When　images　undergo　quality　degradation　caused　by　editing,　compression　or　transmission,　their　saliency　tends　to　shift　away　from　its　original　position.　Saliency　shifts　indicate　visual　behaviour　change　and　therefore　contain　vital　information　regarding　perception　of　visual　content　and　its　distortions.　Given　a　pristine　image　and　its　distorted　format,　we　want　to　be　able　to　detect　saliency　shifts　induced　by　distortions.　The　resulting　saliency　shift　map　(SSM)　can　be　used　to　identify　the　region　and　degree　of　visual　distraction　caused　by　distortions,　and　consequently　to　perceptually　optimise　image　coding　or　enhancement　algorithms.　To　this　end,　we　first　create　a　largest-of-its-kind　eye-tracking　database,　comprising　60　pristine　images　and　their　associated　540　distorted　formats　viewed　by　96　subjects.　We　then　propose　a　computational　model　to　predict　the　saliency　shift　map　(SSM),　utilising　transformers　and　convolutional　neural　networks.　Experimental　results　demonstrate　that　the　proposed　model　is　highly　effective　in　detecting　distortion-induced　saliency　shifts　in　natural　images.