Timeline for Can FFT2 be used as a randomness test for image encryption?
Current License: CC BY-SA 4.0
6 events
| when toggle format | what | by | license | comment | |
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| Nov 1, 2022 at 16:39 | answer | added | blamocur | timeline score: 1 | |
| Oct 16, 2022 at 20:25 | comment | added | Peter K.♦ | That will not work. The distribution (Gaussian) of the samples has very little to do with the randomness of the samples. You have to answer the question what is your measure of randomness?. You keep referring to Entropy. Why not use that directly and not both with FFTs? | |
| Oct 16, 2022 at 1:39 | comment | added | user64854 | Analyzing the degree of patternless encrypted data is called the randomness test. For example, in image encryption, an encrypted image with an entropy close to 8 is closer to the random state than an image with an entropy close to 7. The hypothesis I propose is that if we accept that the output of FFT2 calculations is in the form of a normal graph similar to a white noise image, then our data is random, and whatever we move away from this state, we have an image in which a pattern can be found. Can it be shown that this hypothesis is correct? | |
| Oct 16, 2022 at 1:39 | comment | added | user64854 | @Peter K. I have considered the image of white noise as a random image, and to answer your question, yes, the image of white noise is more random than the image of Lena. | |
| Oct 15, 2022 at 17:31 | comment | added | Peter K.♦ | OK, so there's more happening than just the FFT2 (as you allude to). The question is: what is your measure of randomness? Would a histogram like your first one, but flipped around the vertical access (so the peak was at 1 instead of 0) be "more random" than the first plot? More random than the second plot? Would a histogram that is uniform (same value at from 0 to 1) be "more random" ? What do you mean by "random" ? | |
| Oct 15, 2022 at 1:59 | history | asked | user64854 | CC BY-SA 4.0 |