Towards Trustworthy AI: Secure Deepfake Detection using CNNs and Zero-Knowledge Proofs

TL;DR

This paper introduces TrustDefender, a framework combining CNN-based deepfake detection with zero-knowledge proofs to ensure privacy and security in real-time XR applications, achieving high accuracy and cryptographic efficiency.

Contribution

It presents a novel two-stage system integrating lightweight CNN detection with ZKP validation, addressing privacy, security, and computational challenges in deepfake detection for XR.

Findings

Achieves 95.3% detection accuracy on benchmark datasets.

Provides efficient cryptographic proof generation compatible with XR platforms.

Ensures privacy preservation without exposing raw user data.

Abstract

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.