Using two equally important technologies – hash matching and artificial intelligence (AI) – Safer detects both known and unknown CSAM and recognizes text-based online conversations that indicate or could lead to child exploitation.
Cryptographic and proprietary perceptual hashing algorithms identify known CSAM in both image and video content. With access to a vast database aggregating 57.3 million hashes of known CSAM, Safer Match casts a wide net to detect and flag harmful content effectively.
Thorn's proprietary perceptual scene sensitive video hashing (SSVH) technique splits videos into scenes and frames to identify CSAM with precision.
Safer Predict’s advanced machine learning (ML) classification models detect new or previously unreported CSAM and child sexual exploitation behavior (CSE), generating a risk score to make human decisions easier and faster.
The Safer review tool is a content moderation UI with wellness features built in to help you reduce unneeded exposure to CSA content, while enabling you to review CSAM effectively.
Safer's reporting service provides a form UI to collect necessary data and connects to central reporting bodies in the US and Canada. In addition to packaging documentation, Safer’s reporting tool includes secure storage to preserve reported content.
Safer offers tools that enable cross-platform sharing of CSAM hash values. By enabling sharing, you can share your self-managed hash list of CSAM detected on your platform, either named or anonymously, with other Safer customers to help diminish the viral spread of harmful content.
A self-sustaining feedback loop that improves matches, increases accuracy, and provides continuous service improvements.
A set of self-managed hash lists your content moderation team can use to reduce re-review of CSAM and to support policy enforcement for sexually exploitative content.
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