Image Splicing Detection

Thus far, most research in Image Forgery Detection has concentrated on the detection and localization of specific methods of forgery using methods like patch-matching, anomaly detection, and examining residual-based local descriptors. Recent research has shown that sufficiently trained Convolutional Neural Networks can learn functions similar to those of networks trained on handcrafted features. This research focuses on combining this new knowledge with various preprocessing methods to demonstrate a proof-of-concept model. Keywords—Image Forensics, Splicing, Machine Learning, Convolutional Neural Networks, Autoencoders

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