A Full-Image Full-Resolution End-to-End-Trainable CNN Framework for Image Forgery Detection

Francesco Marra,Giovanni Poggi,Diego Gragnaniello,Luisa Verdoliva

doi:10.1109/access.2020.3009877

Francesco Marra, Giovanni Poggi + Show 2 more

Open Access

https://doi.org/10.1109/access.2020.3009877

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Abstract

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.

Highlights

In this work, we propose a new framework for image forgery detection based on convolutional neural networks (CNN)
Best results are highlighted in red for reference methods and in blue for our proposal
E2E-RGB provides a further significant performance improvement. Our explanation for this phenomenon is the strong heterogeneity of the input: since RGB bands and noiseprints have quite different statistics, the net may have a hard time processing them jointly

Summary

Introduction

We propose a new framework for image forgery detection based on convolutional neural networks (CNN). This may not look exciting: deep learning is by- common practice to solve all kinds of vision-related problems. Image forensics has some peculiarities that set it apart from standard computer vision problems. This well-crafted splicing does not show obvious artifacts that allow detection by visual inspection, but a suitable textural analysis reveals differences that may be due only to the insertion of alien material in the host image. Many state-of-the-art forensic tools rely on the statistical analysis of local micro-patterns, observed at their native (full) resolution. Clues emerging from the whole image, The associate editor coordinating the review of this manuscript and approving it for publication was Joewono Widjaja

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