77 research outputs found
Manipulation Detection in Satellite Images Using Deep Belief Networks
Satellite images are more accessible with the increase of commercial
satellites being orbited. These images are used in a wide range of applications
including agricultural management, meteorological prediction, damage assessment
from natural disasters, and cartography. Image manipulation tools including
both manual editing tools and automated techniques can be easily used to tamper
and modify satellite imagery. One type of manipulation that we examine in this
paper is the splice attack where a region from one image (or the same image) is
inserted (spliced) into an image. In this paper, we present a one-class
detection method based on deep belief networks (DBN) for splicing detection and
localization without using any prior knowledge of the manipulations. We
evaluate the performance of our approach and show that it provides good
detection and localization accuracies in small forgeries compared to other
approaches
Detection and localization enhancement for satellite images with small forgeries using modified GAN-based CNN structure
The image forgery process can be simply defined as inserting some objects of different sizes to vanish some structures or scenes. Satellite images can be forged in many ways, such as copy-paste, copy-move, and splicing processes. Recent approaches present a generative adversarial network (GAN) as an effective method for identifying the presence of spliced forgeries and identifying their locations with a higher detection accuracy of large- and medium-sized forgeries. However, such recent approaches clearly show limited detection accuracy of small-sized forgeries. Accordingly, the localization step of such small-sized forgeries is negatively impacted. In this paper, two different approaches for detecting and localizing small-sized forgeries in satellite images are proposed. The first approach is inspired by a recently presented GAN-based approach and is modified to an enhanced version. The experimental results manifest that the detection accuracy of the first proposed approach noticeably increased to 86% compared to its inspiring one with 79% for the small-sized forgeries. Whereas, the second proposed approach uses a different design of a CNN-based discriminator to significantly enhance the detection accuracy to 94%, using the same dataset obtained from NASA and the US Geological Survey (USGS) for validation and testing. Furthermore, the results show a comparable detection accuracy in large- and medium-sized forgeries using the two proposed approaches compared to the competing ones. This study can be applied in the forensic field, with clear discrimination between the forged and pristine images
An Overview on the Generation and Detection of Synthetic and Manipulated Satellite Images
Due to the reduction of technological costs and the increase of satellites
launches, satellite images are becoming more popular and easier to obtain.
Besides serving benevolent purposes, satellite data can also be used for
malicious reasons such as misinformation. As a matter of fact, satellite images
can be easily manipulated relying on general image editing tools. Moreover,
with the surge of Deep Neural Networks (DNNs) that can generate realistic
synthetic imagery belonging to various domains, additional threats related to
the diffusion of synthetically generated satellite images are emerging. In this
paper, we review the State of the Art (SOTA) on the generation and manipulation
of satellite images. In particular, we focus on both the generation of
synthetic satellite imagery from scratch, and the semantic manipulation of
satellite images by means of image-transfer technologies, including the
transformation of images obtained from one type of sensor to another one. We
also describe forensic detection techniques that have been researched so far to
classify and detect synthetic image forgeries. While we focus mostly on
forensic techniques explicitly tailored to the detection of AI-generated
synthetic contents, we also review some methods designed for general splicing
detection, which can in principle also be used to spot AI manipulate imagesComment: 25 pages, 17 figures, 5 tables, APSIPA 202
Detection and Localization of GAN Manipulated Multi-spectral Satellite Images
Owing to their realistic features and continuous improvements, images manipulated by Generative Adversarial Network (GAN)
have become a compelling research topic. In this paper, we apply detection and localization to GAN manipulated images by means of models,
based on EfficientNet-B4 architectures. Detection is tested on multiple
generated multi-spectral datasets from several world regions and different
GAN architectures, whereas localization is tested on an inpainted images
dataset of sizes 2048×2048×13. The results obtained for both detection
and localization are shown to be promising
HRFNet: High-Resolution Forgery Network for Localizing Satellite Image Manipulation
Existing high-resolution satellite image forgery localization methods rely on
patch-based or downsampling-based training. Both of these training methods have
major drawbacks, such as inaccurate boundaries between pristine and forged
regions, the generation of unwanted artifacts, etc. To tackle the
aforementioned challenges, inspired by the high-resolution image segmentation
literature, we propose a novel model called HRFNet to enable satellite image
forgery localization effectively. Specifically, equipped with shallow and deep
branches, our model can successfully integrate RGB and resampling features in
both global and local manners to localize forgery more accurately. We perform
various experiments to demonstrate that our method achieves the best
performance, while the memory requirement and processing speed are not
compromised compared to existing methods.Comment: ICIP 202
Datasets, Clues and State-of-the-Arts for Multimedia Forensics: An Extensive Review
With the large chunks of social media data being created daily and the
parallel rise of realistic multimedia tampering methods, detecting and
localising tampering in images and videos has become essential. This survey
focusses on approaches for tampering detection in multimedia data using deep
learning models. Specifically, it presents a detailed analysis of benchmark
datasets for malicious manipulation detection that are publicly available. It
also offers a comprehensive list of tampering clues and commonly used deep
learning architectures. Next, it discusses the current state-of-the-art
tampering detection methods, categorizing them into meaningful types such as
deepfake detection methods, splice tampering detection methods, copy-move
tampering detection methods, etc. and discussing their strengths and
weaknesses. Top results achieved on benchmark datasets, comparison of deep
learning approaches against traditional methods and critical insights from the
recent tampering detection methods are also discussed. Lastly, the research
gaps, future direction and conclusion are discussed to provide an in-depth
understanding of the tampering detection research arena
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