Synthetic Aperture Radar (SAR) images are a valuable asset for a wide variety of tasks. In the last few years, many websites have been offering them for free in the form of easy to manage products, favoring their widespread diffusion and research work in the SAR field. The drawback of these opportunities is that such images might be exposed to forgeries and manipulations by malicious users, raising new concerns about their integrity and trustworthiness. Up to now, the multimedia forensics literature has proposed various techniques to localize manipulations in natural photographs, but the integrity assessment of SAR images was never investigated. This task poses new challenges, since SAR images are generated with a processing chain completely different from that of natural photographs. This implies that many forensics methods developed for natural images are not guaranteed to succeed. In this paper, we investigate the problem of amplitude SAR imagery splicing localization. Our goal is to localize regions of an amplitude SAR image that have been copied and pasted from another image, possibly undergoing some kind of editing in the process. To do so, we leverage a Convolutional Neural Network (CNN) to extract a fingerprint highlighting inconsistencies in the processing traces of the analyzed input. Then, we examine this fingerprint to produce a binary tampering mask indicating the pixel region under splicing attack. Results show that our proposed method, tailored to the nature of SAR signals, provides better performances than state-of-the-art forensic tools developed for natural images.
翻译:合成孔径雷达(SAR)图像是各种任务的宝贵资产。 在过去几年里,许多网站以易于管理的产品的形式免费提供这些图像,其形式为易于管理的产品,有利于在合成孔径雷达领域进行广泛的传播和研究工作。这些机会的缺点是,这些图像可能暴露在恶意用户的伪造和操纵中,引起人们对其完整性和可信度的新的关注。到目前为止,多媒体法医文献提出了将自然照片操作进行本地化的多种技术,但对合成孔径雷达图像的完整性评估却从未进行过调查。这项任务带来了新的挑战,因为合成孔径雷达图像是用与自然照片完全不同的处理链生成的。这意味着许多为自然图像开发的法医方法不会成功。在本文中,我们调查了合成孔径雷达图像的粘度问题,目的是将从另一个图像中复制和粘贴下来的合成孔径雷达图像区域本地化,可能在此过程中进行某种程度的编辑。为了做到这一点,我们利用进化神经网络(CNN)生成的图像与自然图像的处理链路系完全不同,从而提取一种指纹来显示自然图像的准确性,然后分析我们制作的图像结果,以图象学为结果。