Along　with　the　advancement　of　manipulation　technologies,　image　modification　is　becoming　increasingly　convenient　and　imperceptible.　To　tackle　the　challenging　image　tampering　detection　problem,　this　article　presents　an　attentional　cross-domain　deep　architecture,　which　can　be　trained　end-to-end.　This　architecture　is　composed　of　three　convolutional　neural　network　(CNN)　streams　to　extract　three　types　of　features,　including　visual　perception,　resampling,　and　local　inconsistency　features,　from　spatial　and　frequency　domains.　The　multitype　and　cross-domain　features　are　then　combined　to　formulate　hybrid　features　to　distinguish　manipulated　regions　from　nonmanipulated　parts.　Compared　with　other　deep　architectures,　the　proposed　one　spans　a　more　complementary　and　discriminative　feature　space　by　integrating　richer　types　of　features　from　different　domains　in　a　unified　end-to-end　trainable　framework　and　thus　can　better　capture　artifacts　caused　by　different　types　of　manipulations.　In　addition,　we　design　and　train　a　module　called　tampering　discriminative　attention　network　(TDA-Net)　to　highlight　suspicious　parts.　These　part-level　representations　are　then　integrated　with　the　global　ones　to　further　enhance　the　discriminating　capability　of　the　hybrid　features.　To　adequately　train　the　proposed　architecture,　we　synthesize　a　large　dataset　containing　various　types　of　manipulations　based　on　DRESDEN　and　COCO.　Experiments　on　four　public　datasets　demonstrate　that　the　proposed　model　can　localize　various　manipulations　and　achieve　the　state-of-the-art　performance.　We　also　conduct　ablation　studies　to　verify　the　effectiveness　of　each　stream　and　the　TDA-Net　module.