Google DeepMind和新加坡政府宣布保护公民免受骗局的新AI工具
合作提供端侧骗局检测模式，帮助保护公民而不损害隐私。
斯卡姆斯占新加坡举报罪行一半以上，自2019年以来损失超过40亿新元。 与此同时，骗局策略已经从简单的钓鱼转向复杂的社会工程，攻击者在索要钱财前用令人信服的谈话操纵受害者。 这种骗局现在占所有损失的80%。
为了解决这一问题，Google DeepMind和新加坡政府技术局(GovTech)一直在合作，作为我们与新加坡政府的国家AI伙伴关系的一部分。 这让我们可以提供端侧骗局检测模式
端侧人工智能保护而不损害隐私
通用模型往往会与本地化的欺骗技术发生斗争，使得骗局的侦测更加困难。 为了解决这个问题，Google DeepMind的轻量级Gemma模型在本地骗局对话上进行了微调。 其中包括公共机构、银行和地方人物的冒充，以及区域计划的趋势，确保这一模式能够区分良性的社会对话与真正的操纵。
这个新模型也处理设备上的对话数据。 传统上，威胁检测依赖于服务器侧扫描云中的信息，但许多消息平台是端到端加密的 - 这意味着它们不能检查私人对话。 人们需要一种方法来避免欺诈，同时保持谈话的隐私，我们的解决办法是把威胁分析带给设备。 这样可以更快地分析对话模式，以发现操纵意图，并允许公民在财务或信誉暴露发生之前采取行动。
头罩下：局部背景和边缘效率
当模型检测到操纵性谈话模式时，它会直接在用户的设备上触发实时警告，建议他们在共享敏感信息或资金之前暂停并进行谨慎。
在现代智能手机上运行一个复杂的骗局分类器模型并非易事，电池效率是一个关键关切领域。 要做到这一点，我们依靠两阶段管道：
- 第一阶段：一个轻量级、高效率的分类屏幕，使用最小的电源，集中处理潜在的威胁。
- 第二阶段：背景评估用我们微调的Gemma模型，在那里，我们分析一个更大的快照谈话确认一个威胁或丢弃一个假警报。
建立在严格治理基础上
在新加坡国际网络周(SICW 2026)上介绍，这一协作为在用户保护与端到端加密之间取得平衡的短信平台提供了一个模式。 在整个发展过程中都坚持严格管理：
主权沙盒：微调发生在GovTech管理和维护的GCP环境内，保持培训数据隔离。
没有公民数据：培训严格排除了私人或个人可识别的信息，使用从骗局报告和情报资料中得出的匿名数据集。
压力测试：该模式通过合成对话和地方公司测试，以涵盖各种骗局类别和不断演变的冒牌战术。
端侧安全的未来
该项目展示了AI如何帮助信息传递服务打击犯罪，显示安全或隐私需要通过直接运行保护AI端侧而受到损害。 它还是显示，政府和模型提供者如何合作，为公益建设技术。 信使平台和操作系统供应商可以直接集成这些轻量级模型来保护本地用户，确保聊天互动保持安全而不暴露私人数据。
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Google DeepMind and Singapore Government announce new AI tool to protect citizens against scams
The collaboration delivers an on-device scam detection model that helps protect citizens without compromising privacy.
Scams make up over half of Singapore’s reported crimes, and account for more than S$4 billion in losses since 2019. At the same time, scam tactics have shifted from simple phishing to complex social engineering, whereby attackers manipulate victims with convincing conversations before requesting money. Such scams now account for up to 80% of all losses.
To address this, Google DeepMind and the Government Technology Agency of Singapore (GovTech) have been working together as part of our National AI Partnership with the Singapore Government. This has allowed us to deliver an on-device scam detection model - optimized to run on typical smartphones in Singapore - to tackle an urgent safety challenge while preserving citizens' privacy.
On-device AI protection without compromising privacy
Generic models often struggle with localised deception techniques, making scam detection harder. To address this, Google DeepMind's lightweight Gemma model was fine-tuned on local scam conversations. These ranged from impersonations of public institutions, banks, and local figures, to trending regional schemes, ensuring the model can distinguish benign social chatter from genuine manipulation.
This new model also processes conversation data on the device. Traditionally, threat detection relies on server-side scanning of messages in the cloud, but many messaging platforms are end-to-end encrypted - meaning they cannot inspect private conversations. People need a way to stay safe from fraud at the same time as keeping conversations private, and our solution is to bring the threat analysis to the device. This allows faster analysis of conversation patterns to detect manipulative intent and allow citizens to act before financial or credential exposure can occur.
Under the hood: local context and edge efficiency
When the model detects manipulative conversational patterns, it triggers a real-time warning directly on the user's device advising them to pause and exercise caution before sharing sensitive information or funds.
Running a complex scam classifier model on modern smartphones is no easy task, with battery efficiency being a critical area of concern. To do this we're relying on a two-stage pipeline:
- Stage 1: A lightweight, efficient classifier screens inbound messages, using minimal power, focused on catching potential threats.
- Stage 2: Contextual evaluation with our fine-tuned Gemma model, where we analyze a larger snapshot of the conversation to confirm a threat or discard a false alarm.
Built on strict governance
Presented at Singapore International Cyber Week (SICW 2026), this collaboration provides a model for messaging platforms balancing user protection with end-to-end encryption. Strict governance was maintained throughout development:
Sovereign Sandbox: Fine-tuning took place within GovTech's managed and maintained GCP environment, keeping training data isolated.
No Citizen Data: Training strictly excluded private or personally identifiable information, utilising anonymised datasets derived from scam reports and intelligence feeds.
Stress-Testing: The model was tested against synthetic dialogues and local corpora to cover diverse scam categories and evolving impersonation tactics.
The future of on-device safety
The project demonstrates how AI can help messaging services counter criminality and shows safety nor privacy need be compromised by running protective AI directly on-device. It also shows how governments and model providers can work together to build tech for public good. Messaging platforms and operating system vendors can directly integrate these lightweight models to safeguard users locally, ensuring chat interactions remain secure without exposing private data.
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