Unfortunately, (almost all!) image hashing don't detect color differences--they map images to greyscale first. This may be fine for many situations, but it will return the same result for a sepia tint, a full color original with incorrect white balance, and the final result you made after mucking with channels for a couple minutes.
I also found that there really isn't one "best" image hash algorithm. Using _several different_ image hash algos turns out to be only fractionally more expensive during both compute and query times, and substantially improves both precision and recall. I'm using a mean hash, gradient diff, and a DCT, all rendered from all three CIELAB-based layers, so they're sensitive to both brightness and color differences.
I also found that there really isn't one "best" image hash algorithm. Using _several different_ image hash algos turns out to be only fractionally more expensive during both compute and query times, and substantially improves both precision and recall. I'm using a mean hash, gradient diff, and a DCT, all rendered from all three CIELAB-based layers, so they're sensitive to both brightness and color differences.