If Facebook already embeds user IDs in images (AI or no AI) I can only drool to think what kind tracking, advertising and mass surveillance opportunities are coming.
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itake
A watermark is not just “transparency.” It can reveal what tool someone used, how they work, or that an image came from a stigmatized platform. In sensitive contexts—politics, sexuality, medical issues, protest material, or private expression—that can become surveillance.
This is a great statistical analysis and it was a pleasure to read, but I wasn't expecting the claims to be so poorly supported.
There's also a reply from one of the Meta authors there, worth checking out.
kamranjon
How common is it for peer reviewed papers like this to be so far off their claimed findings?
“According to Google's peer-reviewed and published paper, they claim to have a true positive rate (TPR) above 99.97% -- meaning that they will miss their own watermarks less than 1 in 10,000 times. However, my own empirical testing found that is it much closer to 1 in 20.”
Related to this, the EU AI Act requires mandatory watermarking that is cannot be removed or is illegal to remove.
https://digital-strategy.ec.europa.eu/en/policies/eu-icons-l...
If Facebook already embeds user IDs in images (AI or no AI) I can only drool to think what kind tracking, advertising and mass surveillance opportunities are coming.
A watermark is not just “transparency.” It can reveal what tool someone used, how they work, or that an image came from a stigmatized platform. In sensitive contexts—politics, sexuality, medical issues, protest material, or private expression—that can become surveillance.
I am working on Saigon Watermarks: https://apps.apple.com/us/app/saigon-watermark/id6777061197 for detecting and removing provenence markers in AI.
The tool also removes c2pa markers, which google is now linking the device that took the photo with the photo.
scary stuff.
https://security.googleblog.com/2025/09/pixel-android-truste...
This is a great statistical analysis and it was a pleasure to read, but I wasn't expecting the claims to be so poorly supported. There's also a reply from one of the Meta authors there, worth checking out.
How common is it for peer reviewed papers like this to be so far off their claimed findings?
“According to Google's peer-reviewed and published paper, they claim to have a true positive rate (TPR) above 99.97% -- meaning that they will miss their own watermarks less than 1 in 10,000 times. However, my own empirical testing found that is it much closer to 1 in 20.”
also, easily bypassed now: https://twotensors.ai/