Online platforms today face an unprecedented challenge: protecting users from the circulation of Child Sexual Abuse Material (CSAM) at a scale never seen before.
With millions of new images and videos uploaded daily, manually reviewing content for CSAM is not only impractical but has become entirely unscalable.
For Trust & Safety professionals, this raises an important question: how do you effectively scan this massive volume of content to identify and prevent the spread of CSAM while still maintaining platform performance and user privacy?
This is where hash matching technology has emerged as the only truly scalable solution we have today for identifying and blocking known CSAM content. Hash matching works by converting these images and videos into unique digital fingerprints (or "hashes"), allowing platforms to instantly detect and remove previously identified CSAM at the point of upload, before it can be shared or distributed further.
For Trust & Safety professionals, understanding hash matching is critical, not just how it works, but how different hashing technologies compare, how they fit into a broader CSAM detection strategy, and how to integrate them effectively to enhance both user protection and legal compliance. In this guide, we’ll explore:
The fundamentals of hashing and how it helps detect CSAM
The differences between cryptographic and perceptual hashing
Real-world implementations and how leading platforms use hash matching
The role of AI-assisted detection in strengthening CSAM moderation
What is hash matching?
Think of a hash as a digital fingerprint. Just as each person's fingerprint is unique, a hash is a unique string of characters that represents digital content. When an image or video is "hashed," it's processed through a special mathematical function that converts it into this digital fingerprint. This process is similar to how your fingerprint can be converted into a unique pattern of lines and swirls that identifies you, but can't be used to recreate your actual finger.
What makes hashing particularly powerful for CSAM detection is that:
The same image will always produce the same hash, making detection reliable
The hash is a one-way process, you cannot reconstruct the original image from the hash
The hash is typically much smaller than the original file, making storage and comparison extremely efficient
Types of Hashing
There are two main types of hashing used in CSAM detection:
Cryptographic hashing: This creates an exact digital fingerprint where even a single pixel change in an image produces a completely different hash. It's perfect for detecting exact copies of known CSAM content but won't catch slightly modified versions.
Perceptual hashing: Think of this as a "fuzzy fingerprint" that captures the essential visual characteristics of an image. Just as you can recognize a person even if they change their hairstyle, perceptual hashing can identify CSAM content even if it's been slightly modified, resized, or had filters applied.
| Feature | Cryptographic hashing | Perceptual hashing |
|---|---|---|
Purpose
| Exact file matching
| Detecting altered CSAM
|
Change sensitivity
| Extremely sensitive (even one pixel change creates a new hash)
| Tolerant to minor changes
|
Detection scope
| Only exact duplicates
| Similar images/videos, even if resized or slightly edited
|
False positives
| Very low
| Slightly higher due to similarity-based matching
|
Common algorithms
| MD5, SHA-1, SHA-256
| PhotoDNA, pHash, aHash, dHash
|
Best for
| Database lookups of known CSAM
| Finding altered versions of CSAM
|
How Hash matching works in CSAM detection?
Hash matching works by comparing newly uploaded content against existing CSAM hash databases maintained by child protection organizations and law enforcement agencies. This process ensures that known CSAM cannot be re-uploaded, shared, or circulated, effectively breaking the chain of distribution.
The effectiveness of hash matching relies on a robust ecosystem of organizations working together. At the center are trusted organizations like the National Center for Missing and Exploited Children (NCMEC), the Internet Watch Foundation (IWF), and INHOPE. These organizations maintain secure databases of hashes from verified CSAM content.
The workflow typically follows these steps:
01.
Discovery
CSAM content is reported through various channels (by platform reports, law enforcement, and dedicated tiplines).
02.
Verification
Expert analysts at these organizations carefully review reported content to confirm it is CSAM and ensure false positives don't enter the database.
03.
Hashing
Confirmed CSAM content is converted into hashes using standardized algorithms.
04.
Database Integration
These hashes are added to secure databases, which platforms can access through APIs.
05.
Platform Implementation
When users upload content to a platform, it's automatically hashed and compared against these databases. If a match is found, the platform can take immediate action.
Why has hash matching become the gold standard for known CSAM detection?
Hash matching has become the most effective solution for known CSAM detection for several reasons. First, it's the only truly scalable way to process millions of uploads in real-time. Unlike manual review or other automated systems, hash matching can process content instantly without compromising accuracy.
Other significant advantages include:
Near-zero false positives when using cryptographic hashing.
Ability to detect modified versions of known content through perceptual hashing.
Detects CSAM without storing or viewing explicit content.
Rapid response times.
Cross-platform effectiveness through shared hash databases.
Beyond hash matching: Building a comprehensive CSAM protection system
While hash matching is the most reliable method for detecting known CSAM, it alone is not enough to combat the evolving threats of CSAM distribution. Newly generated or modified CSAM, along with grooming behaviors, require additional layers of protection.
To create a robust CSAM prevention framework, tech platforms should combine hash matching with AI-assisted detection and real-time moderation tools.
1. AI-assisted detection for new CSAM identification
AI models trained on vast datasets can detect newly created CSAM by identifying patterns in images, videos, and text, even if the content has never been hashed. They also recognize manipulated CSAM using perceptual hashing and deep learning, allowing them to detect altered versions of known material, even when bad actors attempt to evade detection.
Additionally, AI supports human moderators by filtering out low-confidence cases, ensuring that review teams can focus on critical violations.
Examples of AI-powered CSAM detection tools
Google content safety API: Uses AI to detect CSAM in images and videos, even if they have not been hashed.
Thorn’s Safer: AI-assisted detection system that analyzes media uploads and text-based conversations for signs of child exploitation.
ActiveFence: Provides AI-powered moderation and trust & safety intelligence to identify emerging CSAM threats.
2. CometChat’s moderation tools for real-time chat protection
CometChat provides a suite of moderation tools that help prevent CSAM circulation within messaging platforms, combining automated filtering, AI-powered analysis, and integration with CSAM detection tools.
Automated content moderation
Pre-built filters detect prohibited words, phrases, and media associated with grooming, exploitation, and CSAM.
AI-powered chat analysis
Machine learning models identify high-risk conversations and grooming behaviors that lead to CSAM creation.
Seamless integration with CSAM detection tools
Out of the box integration with hash-matching databases to block known CSAM media.
Real-time flagging & reporting
Instantly blocks, flags, or escalates messages containing potential CSAM for review.
Automated user actions
Bans, suspends, or reports users engaging in CSAM-related activities.
How CometChat works alongside hash matching & AI detection
| Feature | How It Helps in CSAM Prevention |
|---|---|
Hash matching integration
| Prevents the upload or sharing of known CSAM media.
|
AI-assisted chat moderation
| Detects grooming conversations and suspicious language.
|
Real-time media scanning
| Blocks harmful images, videos, and GIFs before they are seen.
|
Behavior monitoring
| Identifies repeat offenders and high-risk users.
|
Automated actions
| Blocks, warns, or reports users based on detected violations.
|
Strengthening your platform’s trust & safety with CometChat
Effectively combating CSAM requires a multi-layered approach that combines hash matching, AI-powered detection, and real-time chat moderation. While hash matching remains the most effective solution for detecting known CSAM, integrating AI-driven content moderation ensures that new and manipulated CSAM, as well as grooming behaviors, are proactively identified and mitigated.
If your platform enables user-generated content or real-time communication, ensuring a safe and compliant environment is not just a regulatory requirement, it’s a responsibility. CometChat helps you achieve this by providing:
AI-powered real-time chat moderation to detect and prevent grooming and exploitation.
Seamless integration with CSAM detection tools to automatically block known CSAM.
Customizable moderation workflows for automated actions like content flagging, user suspension, and reporting.
To learn how CometChat’s moderation tools can help you build a secure, compliant, and user-friendly platform, explore our solutions today.
Haris Kumar
Lead Content Strategist , CometChat