论文标题
使用有条件gan的卫星图像中的剪接检测和定位
Splicing Detection and Localization In Satellite Imagery Using Conditional GANs
论文作者
论文摘要
图像编辑工具的广泛可用性和图像处理技术的改进使图像操纵非常容易。通常，易于使用但复杂的图像操纵工具会导致人类观察者无法察觉到的变形/变化。锻造图像的分布可能会产生巨大的后果，尤其是当互联网的速度和广阔之处时。因此，验证图像完整性对数字法医社区构成了巨大而重要的挑战。卫星图像可以通过多种方式进行修改，包括插入对象隐藏现有场景和结构。在本文中，我们描述了条件生成对抗网络（CGAN）的使用来确定卫星图像中这种剪接的伪造的存在。此外，我们确定了它们的位置和形状。经过原始图像和伪造的图像培训，我们的方法在这些检测和本地化目标上取得了很大的成功。
The widespread availability of image editing tools and improvements in image processing techniques allow image manipulation to be very easy. Oftentimes, easy-to-use yet sophisticated image manipulation tools yields distortions/changes imperceptible to the human observer. Distribution of forged images can have drastic ramifications, especially when coupled with the speed and vastness of the Internet. Therefore, verifying image integrity poses an immense and important challenge to the digital forensic community. Satellite images specifically can be modified in a number of ways, including the insertion of objects to hide existing scenes and structures. In this paper, we describe the use of a Conditional Generative Adversarial Network (cGAN) to identify the presence of such spliced forgeries within satellite images. Additionally, we identify their locations and shapes. Trained on pristine and falsified images, our method achieves high success on these detection and localization objectives.
