In　the　current　Internet　landscape,　the　rampant　spread　of　fake　news,　particularly　in　the　form　of　multi-modal　content,　poses　a　great　social　threat.　While　automatic　multi-modal　fake　news　detection　methods　have　shown　promising　results,　the　lack　of　explainability　remains　a　significant　challenge.　Existing　approaches　provide　superficial　explainability　by　displaying　learned　important　components　or　views　from　well-trained　networks,　but　they　often　fail　to　uncover　the　implicit　deceptive　patterns　that　reveal　how　fake　news　is　fabricated.　To　address　this　limitation,　we　begin　by　predefining　three　typical　deceptive　patterns,　namely　image　manipulation,　cross-modal　inconsistency,　and　image　repurposing,　which　shed　light　on　the　mechanisms　underlying　fake　news　fabrication.　Then,　we　propose　a　novel　Neuro-Symbolic　Latent　Model　called　NSLM,　that　not　only　derives　accurate　judgments　on　the　veracity　of　news　but　also　uncovers　the　implicit　deceptive　patterns　as　explanations.　Specifically,　the　existence　of　each　deceptive　pattern　is　expressed　as　a　two-valued　learnable　latent　variable,　which　is　acquired　through　amortized　variational　inference　and　weak　supervision　based　on　symbolic　logic　rules.　Additionally,　we　devise　pseudo-siamese　networks　to　capture　distinct　deceptive　patterns　effectively.　Experimental　results　on　two　real-world　datasets　demonstrate　that　our　NSLM　achieves　the　best　performance　in　fake　news　detection　while　providing　insightful　explanations　of　deceptive　patterns.　Copyright　©　2024,　Association　for　the　Advancement　of　Artificial　Intelligence　(www.aaai.org).　All　rights　reserved.