Thorn's new scalable solution helps protect
platforms from the risks of hosting child sexual abuse material
(CSAM) and text-based interactions that could lead to child sexual
exploitation (CSE)
LOS
ANGELES, July 22, 2024 /PRNewswire/ -- Thorn, a
nonprofit that builds technology to defend children from sexual
abuse, today announced the launch of Safer Predict, a
transformative AI-powered solution to help content-hosting
platforms proactively detect and mitigate the risks of hosting
child sexual abuse material (CSAM) and text-based interactions that
could lead to child sexual exploitation (CSE).
The National Center For Missing And Exploited Children (NCMEC)
received more than 36 million reports of suspected child sexual
exploitation to its CyberTipLine last year. Safer Predict
helps content-hosting platforms reduce the risks of hosting new or
previously unreported child sexual abuse material (CSAM) and child
sexual exploitation (CSE), including text-based material that may
indicate instances of threats that could lead to sexual harms
against children.
"Child safety risks are skyrocketing, and platforms need
solutions that can effectively scale protection for their users,"
said Julie Cordua, CEO of Thorn.
"Safer Predict gives platforms the power of Thorn's cutting-edge
child safety technology to identify new or previously unreported
CSAM and CSE across images, video, and text. This allows them to
take swift action, remove harmful content, and create a safer
digital environment for everyone."
Safer Predict leverages Thorn's advanced machine learning
classification models, trained on confirmed child sexual abuse data
– including the organization's CSAM classifier, which is trained in
part using trusted data from the NCMEC CyberTipline. This trusted
data enables Thorn's models to predict the likelihood that image
and video content contains CSAM. Safer Predict's text detection
models are also trained on confirmed messages related to child
sexual exploitation.
Thorn's brand-new CSE text classification model identifies
potential abuse based on the context of the conversation and allows
users to "stack" multiple labels that narrow down problem accounts
or quickly target abuse. Multiple language models examine the
context of complete conversations – line by line – and classify
text to predict possible instances of child sexual abuse and
exploitation, providing risk scores for CSAM, child access,
sextortion, self-generated content, and more.
Safer Predict offers highly customizable workflows that enable
platforms to develop strategic detection plans, prioritize
high-risk accounts, and expand their CSAM and CSE detection
coverage. The solution also streamlines content moderation
processes, empowering teams to conduct in-depth investigations of
pertinent results and report harmful material more efficiently.
Ahead of the broader launch of Safer Predict, Thorn partnered
with leading social media platform X to beta test the solution's
text-based detection capabilities. The text classifier within Safer
Predict proved highly effective at empowering X's content
moderation team to conduct more in-depth investigations of
pertinent results, leading to more comprehensive and prioritized
reports for NCMEC.
"Thorn's issue expertise and high-quality training data made us
eager to participate in the child sexual abuse text classifier
beta," said Kylie McRoberts, Head of
Safety at X. "Our team gained efficiencies in clearing out queues
by quickly finding actionable content. Deploying Safer Predict
helps us to build on our efforts to build a technology-first
approach to combating child sexual exploitation online,
specifically our goal of expanding our capabilities in fighting
high-harm content."
Safer Predict builds upon the impact of Safer, which has
processed more than 130 billion files and identified more than 5
million instances of potential CSAM on customer platforms since
2019.
Find Thorn at TrustCon on July 23
at the Hyatt Regency San Francisco to discuss how Safer Predict
makes the web safer.
About Thorn
Thorn is a nonprofit that builds
technology to defend children from sexual abuse. Founded in 2012,
the organization creates products and programs to empower the
platforms and people who have the ability to defend children.
Thorn's tools have helped the tech industry detect and report
millions of child sexual abuse files on the open web, connected
investigators and NGOs with critical information to help them solve
cases faster and remove children from harm, and provided parents
and youth with digital safety resources to prevent abuse. To learn
more about Thorn's mission to defend children from sexual abuse,
visit thorn.org.
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SOURCE Thorn