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Various methods used to detect child sexual abuse material, including hashing, machine learning classifiers, and detectors.
Platforms combine signature-based matching and machine learning to separate illegal child sexual abuse material (CSAM) from broader categories like child erotica, but each method has brittle edges: ha...
Major tech firms and safety vendors use a hybrid of legacy hash-matching, perceptual/video hashing, and machine-learned classifiers—augmented by proprietary intelligence and red-teaming—to find AI-gen...
Platforms use a layered technical toolkit—cryptographic and perceptual “” of known images, machine‑learning classifiers for sexual content and age estimation, and newer detectors trained to spot synth...
Investigators combine established forensic collection—disk and mobile imaging, metadata extraction, and —with automated detection (hash matching) and newer to identify who viewed or distributed (CSAM)...
AI companies generally follow a common playbook when they encounter child sexual abuse material (CSAM): detect with automated tools, remove and preserve evidence, and submit reports to the National Ce...
End‑to‑end encryption (E2EE) substantially reduces platforms’ technical ability to scan message content for known child sexual abuse material (CSAM), meaning traditional server‑side hashing and AI det...