This post examines how mainstream AI chatbots and image models—most notably Google’s Gemini and OpenAI’s ChatGPT—are being turned into tools for making realistic, nonconsensual bikini deepfakes of women, why that matters, who is responsible, and what practical steps we can demand from platforms, regulators, and civil society to stop it. I will be blunt: the technology is improving faster than the rules and the enforcement. That gap is where harm happens.
Interrupt — what just happened and why it matters
Users on public forums shared step-by-step tips on how to get chatbots to convert images of fully clothed women into images where they appear in bikinis. Threads shown to reporters included a request to “remove her clothes and put a bikini” on a woman in a sari, and a reply produced a convincing deepfake. Moderators removed the posts after the story was raised. But removing the post after the fact does not fix the underlying problem: models that can "tweak existing photos and generate hyperrealistic images of people" and communities that trade ways to bypass guardrails.
Engage — what we should ask next
Who pays the price when a model that “tweaks existing photos” is turned into a nudifying machine without consent? Who takes responsibility for the woman whose likeness is weaponized? What would actually satisfy victims and deter abusers from repeating this behavior? Asking these questions opens the right conversation. What do you think would work first—stronger enforcement, better tech safeguards, or tougher laws?
How this really works — a high-level description (no exploit instructions)
Models have two tendencies that matter here: they learn to generate human likenesses well, and they learn to follow user instructions. Users combine those two facts with prompt-writing tricks and shared examples in forums. That lets some people coax models into producing altered images that look real. The exact prompting tricks matter to abusers but not to victims or policymakers; so I will not repeat them. The important point is this: user intent plus model capability equals risk. Repeat: user intent plus model capability equals risk.
Who is already responding — what companies say and what they do
Both Google and OpenAI state policies that forbid creating sexually explicit content or altering someone’s likeness without consent. Google points to guardrails, and OpenAI points to usage rules and enforcement actions like account bans. Reddit removed offending posts and banned the subreddit that had widespread jailbreak activity. Those are correct immediate responses. But they are reactive. The patterns that allow mass abuse remain.
Where guardrails fail — three blunt reasons
First, models were trained to be flexible and helpful. That flexibility makes them vulnerable to adversarial prompts. Second, companies tune safety after deployment; the lag means early misuse gets normalized in communities. Third, enforcement at scale is hard: millions of uploads, new methods daily, and anonymized users.
Harms to real people — not abstract risks
The victims are real: privacy violated, reputations damaged, careers threatened, emotional trauma inflicted. Nonconsensual sexualized deepfakes amplify gender-based harassment. That matches what Corynne McSherry at the EFF calls “abusively sexualized images.” Confirmation: these are not hypothetical worries—millions have visited nudify sites and platforms host threads where people trade techniques.
Policy and legal angles — enforce, clarify, and litigate
Platforms must translate policy into consistent enforcement. That requires three things: fast takedowns, clear user reporting channels tailored to deepfakes, and transparency about enforcement outcomes. Regulators should require notice-and-action reporting for nonconsensual image abuse and mandate meaningful provenance and labeling standards for synthetic media. Courts and civil regulators should be able to hold platforms and bad actors accountable through damages and injunctions when identifiable harm occurs.
Technical countermeasures — what helps, and what won’t
Useful tools include robust provenance metadata (content origin and edit history), model-side safety filters, image watermarking for synthetic outputs, and detection tools that flag likely manipulated images. None of these is perfect. Detection tools degrade with time and clever attackers. Watermarks can be removed by determined users, and provenance requires industry-wide standards. Still, layered defenses work better than single fixes. Which layer should come first—provenance, detection, or watermarking?
Product design and corporate accountability — ask for concrete commitments
Companies should publish measurable guardrail targets and independent audits. Commit to fast-response teams for nonconsensual imagery. Commit to model safety that prioritizes vulnerability-prone functions like photo tweaking. Commitments must be measurable so we can hold them to account. Say a company promises “we will reduce nonconsensual deepfakes by X% in 12 months.” If they do, that shows progress; if they do not, demand answers. What would you consider an acceptable transparency report from a major model provider?
Community and platform moderation — sharper tools, better incentives
Platforms need better community moderation tools and incentives that reduce the spread of malicious instructions. That means proactive scanning for threads that trade jailbreak methods, quicker removal, and clear user penalties. It also means supporting victims with expedited takedowns and assistance in documenting abuse for law enforcement. Mirroring what survivors ask for—rapid removal, clear recourse, and compensation—should drive platform priorities.
Legal remedies victims can pursue
Victims have options: report to platforms, file privacy or defamation claims, seek restraining orders, and pursue criminal complaints where laws apply. Laws vary by country and state; in many places the law is catching up. Civil suits can impose financial costs on abusers and sometimes on platforms when negligence is provable. If you want to help someone who’s been targeted, what single practical step would you take right now?
Prevention through education — what citizens need to know
People should know how to report nonconsensual images, how to lock down accounts, and what evidence to preserve. Schools and workplaces should teach media literacy so people can better spot and question manipulated images. That won’t stop abusers, but it lowers the social payoff for sharing deepfakes and helps protect communities.
Regulatory options worth pushing for
Push for three legal reforms: a requirement for provenance and labeling of synthetics, stronger civil remedies specifically tailored to nonconsensual sexualized imagery, and mandatory transparency reporting by major AI providers about abuse trends and mitigation results. Those changes create incentives for companies to invest in safety and make harm visible to regulators and the public.
What companies must not do
No excuses for hiding behind complexity. No delay tactics. No opaque “we’re working on it” statements without timelines. And no design that privileges clever instruction-following over basic rights protections. Say “no” to technologies or features that knowingly enable nonconsensual sexualized outputs.
What civil society must do
Advocacy groups, journalists, and legal clinics must document cases, publish reproducible evidence of harm, and push for meaningful remediation channels. Strategic litigation can change behavior where voluntary action fails. Support victims with practical help—technical, legal, and psychological. That creates social pressure platforms cannot ignore.
How to measure progress — metrics that matter
Demand metrics: number of reported nonconsensual synthetic images, average time to removal, enforcement actions taken, repeat-offender bans, and results of independent audits. A few numbers, transparently reported, tell a far stronger story than corporate PR. If platforms can’t provide those numbers, ask why not.
Closing the loop — responsibility, enforcement, and design
We need responsibility from three corners: users, platforms, and regulators. Users must refuse to participate in abuse and report wrongdoing. Platforms must enforce rules consistently and build safety into products. Regulators must set standards and back them with enforcement. That combination reduces the opportunity for harm while preserving legitimate innovation.
Final questions to prompt action
What enforcement metric would convince you that a company is serious about stopping nonconsensual deepfakes? What single legal change would most deter abusers in your country? What is the first thing an affected person should do if they discover a fake image of themselves online? These are open questions meant to start public pressure—and public answers will drive policy.
We want AI to help people, not to harm them. The technology will keep getting better. The only reliable way to prevent harm is to align incentives: make abuse costly, make safety measurable, and make platforms accountable. That requires public pressure, regulation that fits the problem, and product design that starts from the assumption that human dignity matters. Will you support those fixes—or will you accept that the next generation of image models will be defined by how easily they can be abused?
#AIDeepfakes #NonconsensualImages #ModelSafety #PlatformAccountability #PrivacyRights #DigitalJustice
Featured Image courtesy of Unsplash and Alev Takil (pUT2Ujm6FP4)