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A Full-Image Full-Resolution End-to-End-Trainable CNN Framework for Image Forgery Detection
[article]
2019
arXiv
pre-print
Due to limited computational and memory resources, current deep learning models accept only rather small images in input, calling for preliminary image resizing. This is not a problem for high-level vision problems, where discriminative features are barely affected by resizing. On the contrary, in image forensics, resizing tends to destroy precious high-frequency details, impacting heavily on performance. One can avoid resizing by means of patch-wise processing, at the cost of renouncing
arXiv:1909.06751v1
fatcat:vwh75pn6ofac5dcf3oy742team