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Google DeepMind and Singapore GovTech Deliver Gemma Model for On-Device Scam Detection

4 reports 4 reporting sources 3 days agoNew

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Google DeepMind said it and Singapore’s Government Technology Agency (GovTech) have delivered a Gemma-based scam detection model optimized for typical smartphones in Singapore, although its accompanying X post described the work as still being built. The model was fine-tuned on local scam conversations to flag suspected manipulation while processing conversations on the device. A lightweight classifier first screens incoming messages, then the model evaluates the broader conversation context and triggers warnings. Google DeepMind said fine-tuning took place in GovTech’s managed Google Cloud Platform environment, with training data kept isolated. Training excluded private or personally identifiable information and used anonymized datasets derived from scam reports and intelligence feeds. The model was tested against synthetic dialogues and local corpora covering different scam categories and evolving impersonation tactics.

Generated by AI from reports · Updated 3 days ago

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10/14
  1. Over half of reported crimes in Singapore use sophisticated social engineering. With @GovTechSG, we’re building on-device fraud detection using our open model Gemma. By fine-tuning it on local scam tactics, it identifies threats in real time without compromising personal privacy → https://goo.gle/4ygVkJa

    Over half of reported crimes in Singapore use sophisticated social engineering. With @GovTechSG, we’re building on-device fraud detection using our open model Gemma. By fine-tuning it on local scam tactics, it identifies threats in real time without compromising personal privacy → https://goo.gle/4ygVkJa

  2. Google DeepMind and Singapore GovTech deliver on-device Gemma model for scam detection

    Google DeepMind and Singapore’s Government Technology Agency (GovTech) have delivered a Gemma model fine-tuned on local scam conversations and optimized for typical smartphones in Singapore. A lightweight classifier screens incoming messages before the model evaluates broader conversation context and triggers warnings about suspected manipulation, with conversation processing kept on the device. Training excluded private or personally identifiable information, using anonymized datasets derived from scam reports and intelligence feeds.

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