In　this　study,　we　aim　to　improve　the　accuracy　of　image　splicing　detection.　We　propose　a　progressive　image　splicing　detection　method　that　can　detect　the　position　and　shape　of　spliced　region.　Because　image　splicing　is　likely　to　destroy　or　change　the　consistent　correlation　pattern　introduced　by　color　filter　array　(CFA)　interpolation　process,　we　first　used　a　covariance　matrix　to　reconstruct　the　R,　G　and　B　channels　of　image　and　utilized　the　inconsistencies　of　the　CFA　interpolation　pattern　to　extract　forensics　feature.　Then,　these　forensics　features　were　used　to　perform　coarse-grained　detection,　and　texture　strength　features　were　used　to　perform　fine-grained　detection.　Finally,　an　edge　smoothing　method　was　applied　to　realize　precise　localization.　As　compared　to　the　state-of-the-art　CFA-based　image　splicing　detection　methods,　the　proposed　method　has　a　high-level　detection　accuracy　and　strong　robustness　against　content-preserving　manipulations　and　JPEG　compression.　©　2021