5 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
Towards Effective Image Forensics via A Novel Computationally Efficient Framework and A New Image Splice Dataset
Splice detection models are the need of the hour since splice manipulations
can be used to mislead, spread rumors and create disharmony in society.
However, there is a severe lack of image splicing datasets, which restricts the
capabilities of deep learning models to extract discriminative features without
overfitting. This manuscript presents two-fold contributions toward splice
detection. Firstly, a novel splice detection dataset is proposed having two
variants. The two variants include spliced samples generated from code and
through manual editing. Spliced images in both variants have corresponding
binary masks to aid localization approaches. Secondly, a novel
Spatio-Compression Lightweight Splice Detection Framework is proposed for
accurate splice detection with minimum computational cost. The proposed
dual-branch framework extracts discriminative spatial features from a
lightweight spatial branch. It uses original resolution compression data to
extract double compression artifacts from the second branch, thereby making it
'information preserving.' Several CNNs are tested in combination with the
proposed framework on a composite dataset of images from the proposed dataset
and the CASIA v2.0 dataset. The best model accuracy of 0.9382 is achieved and
compared with similar state-of-the-art methods, demonstrating the superiority
of the proposed framework
Manipulation and generation of synthetic satellite images using deep learning models
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. (c) The Authors. Published by SPIE under a Creative Commons Attribution 4.0 International License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI
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