<p>It's dependent on the algorithm used for comparison, but if you have access to that algorithm, you can add small amounts of noise to pixels randomly until it misclassifies the image.</p>

<p>If you had deeper knowledge of the algorithm used, and it was differentiable, you could use gradient descent (ascent) to modify the image to increase the error of what was detected. This is the reverse of what you do when training image comparisons/classifiers which modify the weights to minimize the error.</p>

<p>But in short, if you dont have much knowledge of how the algorithm used works, I personally would try adding noise to pixels randomly, such that the image was still easily recognizable, but a decent amount of noise was added in.</p>

CC BY-SA 4.0
2019-11-17T20:23:20.773Z
Probably, there is a fft, wavelet or laplacian. Many of them are extremely noise resistant.
joojaa
CC BY-SA 4.0
2019-11-17T20:45:42.853Z
Is there something that those techniques are weak against? I wonder if tone mapping or white balance type things would thwart them?
Alan Wolfe
CC BY-SA 4.0
2019-11-17T21:01:50.437Z
Well usually a mirror image or gafting two pictures into one
joojaa
CC BY-SA 4.0
2019-11-17T21:02:49.113Z
Ah neat, so maybe mirror image and add some noise to cover your bases?
Alan Wolfe
CC BY-SA 4.0
2019-11-17T21:49:22.353Z
@joojaa The laplacian is the opposite of noise resistant, it blows up noise disproportionately. That's why you usually apply Gaussian convolution before that. But facebook certainly uses something more robust, so it should deal with noise quite easily. Honestly, without knowing the algo, or extensive trial and error, it is very hard to know how to modify the image.
lightxbulb
CC BY-SA 4.0
2019-11-17T19:30:52.803Z
2019-11-17T19:30:52.803Z
Alan Wolfe
<p>Is there a way to change a picture so it would fail to match against existing copy of the same picture on the big social networks such as Facebook, Twitter, Instagram, Tinder and others? Desired result should be as close as possible to the original.</p>

<image-comparison>
How to change a picture to fail comparison algorithms?
Caballero
# How to change a picture to fail comparison algorithms?

(Asked by: username_0 on 2019-11-17)

Is there a way to change a picture so it would fail to match against existing copy of the same picture on the big social networks such as Facebook, Twitter, Instagram, Tinder and others? Desired result should be as close as possible to the original.

**Tags:** image-comparison


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## 1 answers

### Answer by username_1 on 2019-11-17. Score: 2 | Accepted answer

It's dependent on the algorithm used for comparison, but if you have access to that algorithm, you can add small amounts of noise to pixels randomly until it misclassifies the image.

If you had deeper knowledge of the algorithm used, and it was differentiable, you could use gradient descent (ascent) to modify the image to increase the error of what was detected. This is the reverse of what you do when training image comparisons/classifiers which modify the weights to minimize the error.

But in short, if you dont have much knowledge of how the algorithm used works, I personally would try adding noise to pixels randomly, such that the image was still easily recognizable, but a decent amount of noise was added in.

> **Comments:**
> **username_2**: Probably, there is a fft, wavelet or laplacian. Many of them are extremely noise resistant.  
> **username_1**: Is there something that those techniques are weak against? I wonder if tone mapping or white balance type things would thwart them?  
> **username_2**: Well usually a mirror image or gafting two pictures into one  
> **username_1**: Ah neat, so maybe mirror image and add some noise to cover your bases?  
> **username_3**: @username_2 The laplacian is the opposite of noise resistant, it blows up noise disproportionately. That's why you usually apply Gaussian convolution before that. But facebook certainly uses something more robust, so it should deal with noise quite easily. Honestly, without knowing the algo, or extensive trial and error, it is very hard to know how to modify the image.  

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