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What platforms are trying to detect
CSAM is illegal material that documents or depicts the sexual abuse or sexual exploitation of a child. Platforms generally distinguish between content that matches a trusted record of known CSAM, content that an automated system suspects may be new CSAM, and material that has been confirmed by trained reviewers under the service’s policy and applicable law. That distinction matters: an automated score or a report is an investigative lead, not a legal finding.
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Detection can cover uploaded photos and videos, files shared in messages, text, account behavior and signals associated with online enticement or grooming. Whether a service can scan a particular surface depends on its architecture, terms, local law and use of end-to-end encryption.
How hash matching finds known material
Cryptographic hashes identify exact files
A cryptographic hash converts a file into a fixed-length string of numbers and letters. If two files are byte-for-byte identical, they produce the same hash, so a platform can compare an upload with hashes supplied by trusted organizations such as the National Center for Missing & Exploited Children (NCMEC) and the Internet Watch Foundation (IWF). The hash is a fingerprint, not a copy of the image: it is not reversible into the underlying content.
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This method is fast and has a very low chance of treating unrelated files as the same file, but even a small change to the file—such as resizing, cropping or re-encoding—normally produces a different cryptographic hash.
Perceptual hashes recognize altered copies
Perceptual hashing creates a fingerprint based on visual characteristics rather than every byte. A service can therefore compare an image with a known CSAM record even when the copy has been resized, cropped, recompressed or otherwise transformed. Ofcom says perceptual matching is more likely to detect a larger amount of CSAM than cryptographic matching, which is why its codes recommend perceptual hash matching for high-risk file-sharing and file-storage services.
Perceptual matching is not infallible. Similar-looking legitimate images can produce near matches, so platforms set thresholds and send uncertain results for human review. A hash database also has a fundamental boundary: it cannot identify genuinely new material until that material has first been assessed and its fingerprint added to a trusted list.
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Repair common Windows errors and clear accumulated junk for a smoother, more stable PC - no reinstall needed.Free scan · no reinstall| Method | Best at detecting | Important limitation |
|---|---|---|
| Cryptographic hash | Exact copies of a known file | Fails when the file changes, even slightly |
| Perceptual hash | Visually similar copies after resizing, cropping or recompression | Similarity thresholds can create false positives and still do not reveal entirely new material |
| AI or machine-learning classifier | Patterns associated with novel CSAM, grooming or online enticement | Produces a risk score, not proof; requires safeguards and human confirmation |
How AI adds coverage for new material
Machine-learning classifiers examine visual, video, text or behavioral features that may be associated with CSAM, grooming or online enticement. The European Commission describes these systems as complementary to hash matching: hashes cover known material, while classifiers can generate leads about material that has not yet been fingerprinted. A classifier may flag a probability or risk score; it does not establish that a crime has occurred.
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Models can make errors because context is difficult to infer from pixels, language or behavior. A legitimate family photograph, an educational discussion or an ambiguous message can resemble a pattern in training data. For that reason, a responsible process uses confidence thresholds, specialist review, documentation of decisions and a way to challenge an erroneous action. Models also need ongoing evaluation as attackers alter files, language and distribution methods.
The detection-to-removal workflow
The exact order differs by service, but a typical process has six stages.
- Ingest and scan. When a user uploads a file, or sends content on a surface the provider is legally and technically able to inspect, the platform can calculate hashes and compare them with trusted databases. Some services also run classifiers at upload time or in periodic scans. Google has said that approximately 90% of imagery it reported matched previously identified CSAM, much of it in the NCMEC database; that is a company figure for its reporting, not a universal rate for every service.
- Classify novel material. AI systems score content and related signals for possible new CSAM, grooming or enticement. The score creates a queue or alert for investigation. It should not be treated as a final determination by itself.
- Conduct specialist human review. Trained reviewers examine the available evidence, apply the platform’s rules and consider the applicable law. Google describes teams with expertise in law, child safety, social work and cyber investigations. The European Commission calls for human oversight, verification, accurate decisions and appeal mechanisms when automated tools are used. Services also need procedures to protect reviewers from repeated exposure to traumatic material.
- Enforce the service’s rules. Confirmed CSAM is removed or blocked. The account may be suspended, and attempts to upload a matching file can be stopped before publication. Removal does not erase copies already held by other services or devices.
- Report to authorities where required. US providers report apparent CSAM to NCMEC’s CyberTipline. NCMEC evaluates reports and may refer them to the relevant law-enforcement agency. NCMEC’s 2024 REPORT Act guidance also addresses indicators of online enticement and child sex trafficking. Reporting duties and procedures vary by jurisdiction.
- Add fingerprints to prevent re-upload. Once material is confirmed, a new hash can be added to trusted lists shared with participating services. Future copies can then be detected by matching systems, subject to the participating service’s coverage and legal authority.
What happens after a flag
A platform normally restricts access to the suspected item while it investigates, but the visible action can vary from immediate blocking to removal after review. It may preserve relevant account and technical information under its legal-retention procedures, remove related posts, disable sharing and suspend an account. A report to NCMEC or another authority is separate from the platform’s internal decision; a report does not mean that a court has already found the user guilty.
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Why encryption changes what can be detected
On a public page or an unencrypted file-sharing service, a provider can generally run hash comparisons and classifiers before content is displayed. End-to-end encrypted services are different: the provider may not possess the readable file or message, so it cannot perform the same server-side scan. Encryption is not evidence that abuse is occurring, but it limits a platform’s visibility and therefore the preventive tools it can deploy. A detection statistic from a public service should not be presented as a measure of all CSAM on the internet.
Services can still receive user reports, metadata or information from a device, depending on the product design and law. Those channels do not make encrypted systems equivalent to services that can inspect content centrally.
How large are the known-content databases?
| Figure | Source and date | What it means |
|---|---|---|
| More than 14.6 million files | NCMEC, 2025 | Files NCMEC says it labelled during 2025 |
| More than 77.1 million files | NCMEC, 31 December 2025 | NCMEC’s cumulative labelled-file count by that date |
| Approximately 90% of reported imagery | Google, 2022 | The company said most imagery it reported matched previously identified CSAM |
| Over four billion images processed in 30 days | Google, 2022 | A dated company statement about throughput for partners using its Content Safety API, not a current or universal benchmark |
These figures are not directly comparable: one counts labelled files, another describes the share of a company’s reports, and another measures processing volume for a particular service and period. Counts also change as new cases are identified, duplicates are handled and reporting rules evolve.
Regulation and oversight
European Union
The European Commission recommends proactive detection and removal, easy notice-and-action systems, trusted flaggers, human oversight and safeguards for fundamental rights and data protection. Under the Digital Services Act framework, national authorities may order platforms operating in the EU to remove illegal content where national law gives them that power. The Commission’s proposed EU-centre model would maintain a database of hashes and AI indicators, receive company reports and support detection that is lawful and transparent.
United Kingdom
The Online Safety Act 2023 places duties on regulated services to prevent users encountering priority illegal content, reduce risk and minimize the time CSAM remains available. Ofcom opened an enforcement programme on 17 March 2025. Its recommended codes include perceptual hash matching for high-risk file-sharing and file-storage services. The recommendations do not mean every service has identical scanning capability or that a match alone proves criminal liability.
United States reporting
US providers use the NCMEC CyberTipline for reports of apparent CSAM and related indicators. NCMEC reviews incoming reports and can refer them to law enforcement. A provider’s report may contain technical and account information needed for investigators; what is available depends on the service, retention rules and the law governing the request.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to judge a platform’s detection system
Marketing claims such as “AI-powered” do not tell you whether a service is safe or accurate. The meaningful comparison questions are:
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1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errors- Known-content coverage: Which trusted hash sources are used, and are both cryptographic and perceptual methods supported?
- Novel-content performance: How are classifiers evaluated for new or synthetic material, and how often are models updated?
- False-positive controls: Is there specialist human confirmation, documented quality assurance and an appeal route?
- Speed: How long can a suspected file remain visible between upload, review and removal?
- Surface coverage: Does scanning include public posts, private messages, file storage and live features, or are some areas encrypted or otherwise inaccessible?
- Reporting quality: Are reports complete, timely and routed to the correct authority?
- Privacy and legal basis: What data is inspected, how is it retained and what safeguards apply to children and other users?
- Reviewer welfare: Does the provider limit exposure, provide psychological support and audit reviewer decisions?
High recall is not enough if a system wrongly removes lawful content or provides no appeal. Conversely, a low false-positive rate is not enough if known copies are missed. Effective programs balance detection, accuracy, speed, due process and privacy.
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What a victim or trusted adult can do about re-uploads
Take It Down
NCMEC’s Take It Down service is designed for a person who was under 18 when the image or video was made. It creates a hash on the person’s own device without sending the image itself to the service. Participating public or unencrypted services can scan for matches and block or remove them under their procedures. The service cannot guarantee discovery everywhere and cannot scan encrypted platforms.
- Use the original device containing the image or video and follow Take It Down’s instructions to generate the hash locally.
- Do not download, forward or deliberately share the material in order to submit it.
- Understand that a hash only helps participating services find matching copies; it does not remove copies from every site or device.
- If you need help, or if the image is connected to abuse or exploitation, contact NCMEC’s CyberTipline rather than circulating the file.
People who were adults when an image was made may need a platform’s own reporting route or another service appropriate to their jurisdiction. In every case, preserve safety and privacy, avoid re-sharing the image and seek help from a trusted adult, child-protection organization or law-enforcement agency when there is an immediate risk.
What platforms still cannot promise
- Complete prevention: Hashes do not find material that has never been identified, and no classifier detects every new or synthetic file.
- Universal removal: A platform can act only within its own systems and the services that participate in a hash-sharing program.
- Perfect accuracy: AI and perceptual matching can misclassify content, so human review and appeals remain essential.
- Instant action everywhere: Review queues, legal requests, time zones and encrypted surfaces can delay or prevent provider action.
- A substitute for investigation: A flag or removal is not a judicial finding. Authorities must evaluate evidence under the law that applies to the case.
The strongest approach is layered: hash matching stops known copies, AI supplies leads about novel material, trained people verify decisions, enforcement limits further distribution, and reporting enables child-protection and law-enforcement action. Encryption, incomplete databases, model error and cross-platform duplication mean that this system reduces harm without eliminating the need for reporting, investigation and support for victims.
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