Image Comparison tuned to Human Perception
Unfortunately there is not likely to be any good general algorithm for this.  Human perception is extremely complex.  The best you can do is find algorithms that will sometimes recognize two images as perceptually similar, but there will inevitably be some pairs of images that humans would classify as perceptually similar but your algorithm won't detect as similar.

Your best bet is probably to try to find out exactly what minor differences tend to be introduced by these particular platforms, and then try to devise an algorithm that is tuned to those particular kinds of differences -- you'll probably have better luck than that than with any general algorithm.

One very simple algorithm is to align the two images (e.g., using SIFT/SURF/etc.), add a bit of Gaussian blur to the two images, downscale, and then compare using a L2 norm.  Another simple algorithm is to align the two images with SIFT/SURF/etc., and then compute a metric based upon the number of inliers vs outliers in the feature points (how well the set of feature points from the first image match up with the set of feature points from the second image).  However, it's hard to know how well this will work for your particular application, without knowing the details of the differences that tend to arise in your situation.