GAN-VIT-CMFD: A NOVEL FRAMEWORK INTEGRATING GENERATIVE ADVERSARIAL NETWORKS AND VISION TRANSFORMERS FOR ENHANCED COPY-MOVE FORGERY DETECTION AND CLASSIFICATION WITH SPECTRAL CLUSTERING

GAN-ViT-CMFD: A novel framework integrating generative adversarial networks and vision transformers for enhanced copy-move forgery detection and classification with spectral clustering

Copy-move forgery detection (CMFD) is a critical task in digital forensics to ensure the authenticity of visual content, as the prevalence of advanced editing tools has made it increasingly easy to tamper with images.Such forgeries can have severe implications in fields like journalism, legal evidence, and cybersecurity.The motivation for adopting

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PePIF1, a P-lineage of PIF-like transposable element identified in protocorm-like bodies of Phalaenopsis orchids

Abstract Background Orchids produce a colorless protocorm by symbiosis with fungi upon seed germination.For mass production of orchids, the prevailing approaches are both generation of protocorm-like bodies (PLBs) from callus and multiplication of adventitious buds on inflorescence.However, somaclonal variations occur during micropropagation.Result

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