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Copy-move Forgery Is One Of The Most Prevalent Image Tampering Techniques, Where A Region Of An Image Is Copied And Pasted Within The Same Image To Conceal Or Duplicate Content. Detecting Such Manipulations Is Highly Challenging Due To Post-processing Operations Such As Scaling, Rotation, And Compression. In This Work, We Propose A Novel Framework For Copy-move Forgery Detection That Integrates Deep PatchMatch With Pairwise Ranking Learning. The Deep PatchMatch Module Leverages Deep Feature Representations To Establish Reliable Correspondences Between Image Patches, Overcoming Limitations Of Handcrafted Descriptors. Subsequently, A Pairwise Ranking Learning Strategy Is Employed To Differentiate Authentic Patch Correspondences From Forged Ones, Enabling Robust Detection Even Under Complex Transformations. The Proposed Approach Achieves Precise Localization Of Forged Regions While Maintaining Resilience Against Common Post-processing Attacks. Extensive Experiments On Publicly Available Benchmark Datasets Demonstrate That Our Method Outperforms Existing State-of-the-art Techniques In Both Detection Accuracy And Localization Quality. This Work Highlights The Potential Of Combining Deep Patch Similarity Search With Learning-based Ranking For Advancing Image Forensics.