Hash Matching

What is Hash Matching?

Hash matching is a content detection technique that generates a unique identifier (hash) for each image and compares it against databases of known harmful content. This allows platforms to quickly identify previously-identified illegal or harmful imagery without human reviewers needing to view the content.

Unlike traditional cryptographic hashes (MD5, SHA), perceptual hashes used in content moderation can identify images even after modifications like resizing, compression, or minor edits.

How Hash Matching Works

• Hash generation: Algorithm analyzes image features to create a fingerprint
• Database comparison: Hash is compared against known content databases
• Match detection: Similar hashes indicate matching or near-matching images
• Action taken: Matches trigger blocking, reporting, or review

Types of Image Hashes

• PhotoDNA: Microsoft's perceptual hash for CSAM detection
• pHash: Open-source perceptual hashing algorithm
• aHash: Average hash based on image luminance
• dHash: Difference hash comparing adjacent pixels

Advantages of Hash Matching

Hash matching enables platforms to detect known harmful content instantly without exposing moderators to traumatic material. It's computationally efficient, privacy-preserving (only hashes are shared, not images), and highly accurate for known content detection.