In　recent　years,　the　rapid　advancement　of　image　generation　techniques　has　resulted　in　the　widespread　abuse　of　manipulated　images,　leading　to　a　crisis　of　trust　and　affecting　social　equity.　Thus,　the　goal　of　our　work　is　to　detect　and　localize　tampered　regions　in　images.　Many　deep　learning　based　approaches　have　been　proposed　to　address　this　problem,　but　they　can　hardly　handle　the　tampered　regions　that　are　manually　fine-tuned　to　blend　into　image　background.　By　observing　that　the　boundaries　of　tempered　regions　are　critical　to　separating　tampered　and　non-tampered　parts,　we　present　a　novel　boundary-guided　approach　to　image　manipulation　detection,　which　introduces　an　inherent　bias　towards　exploiting　the　boundary　information　of　tampered　regions.　Our　model　follows　an　encoder-decoder　architecture,　with　multi-scale　localization　mask　prediction,　and　is　guided　to　utilize　the　prior　boundary　knowledge　through　an　attention　mechanism　and　contrastive　learning.　In　particular,　our　model　is　unique　in　that　1)　we　propose　a　boundary-aware　attention　module　in　the　network　decoder,　which　predicts　the　boundary　of　tampered　regions　and　thus　uses　it　as　crucial　contextual　cues　to　facilitate　the　localization　and　2)　we　propose　a　multi-scale　contrastive　learning　scheme　with　a　novel　boundary-guided　sampling　strategy,　leading　to　more　discriminative　localization　features.　Our　state-of-art　performance　on　several　public　benchmarks　demonstrates　the　superiority　of　our　model　over　prior　works.　IEEE