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AI Can Recognize Your Face Even If You’re Pixelated

tmikaeld 10y ago
This is based on having the original (unpixelated) image in every case, right?
paulpauper 10y ago
if you have the original font, blurring can be defeated . always better to just black out text that you don;t want to to be seen
evincarofautumn 10y ago
I’m not terribly surprised by this. Downsampling is a really effective way to create a “fingerprint” by which you can identify some data. And that’s essentially how an image-searching service works.
- userbinator 10y ago
  It also means that people with similar faces might get identified as the same person.
  - dogma1138 10y ago
    Which has always been a problem as both fingerprint identification and facial recognition are probability based, and often the confidence level is considerably lower than what anyone would be comfortable with.
deleted 10y ago
[deleted]
Houshalter 10y ago
I dont know about pixelation, but gaussian blur can be undone mathematically. Pixelation at least destroys the vast majority of the information.
- dogma1138 10y ago
  Depending on how it's pixelated, I seems that there is a trend of making more pixels because it looks cooler but also reduces the masking power of the process considerably. I would think that a 64 by 64 grid could be sufficient for pretty high level of confidence matching. And matching doesn't need to be perfect it often is far from it it needs to be good enough to provide a basis for elimination. It might even work with considerably lower resolutions because with video pixels aren't static and they change so you get considerably more data than what you would get out of a single frame, once you account for things like lighting and video compression you can get quite a bit of data out of it. This is not that dissimilar from analysing aerial or geospatial photography you can have features represented by a single pixel sometimes and you need to identify them with high enough confidence so you develop models based on how the pixels would "change" over time, or how would the effect nearby pixels at the end this what allows you to distinguish between a tank and an SUV or between a man holding a gun and a man holding a cat, these aren't perfect by any stretch of the imagination but we have put decades of work into this. I would assume that you can both extrapolate directly and indirectly apply a lot of those methods onto facial recognition of pixelated images.
forgotpwtomain 10y ago
Can journalists stop writing 'AI' everywhere when it's just Neural Nets? It's all starting to look ridiculous - if you need a popular science friendly word, what's wrong with Image Recognition Program?
- bonoboTP 10y ago
  More clicks this way. More clicks, more ad revenue. That simple.
Gaessaki 10y ago
Pixelating and blurring images has long been known to be insufficient in completely obscuring information [1]. In fact, in a lot of computer vision work, the image resolution is reduced to ignore noise and facilitate the workload for algorithms anyway. Completely destroying information through blacking out is preferable. [1] https://dheera.net/projects/blur https://dheera.net/projects/blur
chriswarbo 10y ago
Reminds me of https://en.wikipedia.org/wiki/Christopher_Paul_Neil https://en.wikipedia.org/wiki/Christopher_Paul_Neil

This is based on having the original (unpixelated) image in every case, right?

if you have the original font, blurring can be defeated . always better to just black out text that you don;t want to to be seen

evincarofautumn 10y ago

I’m not terribly surprised by this. Downsampling is a really effective way to create a “fingerprint” by which you can identify some data. And that’s essentially how an image-searching service works.

userbinator 10y ago
It also means that people with similar faces might get identified as the same person.
- dogma1138 10y ago
  Which has always been a problem as both fingerprint identification and facial recognition are probability based, and often the confidence level is considerably lower than what anyone would be comfortable with.

deleted 10y ago

[deleted]

Houshalter 10y ago

I dont know about pixelation, but gaussian blur can be undone mathematically. Pixelation at least destroys the vast majority of the information.

dogma1138 10y ago
Depending on how it's pixelated, I seems that there is a trend of making more pixels because it looks cooler but also reduces the masking power of the process considerably. I would think that a 64 by 64 grid could be sufficient for pretty high level of confidence matching. And matching doesn't need to be perfect it often is far from it it needs to be good enough to provide a basis for elimination. It might even work with considerably lower resolutions because with video pixels aren't static and they change so you get considerably more data than what you would get out of a single frame, once you account for things like lighting and video compression you can get quite a bit of data out of it. This is not that dissimilar from analysing aerial or geospatial photography you can have features represented by a single pixel sometimes and you need to identify them with high enough confidence so you develop models based on how the pixels would "change" over time, or how would the effect nearby pixels at the end this what allows you to distinguish between a tank and an SUV or between a man holding a gun and a man holding a cat, these aren't perfect by any stretch of the imagination but we have put decades of work into this. I would assume that you can both extrapolate directly and indirectly apply a lot of those methods onto facial recognition of pixelated images.

forgotpwtomain 10y ago

Can journalists stop writing 'AI' everywhere when it's just Neural Nets? It's all starting to look ridiculous - if you need a popular science friendly word, what's wrong with Image Recognition Program?

bonoboTP 10y ago
More clicks this way. More clicks, more ad revenue. That simple.

Gaessaki 10y ago

Pixelating and blurring images has long been known to be insufficient in completely obscuring information [1]. In fact, in a lot of computer vision work, the image resolution is reduced to ignore noise and facilitate the workload for algorithms anyway. Completely destroying information through blacking out is preferable. [1] https://dheera.net/projects/blur https://dheera.net/projects/blur

chriswarbo 10y ago

Reminds me of https://en.wikipedia.org/wiki/Christopher_Paul_Neil https://en.wikipedia.org/wiki/Christopher_Paul_Neil