A Full-Image Full-Resolution End-to-End-Trainable CNN Framework for Image Forgery Detection
This addresses the challenge of preserving high-frequency details in image forensics for applications like digital media authentication, representing a novel method for a known bottleneck.
The paper tackles the problem of image forgery detection by proposing a CNN framework that processes full-resolution images end-to-end, avoiding performance loss from resizing. It achieves significant performance improvements, largely outperforming all baseline and reference methods on widespread datasets.
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 whole-image analysis. In this work, we propose a CNN-based image forgery detection framework which makes decisions based on full-resolution information gathered from the whole image. Thanks to gradient checkpointing, the framework is trainable end-to-end with limited memory resources and weak (image-level) supervision, allowing for the joint optimization of all parameters. Experiments on widespread image forensics datasets prove the good performance of the proposed approach, which largely outperforms all baselines and all reference methods.