Social platforms distribute information at unprecedented speed, which in turn accelerates the spread of misinformation and threatens public discourse. We present FakeZero, a fully client-side, cross-platform browser extension that flags unreliable posts on Facebook and X (formerly Twitter) while the user scrolls. All computation, DOM scraping, tokenisation, Transformer inference, and UI rendering run locally through the Chromium messaging API, so no personal data leaves the device.FakeZero employs a three-stage training curriculum: baseline fine-tuning and domain-adaptive training enhanced with focal loss, adversarial augmentation, and post-training quantisation. Evaluated on a dataset of 239,000 posts, the DistilBERT-Quant model (67.6 MB) reaches 97.1% macro-F1, 97.4% accuracy, and an AUROC of 0.996, with a median latency of approximately 103 ms on a commodity laptop. A memory-efficient TinyBERT-Quant variant retains 95.7% macro-F1 and 96.1% accuracy while shrinking the model to 14.7 MB and lowering latency to approximately 40 ms, showing that high-quality fake-news detection is feasible under tight resource budgets with only modest performance loss.By providing inline credibility cues, the extension can serve as a valuable tool for policymakers seeking to curb the spread of misinformation across social networks. With user consent, FakeZero also opens the door for researchers to collect large-scale datasets of fake news in the wild, enabling deeper analysis and the development of more robust detection techniques.

翻译：社交平台以前所未有的速度传播信息，这反过来加速了虚假信息的扩散，威胁公共讨论。我们提出FakeZero，一个完全在客户端运行、跨平台的浏览器扩展，可在用户滚动浏览时实时标记Facebook和X（原Twitter）上的不可靠帖子。所有计算、DOM抓取、分词、Transformer推理和UI渲染均通过Chromium消息API在本地执行，因此个人数据不会离开设备。FakeZero采用三阶段训练方案：基于焦点损失、对抗性增强和后训练量化的基线微调与领域自适应训练。在包含239,000条帖子的数据集上评估，DistilBERT-Quant模型（67.6 MB）达到97.1%的宏F1分数、97.4%准确率和0.996的AUROC，在普通笔记本电脑上的中位延迟约为103毫秒。内存高效的TinyBERT-Quant变体在将模型压缩至14.7 MB并将延迟降低至约40毫秒的同时，保持了95.7%的宏F1分数和96.1%的准确率，这表明在严格资源限制下，仅以轻微性能损失即可实现高质量的虚假新闻检测。通过提供内联可信度提示，该扩展可作为政策制定者遏制社交网络虚假信息传播的有力工具。在用户同意的前提下，FakeZero还为研究者收集大规模真实环境中的虚假新闻数据集开辟了途径，从而支持更深入的分析和更鲁棒的检测技术开发。