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Comment by tashbarg | Hacker News Reader

You can certainly not produce inversions. The data that is left in the hash is not enough to produce anything vaguely photorealistic.

However, you can fill the gaps and generate photorealistic photos that fit to the extremely reduced information you get from the hash. You are generating believable (as defined by the training data) photos that fit the hash.

That’s a huge difference.

Statements like yours are extremely dangerous. Without proper understanding of what GenAI can and can not do, people start relying on things that are not there.

Imagine your photorealistic inversion AI putting a mole or a wrinkle in the face of somebody without any foundation in the actual hash. Just because it fits better to the trained data. Explain that to the judge, when the person with just the right facial features sits in front of them.

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>Imagine your photorealistic inversion AI putting a mole or a wrinkle in the face of somebody without any foundation in the actual hash. Just because it fits better to the trained data.

Seeing as AI was trained on 99999999999999 images of 9999 people, if the image in question is of one of those people, it's well conceivable that the AI will implicitly ID the person and attach their corresponding mole. Or in other words, it's possible a good portion of PhotoDNA's database is in the AI training set, so in principle there are cases where the AI does know.

There are only 144 Bytes in a PhotoDNA hash and they are used to identify the whole picture. This is definitely not enough data to identify a face reliably.

The proposed AI does not identify people and it will not report that it "found" the person in the training data. It does not know. And it won't tell you.

Assume twins, one is in the training data, one isn't. The one in the training data has a scar, the other one does not. We "invert" a picture of the twin without the scar and who is not in the training set. As you explained, the resulting image will have the twin from the data set including a highly detailed picture of the scar. And for some reason, that is a good thing.

You are attributing more to this AI than it conceivably can do. Even going as far as finding an excuse for putting false or unfounded data.

It is tremendously important to make clear: most (if not all) of current AI technology is not fit for forensic analysis beyond guiding humans in their own analysis.

This modern narrative of people posting their opinions or assumptions somewhere being "dangerous" because someone could just believe it is much more dangerous because it can be applied to any opinion anywhere that was ever published.

No judge will ever rule on something based on a comment they read in the Internet.

Judges usually rely on experts in forensic science who, of course, are infallible and absolutely not influenced by what they read online during their day.

https://innocenceproject.org/misapplication-of-forensic-scie...

It is dangerous to push the narrative that GenAI can "put information back" where it was once removed. Especially dangerous, because most GenAI is built to put something there that is extremely believable. And while an innocent comment on HN might not play the biggest role, the linked project claims exactly what it can - by definition - not do ("a PhotoDNA hash can be used to produce thumbnail-quality reproductions of the original image") and it looks scientific, too.

You have already assumed that “judges” are somehow better suited to make such decisions than “regular people”, even though they are simply cogs in the wheels of social machines, and will mostly automatically approve anything up to mass murders if “general direction” of the society is like that. But it's convenient for you to believe that they have certain qualities.

Needless to say, when people are so brainwashed that they are ready to pray to actual machines, decisions of those machines won't be questioned. It would just be inconvenient.

To be clear, my use of the word “photorealistic” instead of “accurate” was very intentional.

Hold my beer: symmetric flip. Flip the photo horizontally and it’s essentially the “same” image without a hash collision

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