How does backpropagation differ from reverse-mode autodiff
They are more or less specify the same thing. I think the difference is that back-propagation refers to the updating of weights with respect to their gradient to minimize a function; "back-propagating the gradients" is a typical term used. 

Conversely, reverse-mode diff merely means calculating the gradient of a function. Nothing here in this process implies that we aim to the weights/values of a function.