Classification of very similar images
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.