Mr. Janos Horvath Profile

Janos Horvath

at Purdue Univ

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SPIE Journal Paper | 1 November 2022 Open Access

Manipulation and generation of synthetic satellite images using deep learning models

Lydia Abady, János Horváth, Benedetta Tondi, Edward Delp, Mauro Barni

JARS, Vol. 16, Issue 04, 046504, (November 2022) https://doi.org/10.1117/12.10.1117/1.JRS.16.046504

KEYWORDS: Satellites, Satellite imaging, Earth observing sensors, Vegetation, Transformers, RGB color model, Gallium nitride, Image segmentation, Data modeling, Near infrared

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Generation and manipulation of digital images based on deep learning (DL) are receiving increasing attention for both benign and malevolent uses. As the importance of satellite imagery is increasing, DL has started being used also for the generation of synthetic satellite images. However, the direct use of techniques developed for computer vision applications is not possible, due to the different nature of satellite images. The goal of our work is to describe a number of methods to generate manipulated and synthetic satellite images. To be specific, we focus on two different types of manipulations: full image modification and local splicing. In the former case, we rely on generative adversarial networks commonly used for style transfer applications, adapting them to implement two different kinds of transfer: (i) land cover transfer, aiming at modifying the image content from vegetation to barren and vice versa and (ii) season transfer, aiming at modifying the image content from winter to summer and vice versa. With regard to local splicing, we present two different architectures. The first one uses image generative pretrained transformer and is trained on pixel sequences in order to predict pixels in semantically consistent regions identified using watershed segmentation. The second technique uses a vision transformer operating on image patches rather than on a pixel by pixel basis. We use the trained vision transformer to generate synthetic image segments and splice them into a selected region of the to-be-manipulated image. All the proposed methods generate highly realistic, synthetic, and satellite images. Among the possible applications of the proposed techniques, we mention the generation of proper datasets for the evaluation and training of tools for the analysis of satellite images.

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Proceedings Article | 27 May 2022 Paper

Sat U-net: a fusion based method for forensic splicing localization in satellite images

János Horváth, Ziyue Xiang, Edoardo Daniele Cannas, Paolo Bestagini, Stefano Tubaro, Edward Delp

Proceedings Volume 12100, 1210002 (2022) https://doi.org/10.1117/12.2616150

KEYWORDS: Image fusion, Satellites, Satellite imaging, Earth observing sensors, Transformers, Fusion splicing, Forensic science, Image segmentation, Satellite communications, Image analysis

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Satellite images are widely available to the public. These satellite images are used in various fields including natural disaster analysis, meteorology and agriculture. As with any type of images, satellite images can be altered using image manipulation tools. A common manipulation is splicing, i.e., pasting on top of an image a region coming from a different source image. Most manipulation detection methods designed for images captured by “consumer cameras" tend to fail when used with satellite images. In this paper we propose a machine learning approach, Sat U-Net, to fuse the results of two exiting forensic splicing localization methods to increase their overall accuracy and robustness. Sat U-Net is a U-Net based architecture exploiting several Transformers to enhance the performance. Sat U-Net fuses the outputs of two unsupervised splicing detection methods, Gated PixelCNN Ensemble and Vision Transformer, to produce a heatmap highlighting the manipulated image region. We show that our fusion approach trained on images from one satellite can be lightly retrained on few images from another satellite to detect spliced regions. We compare our approach to well-known splicing detection methods (i.e., Noiseprint) and segmentation techniques (i.e., U-Net and Nested Attention U-Net). We conducted our experiments on two large datasets: one dataset contains images from Sentinel 2 satellites and the other one contains images from Worldview 3 satellite. Our experiments show that our proposed fusion method performs well when compared to other techniques in localizing spliced areas using Jaccard Index and Dice Score as metrics on both datasets.

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