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1	An Adversarial Approach to Spliced Forgery Detection and Localization in Satellite Imagery Emily R Bartusiak (6630773) 11 June 2019 (has links) The widespread availability of image editing tools and improvements in image processing techniques make image manipulation feasible for the general population. Oftentimes, easy-to-use yet sophisticated image editing tools produce results that contain modifications 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, such as inserting objects into an image to hide existing scenes and structures. In this thesis, 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. Image Processing Image Processing Machine Learning Electrical Engineering Computer Engineering Electrical and Computer Engineering ECE Satellite Imagery cGAN Media Forensics Digital Forensics Image Forensic Analysis Spliced Forgery Spliced Objects Splicing Doctored Images Image Manipulation Image Editing Falsified Imagery Forged Images Forgery Localization Forgery Detection

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An Adversarial Approach to Spliced Forgery Detection and Localization in Satellite Imagery