用轻量CNN和零知识证明实现实时深伪检测与隐私保护。
Towards Trustworthy AI: Secure Deepfake Detection using CNNs and Zero-Knowledge Proofs
- 轻量CNN实时分析扩展现实中的深伪图像。
- 95.3%检测准确率，零知识证明确保结果可验证不泄露数据。
- 适合对隐私和实时性要求高的沉浸式应用开发。
在合成媒体时代，深度伪造对信息真实性构成严重威胁。为应对这一挑战，我们提出TrustDefender，一种两阶段框架：(i) 轻量级卷积神经网络（CNN）实时检测扩展现实（XR）流中的深伪图像；(ii) 集成简洁的零知识证明（ZKP）协议，验证检测结果而不暴露原始用户数据。该设计兼顾了XR平台的计算约束与敏感场景下的严格隐私要求。在多个基准深伪数据集上的实验表明，TrustDefender实现了95.3%的检测准确率，同时具备高效证明生成能力，由严格密码学支撑，确保与高性能人工智能系统无缝集成。通过融合先进计算机视觉模型与可证明安全机制，本工作为沉浸式与隐私敏感应用中的可靠AI奠定了基础。
原文摘要 · Abstract (English)
In the era of synthetic media, deepfake manipulations pose a significant threat to information integrity. To address this challenge, we propose TrustDefender, a two-stage framework comprising (i) a lightweight convolutional neural network (CNN) that detects deepfake imagery in real-time extended reality (XR) streams, and (ii) an integrated succinct zero-knowledge proof (ZKP) protocol that validates detection results without disclosing raw user data. Our design addresses both the computational constraints of XR platforms while adhering to the stringent privacy requirements in sensitive settings. Experimental evaluations on multiple benchmark deepfake datasets demonstrate that TrustDefender achieves 95.3% detection accuracy, coupled with efficient proof generation underpinned by rigorous cryptography, ensuring seamless integration with high-performance artificial intelligence (AI) systems. By fusing advanced computer vision models with provable security mechanisms, our work establishes a foundation for reliable AI in immersive and privacy-sensitive applications.
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