Embodied　artificial　intelligence　(AI)　represents　a　new　generation　of　robotics　technology　combined　with　artificial　intelligence,　and　it　is　at　the　forefront　of　current　research.　To　reduce　the　impact　of　deepfake　technology　on　embodied　perception　and　enhance　the　security　and　reliability　of　embodied　AI,　this　paper　proposes　a　novel　deepfake　detection　model　with　a　new　Balanced　Contrastive　Learning　strategy,　named　BCL.　By　integrating　unsupervised　contrastive　learning　and　supervised　contrastive　learning　with　deepfake　detection,　the　model　effectively　extracts　the　underlying　features　of　fake　images　from　both　individual　level　and　category　level,　thereby　leading　to　more　discriminative　features.　In　addition,　a　Multi-scale　Attention　Interaction　module　(MAI)　is　proposed　to　enrich　the　representative　ability　of　features.　By　cross-fusing　the　semantic　features　of　different　receptive　fields　of　the　encoder,　more　effective　deepfake　traces　can　be　mined.　Finally,　extensive　experiments　demonstrate　that　our　method　has　good　performance　and　generalization　capabilities　across　intra-dataset,　cross-dataset　and　cross-manipulation　scenarios.　©　2024　Elsevier　B.V.