Q:

Classification of very similar images

I have two groups of images, each one with 1000 samples.
The speckle pattern, in this context, is the same as a random pattern or "white noise" image. So these images are fundamentally different.
In group one, each figure is generated by considering a random function that returns something similar to a speckle pattern (see fig. 1).
In group two we follow the same procedure as group 1, but we plot a small point above that can be positioned anywhere and with any color (see fig. 2).
I want to classify both groups and I already tried to do it with simple neural networks, but I have been unsuccessful.
What is the best technique for this kind of problems?
Fig. 1:

Fig. 2:

A:

Have you tried rule-based approaches?
Based on your example, I can think of two ways:

For each picture, get a list of all RGB values it contains.
The first contains only grays (which have RGB values (x, x, x)), the second one contains color (so some pixel with RGB value (x, y, z), where $x \neq y$ or $y \neq z$). Or - if that is faster - convert all pictures to gray scale and check if the converted picture is identical to the original.
Scan the image for areas (larger than one pixel) with identical colors. If your dots are large enough and the background noise is uncorrelated for neighboring pixels, it is very likely that these areas are the dots you are looking for.

A:

I found the answer in the paper linked above. 
The authors use a CNN to solve the problem. I will post the code.
https://link.springer.com/article/10.1007/s00170-017-0882-0

