Manipulation Detection and Localization for Satellite Imagery

Janos Horvath, Purdue University

Abstract

Satellite imagery is becoming increasingly available due to a large number of commercial satellite operators. Many fields use satellite images, including meteorology, forestry, natural disaster analysis, and agriculture. These images can be changed or tampered with image manipulation tools that can cause issues in many applications. Manipulation detection techniques designed for images captured by ``consumer cameras'' tend to fail when used on satellite images. In this thesis we examine methods for detecting splices where an object or area is inserted into a satellite image. Three semi-supervised one-class methods are proposed for the detection and localization of manipulated images. A supervised and supervised fusion approach are also describe to detect spliced forgeries. The semi-supervised one-class method does not require any prior knowledge of the type of manipulations that an adversary could insert in the satellite imagery. First, a new method known as Satellite SVDD (Sat-SVDD) which adapts the Deep SVDD technique is described. Another semi-supervised one-class one-class detection technique based on deep belief networks (DBN) for splicing detection and localization is then discussed. Multiple configurations of the Deep Belief network were compared to other common one-class classification methods. Finally, a semi-supervised one-class technique that uses a Vision Transformer to detect spliced areas within satellite images is introduced. The supervised method does not require prior knowledge of the type of manipulations inserted into the satellite imagery. A supervised method known as Nested Attention U-Net, to detect spliced. The supervised fusion approach known as Sat U-Net fuses the results of two exiting forensic splicing localization methods to increase their overall accuracy. Sat U-Net is a U-Net based architecture exploiting several Transformers to enhance the splicing detection performance. Sat U-Net fuses the outputs of two semi-supervised one-class splicing detection methods, Gated PixelCNN Ensemble and Vision Transformer, to produce a heatmap highlighting the manipulated image region. The supervised fusion approach trained on images from one satellite can be lightly retrained on few images from another satellite to detect spliced regions. In this thesis I introduce five datasets of manipulated satellite images that contain spliced objects. Three of the datasets contains images with spliced objects generated by a generative adversarial network (GAN).

Degree

Ph.D.

Advisors

Zhu, Purdue University.

Subject Area

Morphology

COinS