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The hidden smartphone data that turns your photos into digital fingerprints

Technology is getting more advanced by the day, and this scary discovery shows you don’t really know how much information you’re carrying in your pocket.

Did you know smartphone cameras carry so much information, digital forensic teams are able to detect child exploitation material, digital forgeries and other crimes?

Every image you take with your smartphone has a unique pattern of noise which identifies every individual camera unit.

This “noise” is known as photo-response non-uniformity (PRNU), and is currently being used as valid proof in courts all around the world.

Your photos already carry a tonne of information. An image’s metadata records things like the aperture and exposure used, the picture’s location and the camera’s make and model.

But this PRNU noise is able to tell two cameras apart from the same phone model.

According to Dr Lei Pan, a lecturer at Deakin University’s School of Information Technology, tracking a smartphone camera is akin to a ballistics job of studying a firearm and working out whether the shot trajectory matches the crime scene.

It’s not yet clear if post-image processing, like Instagram filters or photoshop tweaks, affects an image’s unique PRNU. But PRNU does remain resilient against Facebook uploading, which strips the metadata from pictures as you upload them for privacy purposes.

However, if you think Facebook is the “safe” option, think again. In 2015, Facebook filed a patent for a technology which uses lens scratches, dirt and pixel flaws to help people find new friends.

So basically, if Facebook detects the same spec of dust in two separate photographs, the software assumes both snaps might have been taken by the same camera, therefore, those two people may know each other.

Freaky, hey?

About the author

Filmmaker. 3D artist. Procrastination guru. I spend most of my time doing VFX work for my upcoming film Servicios Públicos, a sci-fi dystopia about robots, overpopulated cities and tyrant states. @iampineros

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