A Detection Method of Operated Fake-Images Using Robust Hashing.

Miki Tanaka,Sayaka Shiota,Hitoshi Kiya

doi:10.3390/jimaging7080134

Abstract

SNS providers are known to carry out the recompression and resizing of uploaded images, but most conventional methods for detecting fake images/tampered images are not robust enough against such operations. In this paper, we propose a novel method for detecting fake images, including distortion caused by image operations such as image compression and resizing. We select a robust hashing method, which retrieves images similar to a query image, for fake-image/tampered-image detection, and hash values extracted from both reference and query images are used to robustly detect fake-images for the first time. If there is an original hash code from a reference image for comparison, the proposed method can more robustly detect fake images than conventional methods. One of the practical applications of this method is to monitor images, including synthetic ones sold by a company. In experiments, the proposed fake-image detection is demonstrated to outperform state-of-the-art methods under the use of various datasets including fake images generated with GANs.

Highlights

Recent rapid advances in image manipulation tools and deep image synthesis techniques have made generating fake images easy
Fake images in the Image Manipulation Dataset were generated by using image manipulation tools without generative adversarial networks (GANs), and those in the other datasets were prepared with GANs
Both original images and fake ones were used as query images, where JPEG compression with a quantization parameter of QJ = 80 was applied to all query images, so real images used as query ones included some compression noise

Summary

Introduction

Recent rapid advances in image manipulation tools and deep image synthesis techniques have made generating fake images easy. Detecting fake/tampered images has become an urgent issue [1]. Most methods for detecting forgery assume that images are generated using specific manipulation techniques, so these methods detect unique features caused by such techniques such as checkerboard artifacts [2,3,4,5]. Tampered images are usually uploaded to social networks to share with other people. SNS providers are known to apply uploaded images to resizing and compressing [6], and such operation by an SNS provider or malicious person damage can damage or cause the images to lose the unique features of tampered images. Methods for detecting fake/tampered images suffer from the influence of operation in general. Conventional methods are not robust enough yet against the various types of content-preserving transforms without malice, such as resizing and compression

Methods

Results

Conclusion