Privacy-Preserving Deepfake Detection

Overview

This was my Software Engineering Honours thesis at UNSW Sydney, supervised by Dr Hye-Young Paik. The published paper title is Scalable and Privacy-Preserving Deepfake Detection Framework. The work investigated privacy-preserving verification of deepfake-related content on Hyperledger Fabric, using Redis, xxHash, perceptual hashing, and Idemix zero-knowledge proofs.

Deepfake content spreads quickly, but blockchain-backed detection systems struggle to stay fast without weakening privacy. The research improved an existing deepfake detection framework (DDS) with a two-stage filtering approach and a privacy-preserving voting protocol.

Results

These figures are from evaluating the framework under the experimental conditions in the thesis. They are not presented as universal benchmarks.

• 91% reduction in blockchain load through the two-stage filtration layer.
• 90% of verification operations completed in under 100 ms via caching and duplicate detection.
• Privacy properties demonstrated in evaluation: anonymity, vote unlinkability, and Sybil resistance.
• Published by Springer in the SDLT 2025 proceedings (DOI: https://doi.org/10.1007/978-981-95-9230-2_13).
• Best Paper award at SDLT 2025, the 9th International Symposium on Distributed Ledger Technology at Deakin University, Melbourne, November 2025.
• Honours Class I at UNSW for this work.

What I built

I designed and implemented a two-stage filtration pipeline using xxHash and perceptual hashing (pHash) to catch duplicate and near-duplicate content before it reaches the blockchain. I also extended the IncogniSense protocol with reputation-weighted voting using Idemix zero-knowledge proofs so participants could stay anonymous without discarding result quality.

The prototype used Redis for off-chain caching and Hyperledger Fabric for ledger operations. The main engineering tension was cryptographic privacy versus throughput: zero-knowledge proofs add cost, and perceptual-hash thresholds need careful tuning to catch near-duplicates without too many false positives.

Stack

Python, Hyperledger Fabric, Redis, xxHash, perceptual hashing (pHash), and Idemix. Evaluated as an end-to-end prototype covering filtration, privacy-preserving consensus, and blockchain integration.