The　deep　learning　(DL)　techniques　have　been　an　effective　means　for　electricity　theft　detection.　However,　most　existing　works　are　based　on　1-D　time　series　data,　which　makes　it　challenging　to　fully　capture　and　utilize　the　multidimensional　features　of　the　electricity　data.　Therefore,　a　novel　electricity　theft　detection　approach　based　on　Gramian　angular　summation　field　(GASF)　and　ConvNeXt　is　proposed.　Specifically,　this　article　designs　a　GASF-based　image　coding　strategy　to　convert　1-D　electricity　consumption　data　into　2-D　images.　In　this　case,　the　multidimensional　features　of　electricity　consumption　behavior　can　be　preserved　in　the　GASF　images.　And　then,　the　image　classifier　is　designed　that　utilize　ConvNeXt　as　a　feature　extractor　to　learn　multidimensional　features　of　GASF　images,　thereby　achieving　electricity　theft　detection.　Experimental　results　based　on　a　real-world　dataset　show　that　the　proposed　detector　reaches　an　accuracy　of　0.973,　which　is　superior　to　state-of-the-art　methods.　Furthermore,　the　detection　performance　of　different　anomaly　detectors　against　smart　electricity　theft　behaviors　is　evaluated,　and　the　economic　losses　(ELs)　they　can　recover　for　power　companies　are　quantified.　The　results　indicate　that　the　proposed　detector,　by　virtue　of　its　strong　capability　in　identifying　interclass　differences　among　electricity　theft　behaviors,　can　thereby　minimize　the　ELs　sustained　by　power　companies.