Image　steganography　attempts　to　imperceptibly　hide　the　secret　image　within　the　cover　image.　Most　of　the　existing　deep　learning-based　steganography　approaches　have　excelled　in　payload　capacity,　visual　quality,　and　steganographic　security.　However,　they　are　difficult　to　losslessly　reconstruct　secret　images　from　stego　images　with　relatively　large　payload　capacity.　Recently,　although　some　studies　have　introduced　invertible　neural　networks　(INNs)　to　achieve　large-capacity　image　steganography,　these　methods　still　cannot　reconstruct　the　secret　image　losslessly　due　to　the　existence　of　lost　information　on　the　output　side　of　the　concealing　network.　We　present　an　INN-based　framework　in　this　paper　for　lossless　image　steganography.　Specifically,　we　regard　image　steganography　as　an　image　super-resolution　task　that　converts　low-resolution　cover　images　to　high-resolution　stego　images　while　hiding　secret　images.　The　feature　dimension　of　the　generated　stego　image　matches　the　total　dimension　of　the　input　secret　and　cover　images,　thereby　eliminating　the　lost　information.　Besides,　a　bijective　secret　projection　module　is　designed　to　transform　various　secret　images　into　a　latent　variable　that　follows　a　simple　distribution,　improving　the　imperceptibility　of　the　secret　image.　Comprehensive　experiments　indicate　that　the　proposed　framework　achieves　secure　hiding　and　lossless　extraction　of　the　secret　image.　©　2024　Elsevier　Ltd