The　vulnerability　of　deep　learning　to　adversarial　attacks　has　brought　many　security　risks　to　its　development.　However,　the　currently　proposed　adversarial　attack　detection　methods　are　ineffective　in　defending　large-size　images.　Studies　have　shown　that　detection　methods　based　on　steganalysis　have　apparent　advantages　in　defense　of　large-scale　adversarial　examples.　Still,　their　detection　results　are　very　dependent　on　the　quality　of　manually　extracted　features.　This　paper　proposes　an　end-to-end　convolutional　neural　network　(CNN)　model　based　on　steganalysis　for　adversarial　examples　detection.　We　found　that　adversarial　attacks　tend　to　embed　perturbations　in　high-texture　regions.　To　focus　more　on　vulnerable　areas　during　feature　learning,　we　use　the　original　image　as　information　to　learn　the　attention　distribution,　which　improves　the　model＇s　accuracy.　And　an　adversarial　loss　function　is　proposed　to　fine-tune　the　model　so　that　the　normal　images　and　adversarial　examples　are　more　separated　in　the　spatial　distribution,　reducing　the　probability　of　misclassifying　normal　images.　Experiments　show　that　this　model　has　a　high　defense　effect　against　the　current　state-of-the-art　attack　methods.　©　2022　IEEE.