Simple everyday tasks, such as visual search, require a visual system that is sensitive to differences. Here we report how observers perceive changes in natural image stimuli, and what happens if objects change color, position, or identity-i.e., when the external scene changes in a naturalistic manner. We investigated whether a V1-based difference-prediction model can predict the magnitude ratings given by observers to suprathreshold differences in numerous pairs of natural images. The model incorporated contrast normalization and surround suppression, and elongated receptive-fields. Observers' ratings were better predicted when the model included phase invariance, and even more so when the stimuli were inverted and negated to lessen their semantic impact. Some feature changes were better predicted than others: the model systematically underpredicted observers' perception of the magnitude of blur, but over-predicted their ability to report changes in textures.