I am trying to find some measurement for identifying and distinguishing between compressed and random data. I tried this first by computing the entropy of such data, the entropy value is extremely high (almost maximum) in both cases, so that way does not seem to work as a distinguisher.

I read about the chi square algorithm but I've never used it (actually I still have some problems with interpreting the results). Does anybody know if this algorithm can lead to better results?

  • 1
    $\begingroup$ I am not an expert in this, but the NIST tests might do a better job. $\endgroup$
    – mikeazo
    Aug 15, 2012 at 13:07
  • $\begingroup$ Wikipedia has a pretty good description of Pearson's chi-squared test. The challenge, really, is coming up with a suitable null hypothesis to test; for example, even very poor pseudorandom streams will usually satisfy the simple hypothesis that the frequencies of individual bytes are uniformly distributed, no matter what test you use. $\endgroup$ Aug 15, 2012 at 14:01
  • $\begingroup$ See also crypto.stackexchange.com/questions/1287/… $\endgroup$
    – mikeazo
    Aug 15, 2012 at 14:12

2 Answers 2


The NIST tools are a good starting point.

There is no general-purpose algorithm that will always distinguish compressed from random data

However, if you want to try the chi-squared test, you can compute a histogram of the frequency of all byte values (how many 0 bytes you saw in the data, how many 1 bytes you saw, etc.), and then use the chi-squared test to test whether this appears to deviate from what you'd expect for uniform-random data.


A late answer, but I recently had cause to perform some entropy estimation and calculated some chis.

For context, in uniformly distributed random bytes, the target chi is ~255 leading to a p value of ~ 0.5. As one definition of randomness is in-compressibility, it follows that you cannot differentiate a compressed file from a truly random one. The caveat though is the level of possible compression. A compressed file requires control and format structures within it that significantly differentiate it from perfectly random. These control structures throw out the calculated p values in a chi test. So some examples of compressed data:-

.zip p < 0.0001
.jpg p < 0.0001
.png p < 0.0001

Remember random data would have a p ~ 0.5 on average. More specifically, a Kolmogorov–Smirnov test of these p values should see them uniformly distributed 0 to 1. So at this point my answer would be that yes, you can use a chi test to identify random data.

But compression algorithms have improved and I've found fp8 which is a PAQ8 derivative. It's the most powerful compression program that I found that can be easily compiled. The same files now give the following chis having been compressed by fp8:-

.zip.fp8 p = 0.93
.jpg.fp8 p = 0.14
.png.fp8 p = 0.38

On prima facie evidence, these compressed files produce chi p values consistent with fully random data. So my final answer is no, you cannot differentiate random data from compressed data using a chi test.

Some further insight into chi and p might be had here.


Your Answer

By clicking “Post Your Answer”, you agree to our terms of service and acknowledge you have read our privacy policy.

Not the answer you're looking for? Browse other questions tagged or ask your own question.