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Yes, this was a real 2019 experiment—but it was not a pornography project or a finished commercial AI product. Developer and internet-harassment activist Kelsey Bressler and a collaborator sought legal, consensual penis images to test an image filter that could help platforms block unsolicited nude pictures before recipients saw them.
What happened
In early September 2019, a Twitter account called showMeYourD asked people to submit legal, consensual photographs of penises. The images were intended as test material for a filter designed to recognize penis imagery.
Futurism reported that the project was associated with Kelsey Bressler, described as a developer and internet-harassment activist formerly connected with the anti-revenge-porn group Badass. Bressler built the filter with an unnamed collaborator.
The goal was straightforward: give people a way to filter unwanted nude images in direct messages and other online interactions without forcing them to close their messages entirely. The project was presented as a proof of concept that platforms such as Twitter could potentially use.
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Submissions arrived quickly enough that the team temporarily paused direct messages. It planned to evaluate the results, adjust the filter, and continue training it with existing libraries of penis images. Futurism’s report was updated on September 6, 2019.
What “penis-detecting AI” actually meant
In this context, “AI” meant an image-classification or filtering system intended to determine whether an image contained a penis. It was not described as a system that could identify the sender, recognize a specific person, determine whether an image was consensual, or decide whether conduct was illegal.
It was also not presented as a general-purpose pornography detector or a complete content-moderation platform. Detecting anatomy and detecting abuse are different tasks. A filter might flag an image, but it cannot independently know whether the recipient requested it, whether the sender is harassing someone, or what legal context applies.
The reported failure
The article reported that only one penis image initially passed through the filter. The apparent reason was that the penis was partly hidden behind a small metal cage.
That is an interesting example of a difficult image, not an accuracy score. The report does not provide the number of test images, a test-set design, false-positive or false-negative rates, precision, recall, or an independent evaluation. It would therefore be incorrect to call the filter 99 percent accurate, reliable, or ready for deployment.
Real-world image variation makes this problem harder than recognizing a clear, centered photograph. Lighting, cropping, distance, compression, obstructions, unusual angles, overlays, and multiple people can all affect classification. The cage example illustrates that a detector may fail when the relevant anatomy is only partly visible; the source does not establish how it performed on other edge cases.
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Why the project mattered
Unsolicited nude images are an online-safety and harassment problem. The proposed use was not simply to remove explicit material after publication. It was to screen incoming images before a recipient had to view them.
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That creates a meaningful user-control trade-off. An aggressive filter could block abusive images more effectively but also overblock consensual, educational, medical, artistic, or personally wanted material. A cautious filter would preserve more choice while allowing more unwanted images through. Useful product design would need review, release, deletion, and appeal controls—not just a binary classifier.
Consent did not answer every privacy question
The report says the requested submissions were legal and consensual. That is important, but it does not establish the full safeguards that would normally matter when collecting intimate images.
The available report does not say whether contributors signed formal research-consent forms, confirmed that they were adults, agreed to indefinite retention, could withdraw images, or were told about secondary uses. It also does not explain whether metadata was stripped, where the images were stored, who could access them, or whether they were deleted later.
Those omissions do not prove that the project mishandled the images. They do mean that “consensual submissions” should not be expanded into claims of anonymity, secure storage, ethical review, or permanent deletion.
What the experiment did not prove
- It did not prove that the filter could detect every penis image.
- It did not measure how often non-explicit images would be blocked.
- It did not show that the system could identify sexual harassment or nonconsensual imagery.
- It did not establish that Twitter adopted the technology.
- It did not establish a commercial launch, peer-reviewed study, or publicly released software.
- It did not solve revenge porn; at most, it addressed one possible layer of image screening.
What happened next?
The available reporting documents an early-September 2019 experiment, a flood of submissions, a temporary pause, and plans to refine the filter. It does not verify how many images were collected, what happened to them, whether submissions reopened, or whether the software was later deployed.
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The most accurate description is therefore a small, consent-based prototype aimed at a genuine online-harassment problem. Its reported result was preliminary and anecdotal, not a scientific validation or evidence of platform adoption.
Read the original Futurism report by Dan Robitzski.
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