A Multi-Feature Extraction-Based Algorithm for Stitching Tampered/Untampered Image Classification

Ruofan Jia,Jianqiu Zhang,Abdelwahed Nahli,Dan Li

doi:10.3390/app12052337

Abstract

With the recent internet connectivity revolution, and the fast-growing prevalence of camera-enabled devices, images play a vital role in several fields of modern life. Photos, which often have been seen as evidence in courts, are nowadays subject to more sophisticated tricky forgery. To detect the image stitching between originally unassociated people/scenes and other combining forgery, an algorithm used to extract multiple specific image features, such as grayscale, complementary color wavelet (CCW) based chroma, sharpness, and natural scene statistics (NSS), is first presented in this paper. It is illustrated that a random forest model can be trained by these extracted features and then be employed to classify the stitching tampered/untampered images. The experimental results show that the proposed algorithm favorably outperforms the techniques reported in the literature, and achieves a state-of-the-art performance with higher accuracy values of 91%, 95.24%, and 88.02% on the Tampering ImageNet, Columbia, and CASIA ITDE V2.0 datasets, respectively. The precision, recall, and F1-score were also improved to a certain extent.

Highlights

The internet connectivity revolution, and the prevalence of camera-enabled devices, has resulted in the production of a massive daily amount of digital data
In CASIA ITDE V2.0 [18], 6427 untampered images and 1330 tampered images were selected for detection, and the image sizes were all 384 × 256 or 256 × 384
It is worth mentioning that Columbia [17] and CASIA ITDE V2.0 [18] are already existing public benchmark datasets that were specially built for image tampering detection tasks

Summary

Introduction

The internet connectivity revolution, and the prevalence of camera-enabled devices, has resulted in the production of a massive daily amount of digital data. Among these digital data, images are one of the most important data categories that influence people’s daily life. Stored digital images are vulnerable to tampering, especially when an image editing software is available [2]. It is easy to copy a region in one image to another using the image editing tool. This kind of tampering is known as image stitching. The detection of whether the image content has been tampered with using stitching is significant in the fields of forensic investigation, criminal investigation, news, and surveillance systems [3,4]

Results

Discussion

Conclusion