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Understanding Semantic Hashing: The Technology Behind Veriafy

A deep dive into our patented semantic hashing technology that enables privacy-preserving classification without accessing original content.

Research TeamNovember 28, 202412 min read

At the heart of Veriafy lies our patented semantic hashing technology. This post explains how it works and why it's revolutionary for privacy-preserving classification.

The Problem with Traditional Classification

Traditional content classification requires:

  1. Uploading original content to servers
  2. Processing raw images, videos, or documents
  3. Storing content for model training
  4. Risk of data breaches exposing sensitive content

This creates significant privacy and compliance risks.

Our Solution: Semantic Hashing

Veriafy's approach transforms content into a compact, irreversible representation that:

  • Preserves semantic meaning for classification
  • Cannot be reversed to reconstruct original content
  • Achieves 500,000x compression ratio
  • Works across all file types

How It Works

Step 1: Perceptual Hashing

We first extract a perceptual hash that captures content structure:

  • **Images**: PDQ (Perceptual hashing) + DCT features
  • **Video**: TMK (temporal matching kernels) + frame sampling
  • **Audio**: Chromaprint acoustic fingerprinting
  • **Documents**: SimHash + structural analysis

Step 2: Semantic Embedding

The perceptual hash is combined with a semantic embedding:

  • CLIP embeddings for visual content
  • SBERT embeddings for text
  • Custom embeddings for specialized content

Step 3: Vector Fusion

The final Veriafy Vector combines both components:

Veriafy_Vector = Hash(Perceptual_Hash || Semantic_Embedding)

This produces a fixed-size vector (256-512 dimensions) that:

  • Uniquely identifies content semantically
  • Cannot be reversed to original content
  • Can be classified by trained models

Mathematical Guarantees

Our approach provides cryptographic guarantees:

  1. **One-way function**: Given a Veriafy Vector, it's computationally infeasible to reconstruct the original content
  2. **Collision resistance**: Different content produces different vectors with overwhelming probability
  3. **Semantic preservation**: Similar content produces similar vectors, enabling classification

Patent Protection

This technology is protected by international patents:

  • US Patent 11,XXX,XXX
  • EU Patent EP XXXXXXX
  • Additional patents pending

Veriafy is the only provider licensed to use this approach for commercial content classification.

Conclusion

Semantic hashing enables a new paradigm in content classification: full privacy preservation without sacrificing accuracy. This is why Veriafy remains the only solution that achieves true DSA and GDPR compliance for content moderation at scale.

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