Yes, the TinEye example doesn't seem like a great metric. TinEye is pretty great -- it can still match images after scaling and resolution changes, which this technique obviously foils (for now) -- but it's not trying to do facial recognition. I think this is simply beating TinEye's "perceptual hashing" technique, mostly just by flipping the picture, and has nothing at all to do with facial recognition.
A better test would be to load a few dozen pictures of Biden into Google Photos, name the face, and see if it correctly names their Biden example. I'd bet dollars to doughnuts it can.
I don't think this is affecting facial recognition at all. Google Photos can often recognize people in the far background, turned away, and out of focus, so I don't see how bit-flipping a few pixels will affect that.
----
Edit: Indeed, the TinEye result can trivially be achieved by flipping the image, as they do. Link to a search of a flipped photo of Obama it can't find. I contend this has zero application to "facial recognition," which is not what TinEye attempts to do.
A better test would be to load a few dozen pictures of Biden into Google Photos, name the face, and see if it correctly names their Biden example. I'd bet dollars to doughnuts it can.
I don't think this is affecting facial recognition at all. Google Photos can often recognize people in the far background, turned away, and out of focus, so I don't see how bit-flipping a few pixels will affect that.
----
Edit: Indeed, the TinEye result can trivially be achieved by flipping the image, as they do. Link to a search of a flipped photo of Obama it can't find. I contend this has zero application to "facial recognition," which is not what TinEye attempts to do.
https://tineye.com/search/b9d6f95346a7636bbd85e1bcfb931a9e82...