With　the　rapid　proliferation　of　urbanization,　massive　data　in　social　networks　are　collected　and　aggregated　in　real　time,　making　it　possible　for　criminals　to　use　images　as　a　cover　to　spread　secret　information　on　the　Internet.　How　to　determine　whether　these　images　contain　secret　information　is　a　huge　challenge　for　multimedia　computing　security.　The　steganalysis　method　based　on　deep　learning　can　effectively　judge　whether　the　pictures　transmitted　on　the　Internet　in　urban　scenes　contain　secret　information,　which　is　of　great　significance　to　safeguarding　national　and　social　security.　Image　steganalysis　based　on　deep　learning　has　powerful　learning　ability　and　classification　ability,　and　its　detection　accuracy　of　steganography　images　has　surpassed　that　of　traditional　steganalysis　based　on　manual　feature　extraction.　In　recent　years,　it　has　become　a　hot　topic　of　the　information　hiding　technology.　However,　the　detection　accuracy　of　existing　deep　learning　based　steganalysis　methods　still　needs　to　be　improved,　especially　when　detecting　arbitrary-size　and　multi-source　images,　their　detection　efficientness　is　easily　affected　by　cover　mismatch.　In　this　manuscript,　we　propose　a　steganalysis　method　based　on　Inverse　Residuals　structured　Siamese　network　(abbreviated　as　SiaIRNet　method,　Siamese-Inverted-Residuals-Network　Based　method).　The　SiaIRNet　method　uses　a　siamese　convolutional　neural　network　(CNN)　to　obtain　the　residual　features　of　subgraphs,　including　three　stages　of　preprocessing,　feature　extraction,　and　classification.　Firstly,　a　preprocessing　layer　with　high-pass　filters　combined　with　depth-wise　separable　convolution　is　designed　to　more　accurately　capture　the　correlation　of　residuals　between　feature　channels,　which　can　help　capture　rich　and　effective　residual　features.　Then,　a　feature　extraction　layer　based　on　the　Inverse　Residuals　structure　is　proposed,　which　improves　the　ability　of　the　model　to　obtain　residual　features　by　expanding　channels　and　reusing　features.　Finally,　a　fully　connected　layer　is　used　to　classify　the　cover　image　and　the　stego　image　features.　Utilizing　three　general　datasets,　BossBase-1.01,　BOWS2,　and　ALASKA#2,　as　cover　images,　a　large　number　of　experiments　are　conducted　comparing　with　the　state-of-the-art　steganalysis　methods.　The　experimental　results　show　that　compared　with　the　classical　SID　method　and　the　latest　SiaStegNet　method,　the　detection　accuracy　of　the　proposed　method　for　15　arbitrary-size　images　is　improved　by　15.96%　and　5.86%　on　average,　respectively,　which　verifies　the　higher　detection　accuracy　and　better　adaptability　of　the　proposed　method　to　multi-source　and　arbitrary-size　images　in　urban　scenes.　©　2023　Copyright　held　by　the　owner/author(s).　Publication　rights　licensed　to　ACM.