Image　Manipulation　Localization　(IML)　is　a　fundamental　binary　segmentation　task,　focused　on　the　precise　identification　and　demarcation　of　the　manipulated　regions　within　an　image.　Most　existing　models　primarily　rely　on　RGB　and　noise　features　to　accurately　identify　the　tampered　areas　in　images.　However,　in　practical　IML　tasks,　the　effectiveness　of　commonly　used　noise　feature　modules　is　often　compromised　by　the　unknown　tampering　methods　and　the　diversity　of　images.　In　this　paper,　we　design　an　adaptive　filter　based　on　the　Discrete　Cosine　Transform　(DCT)　to　localize　the　manipulated　regions.　Furthermore,　we　theoretically　demonstrate　that　the　adaptive　filter　is　equivalent　to　convolving　the　image　with　a　large-scale　convolutional　kernel,　thereby　taking　full　account　of　features　across　the　entire　image.　Through　extensive　experimentation　on　several　benchmark　image　tampering　datasets,　our　model　has　demonstrated　performance　that　rivals　the　most　state-of-the-art　methods.　　©　2024　IEEE.