Suspicious about a photo’s veracity? Rather than going DIY CSI, put the image to a common-sense sniff test: Was it posted by a reputable news outlet, for example, or by a site like the trickily named Abcnews.com.co?
Farid suggests running a reverse image search using Google or TinEye. Upload the Houston shark picture to TinEye, for instance, and the first link is to an article
on the Verge
about fake photos. Plug in a picture purporting to show planes underwater in Houston and you find it’s actually a digital mockup of what flooding could do to New York’s LaGuardia Airport (the skyline should have been a dead giveaway).
There is also Izitru
, founded by Farid, which can tell you if an image is a camera original; that is, if it has been modified at all. (This doesn’t always mean an image is fake, however, since it simply could have had its file format changed.)
So humans are lousy at detecting fakes. But surely computers, which created the doctored images to begin with, should be able to detect their own handiwork? They can, says Farid, but not in a way that is scalable, easy, or perfect. Such analysis, which interrogates both image data (in order to reveal modifications) and image geometry, usually requires the input of a human analyst who can assist the program in, say, specifying what is a shadow and what is a person.
To create a simple app that can easily detect fake images would require removing this human from the loop, Farid said. That would mean solving longstanding problems related to computer vision—essentially automating human visual systems. That isn’t easy. On top of that, image forensic techniques are scientific processes, dealing with physics, geometric, optics, and compression. The conclusion isn’t a simple thumbs up or thumbs down, real or fake. These results “require interpretation” from someone with expertise in digital photography, said Farid.
On top of that, Adobe—the creator of Photoshop—is a multi-billion dollar company with a financial incentive to create increasingly easy-to-use image modification tools that can achieve increasingly complex effects. Meanwhile, those working on the other end of things, creating applications and algorithms that detect fakes, hail mainly from academia. This imbalance exacerbates the already asymmetric nature of fake image detection.
“When you’re creating the fake, you only have to get it right once and it gets through,” said Farid. “I have to get it right every single time.”