5 ms·

DeepPrivacy: A Generative Adversarial Network for Face Anonymization

135 points by baylearn 7y ago

baylearn 7y ago
link to the arxiv paper: https://arxiv.org/abs/1909.04538 https://arxiv.org/abs/1909.04538
coolspot 7y ago
Would be useful for amateur adult video sharing. Where face privacy is important, but blur kills the joy.
- mrmonkeyman 7y ago
  Wow
- lucb1e 7y ago
  Or any other place where privacy is important and you want to share a crowd shot or want to take a shot where there are unfortunately people in the background. Which is pretty much the default for me in public settings / where I don't know every person in the picture personally, but in particular, hacker conferences are the kind of place that comes to mind. I guess the photographer should (for the foreseeable future) always double check that it got each of them (perhaps by having the algorithm produce a second picture with green squares around detected faces), but that's peanuts compared to having to open up an editing program and blurring the region or even each face individually (try doing that on mobile), and blurring looks much worse.
jsilence 7y ago
Wow, that is really close to the animation of the anonymizer hood in 'a scanner darkly' (https://www.imdb.com/title/tt0405296/ https://www.imdb.com/title/tt0405296/).
- tyingq 7y ago
  Here's the clip I think you wanted..."the scramble suit": https://www.dailymotion.com/video/xqrvzb https://www.dailymotion.com/video/xqrvzb
garinswari 7y ago
good news
dvh 7y ago
DeepGoldstein
pndy 7y ago
That example gif at the top - the way faces blend between reminds me of camouflage effect from "A Scanner Darkly" film
- tyingq 7y ago
  Here's the clip: https://www.dailymotion.com/video/xqrvzb https://www.dailymotion.com/video/xqrvzb
deleted 7y ago
[deleted]
false-mirror 7y ago
Really cool project! I wonder how long until this sort of software is used in place of blurred-faces. It would be really nice to shoot a public space and instead of worrying about release forms, just anonymize the footage.
a785236 7y ago
I wish the authors wouldn't oversell the privacy claim: > Github: "The DeepPrivacy GAN never sees any privacy sensitive information, ensuring a fully anonymized image." > Abstract: "We ensure total anonymization of all faces in an image by generating images exclusively on privacy-safe information." > Paper: "We propose a novel generator architecture to anonymize faces, which ensures 100% removal of privacy-sensitive information in the original face." Changing a face anonymizes an image the same way that removing a name anonymizes a dataset -- poorly. This is cool, but it's not anonymization.
- lucb1e 7y ago
  "This is cool, but it's not anonymization." Isn't it? For clarity it might be good to establish what I mean when I talk about three terms: "identifiable" is either the original, encrypted with the key available, or a hashed version or bloom filter (or so) of low-entropy data such as email addresses or phone numbers; "pseudonymous" is replacing the data with a unique but disconnected value (e.g. a UUID, or encrypted with a random key and key destroyed); and "anonymous" is either no data, or data that has no relation to the original. As far as I can tell, this algorithm replaces the data with a random value that has no relation to the original. I understand that if we have a list of HN comment metadata and you remove the usernames ("anonymize"), you can still find me by the time of posting correlated to DNS request logs at the ISP. In the case of pictures, I guess the place is usually identifiable + the time is known, thus you can potentially piece together who was there at that time, corroborated by the presence of a certain backpack or shirt. Is that what you mean, or is there something else that makes you say it is either still identifiable or pseudonymized rather than anonymized?
  - a785236 7y ago
    No, it isn't. > ... this algorithm replaces the data with a random value that has no relation to the original. Based on that sentence, I assume that when you write "the data" you mean "the part of a picture corresponding to a person's face." But removing the face from a picture doesn't necessarily make it particularly difficult to identify the subject if the subject is very familiar to you. It doesn't matter if you've never seen that specific picture, or if you have no additional context like place and time. Just look at the examples on the GitHub page for proof! The picture of Obama and Trump is clearly recognizable, and at least one of the other Obama photos is easy to recognize. The soccer players are identifiable from their jersies (Messi is #10 on Barcelona). Jennifer Lawrence was also easy for me to spot.
kzrdude 7y ago
I didn't know what GANs were or how they worked, but this video from yesterday explained it really well. So I'll recommend it for anyone else curious! The clear explanation of what a GAN is from the first 8-10 minutes of the video. And there are quite a few interesting examples in there as well. https://www.youtube.com/watch?v=dCKbRCUyop8 https://www.youtube.com/watch?v=dCKbRCUyop8
optician_owl 7y ago
Looks like ears are unchanged but it's a strong identifier.
thomas_bai 7y ago
Great to see more research being done on the topic! :) At Brighter AI, we work on this technology since almost 2 years, offering a natural anonymization solution for faces as well as license plates (https://brighter.ai/video https://brighter.ai/video). Preventing facial recognition when collecting data in public is very important and to be fair, also other companies pursue this now. Best regards from Berlin, Thomas (thomas@brighter.ai)

135 points by baylearn 7y ago

baylearn 7y ago

link to the arxiv paper: https://arxiv.org/abs/1909.04538 https://arxiv.org/abs/1909.04538

coolspot 7y ago

Would be useful for amateur adult video sharing. Where face privacy is important, but blur kills the joy.

mrmonkeyman 7y ago
Wow
lucb1e 7y ago
Or any other place where privacy is important and you want to share a crowd shot or want to take a shot where there are unfortunately people in the background. Which is pretty much the default for me in public settings / where I don't know every person in the picture personally, but in particular, hacker conferences are the kind of place that comes to mind. I guess the photographer should (for the foreseeable future) always double check that it got each of them (perhaps by having the algorithm produce a second picture with green squares around detected faces), but that's peanuts compared to having to open up an editing program and blurring the region or even each face individually (try doing that on mobile), and blurring looks much worse.

jsilence 7y ago

Wow, that is really close to the animation of the anonymizer hood in 'a scanner darkly' (https://www.imdb.com/title/tt0405296/ https://www.imdb.com/title/tt0405296/).

tyingq 7y ago
Here's the clip I think you wanted..."the scramble suit": https://www.dailymotion.com/video/xqrvzb https://www.dailymotion.com/video/xqrvzb

garinswari 7y ago

good news

dvh 7y ago

DeepGoldstein

pndy 7y ago

That example gif at the top - the way faces blend between reminds me of camouflage effect from "A Scanner Darkly" film

tyingq 7y ago
Here's the clip: https://www.dailymotion.com/video/xqrvzb https://www.dailymotion.com/video/xqrvzb

deleted 7y ago

[deleted]

false-mirror 7y ago

Really cool project! I wonder how long until this sort of software is used in place of blurred-faces. It would be really nice to shoot a public space and instead of worrying about release forms, just anonymize the footage.

a785236 7y ago

I wish the authors wouldn't oversell the privacy claim: > Github: "The DeepPrivacy GAN never sees any privacy sensitive information, ensuring a fully anonymized image." > Abstract: "We ensure total anonymization of all faces in an image by generating images exclusively on privacy-safe information." > Paper: "We propose a novel generator architecture to anonymize faces, which ensures 100% removal of privacy-sensitive information in the original face." Changing a face anonymizes an image the same way that removing a name anonymizes a dataset -- poorly. This is cool, but it's not anonymization.

lucb1e 7y ago
"This is cool, but it's not anonymization." Isn't it? For clarity it might be good to establish what I mean when I talk about three terms: "identifiable" is either the original, encrypted with the key available, or a hashed version or bloom filter (or so) of low-entropy data such as email addresses or phone numbers; "pseudonymous" is replacing the data with a unique but disconnected value (e.g. a UUID, or encrypted with a random key and key destroyed); and "anonymous" is either no data, or data that has no relation to the original. As far as I can tell, this algorithm replaces the data with a random value that has no relation to the original. I understand that if we have a list of HN comment metadata and you remove the usernames ("anonymize"), you can still find me by the time of posting correlated to DNS request logs at the ISP. In the case of pictures, I guess the place is usually identifiable + the time is known, thus you can potentially piece together who was there at that time, corroborated by the presence of a certain backpack or shirt. Is that what you mean, or is there something else that makes you say it is either still identifiable or pseudonymized rather than anonymized?
- a785236 7y ago
  No, it isn't. > ... this algorithm replaces the data with a random value that has no relation to the original. Based on that sentence, I assume that when you write "the data" you mean "the part of a picture corresponding to a person's face." But removing the face from a picture doesn't necessarily make it particularly difficult to identify the subject if the subject is very familiar to you. It doesn't matter if you've never seen that specific picture, or if you have no additional context like place and time. Just look at the examples on the GitHub page for proof! The picture of Obama and Trump is clearly recognizable, and at least one of the other Obama photos is easy to recognize. The soccer players are identifiable from their jersies (Messi is #10 on Barcelona). Jennifer Lawrence was also easy for me to spot.

kzrdude 7y ago

I didn't know what GANs were or how they worked, but this video from yesterday explained it really well. So I'll recommend it for anyone else curious! The clear explanation of what a GAN is from the first 8-10 minutes of the video. And there are quite a few interesting examples in there as well. https://www.youtube.com/watch?v=dCKbRCUyop8 https://www.youtube.com/watch?v=dCKbRCUyop8

optician_owl 7y ago

Looks like ears are unchanged but it's a strong identifier.

thomas_bai 7y ago

Great to see more research being done on the topic! :) At Brighter AI, we work on this technology since almost 2 years, offering a natural anonymization solution for faces as well as license plates (https://brighter.ai/video https://brighter.ai/video). Preventing facial recognition when collecting data in public is very important and to be fair, also other companies pursue this now. Best regards from Berlin, Thomas (thomas@brighter.ai)