy considerations. Our findings show the potential of AI to enhance online child safety and provide parents with greater peace of mind.
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