scholarly journals Splicing Localization Based on Noise Level Inconsistencies in Residuals of Color Channel Differences

2019 ◽  
Vol 8 (3) ◽  
pp. 764-769
Keyword(s):  
Noise Level ◽  
State Of The Art ◽  
Forensic Analysis ◽  
Research Community ◽  
Image Splicing ◽  
Color Channel ◽  
Local Statistics ◽  
Image Sharing ◽  
Active Research ◽  
Photo Editing

With the rapid usage of networking sites there is an enormous increase in image sharing over the internet. At the same time altering or tempering images has become much easier with the availability of photo editing software. Splicing is one of the tempering method, where an object from one image is copied and pasted into another image, is often used with the aim of either getting attention for fun or misleading the general masses. Thus, authenticity of images shared on internet is debatable. Active research is going on in the field of image forensics in order to examine the trustworthiness of the images. Amongst several techniques available for dealing with image splicing, the statistical based methods are gaining attention in research community as it uses image’s local statistics. We propose a simple and effective method based on noise inconsistencies in residuals of Color channel difference for forensic analysis to localize the splicing image forgery. First the image is decomposed in to super pixels and extracted in regular shapes. From each super pixel, three color channel differences are extracted and noise level is estimated on the residual. Finally, the super pixels are clustered into two groups using Farthest Distributed Centroids Clustering (FDCC) method for classifying superpixel as tampered or original. The experimental results show the simplicity and effectiveness of the proposed method over the state of the art.

scholarly journals Generate, Segment, and Refine: Towards Generic Manipulation Segmentation

2020 ◽  
Vol 34 (07) ◽  
pp. 13058-13065 ◽  
Author(s):  
Peng Zhou ◽  
Bor-Chun Chen ◽  
Xintong Han ◽  
Mahyar Najibi ◽  
Abhinav Shrivastava ◽  
...  
Keyword(s):  
State Of The Art ◽  
Training Data ◽  
The Internet ◽  
Generation Process ◽  
Image Generation ◽  
Image Sharing ◽  
Current State ◽  
Photo Editing ◽  
Traditional Work ◽  
False News

Detecting manipulated images has become a significant emerging challenge. The advent of image sharing platforms and the easy availability of advanced photo editing software have resulted in a large quantities of manipulated images being shared on the internet. While the intent behind such manipulations varies widely, concerns on the spread of false news and misinformation is growing. Current state of the art methods for detecting these manipulated images suffers from the lack of training data due to the laborious labeling process. We address this problem in this paper, for which we introduce a manipulated image generation process that creates true positives using currently available datasets. Drawing from traditional work on image blending, we propose a novel generator for creating such examples. In addition, we also propose to further create examples that force the algorithm to focus on boundary artifacts during training. Strong experimental results validate our proposal.

scholarly journals Host-Directed Therapies and Anti-Virulence Compounds to Address Anti-Microbial Resistant Tuberculosis Infection

2020 ◽  
Vol 10 (8) ◽  
pp. 2688 ◽  
Author(s):  
Raphael Gries ◽  
Claudia Sala ◽  
Jan Rybniker
Keyword(s):  
Immune Cells ◽  
State Of The Art ◽  
Research Community ◽  
Review Article ◽  
Drug Resistant ◽  
Resistant Tuberculosis ◽  
Current State ◽  
Major Virulence Factor ◽  
Emerging Therapies ◽  
Active Research

Despite global efforts to contain tuberculosis (TB), the disease remains a leading cause of morbidity and mortality worldwide, further exacerbated by the increased resistance to antibiotics displayed by the tubercle bacillus Mycobacterium tuberculosis. In order to treat drug-resistant TB, alternative or complementary approaches to standard anti-TB regimens are being explored. An area of active research is represented by host-directed therapies which aim to modulate the host immune response by mitigating inflammation and by promoting the antimicrobial activity of immune cells. Additionally, compounds that reduce the virulence of M. tuberculosis, for instance by targeting the major virulence factor ESX-1, are being given increased attention by the TB research community. This review article summarizes the current state of the art in the development of these emerging therapies against TB.

scholarly journals VIPPrint: Validating Synthetic Image Detection and Source Linking Methods on a Large Scale Dataset of Printed Documents

2021 ◽  
Vol 7 (3) ◽  
pp. 50
Author(s):  
Anselmo Ferreira ◽  
Ehsan Nowroozi ◽  
Mauro Barni
Keyword(s):  
Large Scale ◽  
State Of The Art ◽  
Child Pornography ◽  
Forensic Analysis ◽  
Synthetic Image ◽  
Image Detection ◽  
Face Images ◽  
Large Scale Dataset ◽  
Scanned Images ◽  
Analysis Of The Images

The possibility of carrying out a meaningful forensic analysis on printed and scanned images plays a major role in many applications. First of all, printed documents are often associated with criminal activities, such as terrorist plans, child pornography, and even fake packages. Additionally, printing and scanning can be used to hide the traces of image manipulation or the synthetic nature of images, since the artifacts commonly found in manipulated and synthetic images are gone after the images are printed and scanned. A problem hindering research in this area is the lack of large scale reference datasets to be used for algorithm development and benchmarking. Motivated by this issue, we present a new dataset composed of a large number of synthetic and natural printed face images. To highlight the difficulties associated with the analysis of the images of the dataset, we carried out an extensive set of experiments comparing several printer attribution methods. We also verified that state-of-the-art methods to distinguish natural and synthetic face images fail when applied to print and scanned images. We envision that the availability of the new dataset and the preliminary experiments we carried out will motivate and facilitate further research in this area.

Effective splicing localization based on noise level inconsistencies

2021 ◽  
pp. 1-11
Author(s):  
P. N. R. L. Chandra Sekhar Author ◽  
T. N. Shankar Author
Keyword(s):  
Digital Technology ◽  
Social Networking Sites ◽  
Noise Level ◽  
Research Work ◽  
Multimedia Content ◽  
Noise Levels ◽  
Multimedia Forensics ◽  
The Past ◽  
Photo Editing ◽  
State Of Art

In the era of digital technology, it becomes easy to share photographs and videos using smartphones and social networking sites to their loved ones. On the other hand, many photo editing tools evolved to make it effortless to alter multimedia content. It makes people accustomed to modifying their photographs or videos either for fun or extracting attention from others. This altering brings a questionable validity and integrity to the kind of multimedia content shared over the internet when used as evidence in Journalism and Court of Law. In multimedia forensics, intense research work is underway over the past two decades to bring trustworthiness to the multimedia content. This paper proposes an efficient way of identifying the manipulated region based on Noise Level inconsistencies of spliced mage. The spliced image segmented into irregular objects and extracts the noise features in both pixel and residual domains. The manipulated region is then exposed based on the cosine similarity of noise levels among pairs of individual objects. The experimental results reveal the effectiveness of the proposed method over other state-of-art methods.

scholarly journals A Dataset of Photos and Videos for Digital Forensics Analysis Using Machine Learning Processing

Data ◽  
2021 ◽  
Vol 6 (8) ◽  
pp. 87
Author(s):  
Sara Ferreira ◽  
Mário Antunes ◽  
Manuel E. Correia
Keyword(s):  
Machine Learning ◽  
Digital Forensics ◽  
State Of The Art ◽  
Forensic Analysis ◽  
Third Party ◽  
Support Vector ◽  
Multimedia Content ◽  
Digital Forensic ◽  
Video Frames ◽  
Forensic Tools

Deepfake and manipulated digital photos and videos are being increasingly used in a myriad of cybercrimes. Ransomware, the dissemination of fake news, and digital kidnapping-related crimes are the most recurrent, in which tampered multimedia content has been the primordial disseminating vehicle. Digital forensic analysis tools are being widely used by criminal investigations to automate the identification of digital evidence in seized electronic equipment. The number of files to be processed and the complexity of the crimes under analysis have highlighted the need to employ efficient digital forensics techniques grounded on state-of-the-art technologies. Machine Learning (ML) researchers have been challenged to apply techniques and methods to improve the automatic detection of manipulated multimedia content. However, the implementation of such methods have not yet been massively incorporated into digital forensic tools, mostly due to the lack of realistic and well-structured datasets of photos and videos. The diversity and richness of the datasets are crucial to benchmark the ML models and to evaluate their appropriateness to be applied in real-world digital forensics applications. An example is the development of third-party modules for the widely used Autopsy digital forensic application. This paper presents a dataset obtained by extracting a set of simple features from genuine and manipulated photos and videos, which are part of state-of-the-art existing datasets. The resulting dataset is balanced, and each entry comprises a label and a vector of numeric values corresponding to the features extracted through a Discrete Fourier Transform (DFT). The dataset is available in a GitHub repository, and the total amount of photos and video frames is 40,588 and 12,400, respectively. The dataset was validated and benchmarked with deep learning Convolutional Neural Networks (CNN) and Support Vector Machines (SVM) methods; however, a plethora of other existing ones can be applied. Generically, the results show a better F1-score for CNN when comparing with SVM, both for photos and videos processing. CNN achieved an F1-score of 0.9968 and 0.8415 for photos and videos, respectively. Regarding SVM, the results obtained with 5-fold cross-validation are 0.9953 and 0.7955, respectively, for photos and videos processing. A set of methods written in Python is available for the researchers, namely to preprocess and extract the features from the original photos and videos files and to build the training and testing sets. Additional methods are also available to convert the original PKL files into CSV and TXT, which gives more flexibility for the ML researchers to use the dataset on existing ML frameworks and tools.

scholarly journals Technology-enhanced and game based learning for children with special needs: a systematic mapping study

2021 ◽  
Author(s):  
Jose A. Gallud ◽  
Monica Carreño ◽  
Ricardo Tesoriero ◽  
Andrés Sandoval ◽  
María D. Lozano ◽  
...  
Keyword(s):  
Special Needs ◽  
State Of The Art ◽  
Research Community ◽  
Systematic Mapping Study ◽  
Children With Special Needs ◽  
Game Based Learning ◽  
Mapping Study ◽  
Systematic Mapping ◽  
Wide Range ◽  
Research Questions

AbstractTechnology-based education of children with special needs has become the focus of many research works in recent years. The wide range of different disabilities that are encompassed by the term “special needs”, together with the educational requirements of the children affected, represent an enormous multidisciplinary challenge for the research community. In this article, we present a systematic literature review of technology-enhanced and game-based learning systems and methods applied on children with special needs. The article analyzes the state-of-the-art of the research in this field by selecting a group of primary studies and answering a set of research questions. Although there are some previous systematic reviews, it is still not clear what the best tools, games or academic subjects (with technology-enhanced, game-based learning) are, out of those that have obtained good results with children with special needs. The 18 articles selected (carefully filtered out of 614 contributions) have been used to reveal the most frequent disabilities, the different technologies used in the prototypes, the number of learning subjects, and the kind of learning games used. The article also summarizes research opportunities identified in the primary studies.

scholarly journals A Robust Context-Based Deep Learning Approach for Highly Imbalanced Hyperspectral Classification

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Juan F. Ramirez Rochac ◽  
Nian Zhang ◽  
Lara A. Thompson ◽  
Tolessa Deksissa
Keyword(s):  
Deep Learning ◽  
State Of The Art ◽  
Mineral Exploration ◽  
Classification Models ◽  
Noise Resistance ◽  
Deep Convolutional Neural Networks ◽  
Current State ◽  
Feature Augmentation ◽  
Active Research ◽  
Hyperspectral Classification

Hyperspectral imaging is an area of active research with many applications in remote sensing, mineral exploration, and environmental monitoring. Deep learning and, in particular, convolution-based approaches are the current state-of-the-art classification models. However, in the presence of noisy hyperspectral datasets, these deep convolutional neural networks underperform. In this paper, we proposed a feature augmentation approach to increase noise resistance in imbalanced hyperspectral classification. Our method calculates context-based features, and it uses a deep convolutional neuronet (DCN). We tested our proposed approach on the Pavia datasets and compared three models, DCN, PCA + DCN, and our context-based DCN, using the original datasets and the datasets plus noise. Our experimental results show that DCN and PCA + DCN perform well on the original datasets but not on the noisy datasets. Our robust context-based DCN was able to outperform others in the presence of noise and was able to maintain a comparable classification accuracy on clean hyperspectral images.

Women in Brazilian CS Research Community

2012 ◽  
pp. 1824-1839
Author(s):  
Mirella M. Moro ◽  
Taisy Weber ◽  
Carla M.D.S. Freitas
Keyword(s):  
Computer Science ◽  
State Of The Art ◽  
Research Community ◽  
Current Situation ◽  
The State ◽  
Do So

Many communities have been concerned with the problem of bringing more girls to technology and science related areas. The authors believe that the first step in order to solve such a problem is to understand the current situation, like to investigate the “state-of-the-art” of the problem. Therefore, in this chapter, they present the first study to identify which areas of Computer Science have more and less feminine participation. In order to do so, they have considered the program committees of the Brazilian conferences in those areas. The authors’ study evaluates the 2008 and previous editions of such conferences. They also discuss some Brazilian initiatives to bring more girls to Computer Science as well present what else can be done.

scholarly journals Least Squares Optimization: From Theory to Practice

Robotics ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 51 ◽  
Author(s):  
Giorgio Grisetti ◽  
Tiziano Guadagnino ◽  
Irvin Aloise ◽  
Mirco Colosi ◽  
Bartolomeo Della Corte ◽  
...  
Keyword(s):  
Computer Vision ◽  
Least Squares ◽  
Open Source ◽  
State Of The Art ◽  
Nonlinear Least Squares ◽  
Research Community ◽  
Specific Domain ◽  
Theory To Practice ◽  
Least Squares Optimization ◽  
Computer Vision Systems

Nowadays, Nonlinear Least-Squares embodies the foundation of many Robotics and Computer Vision systems. The research community deeply investigated this topic in the last few years, and this resulted in the development of several open-source solvers to approach constantly increasing classes of problems. In this work, we propose a unified methodology to design and develop efficient Least-Squares Optimization algorithms, focusing on the structures and patterns of each specific domain. Furthermore, we present a novel open-source optimization system that addresses problems transparently with a different structure and designed to be easy to extend. The system is written in modern C++ and runs efficiently on embedded systemsWe validated our approach by conducting comparative experiments on several problems using standard datasets. The results show that our system achieves state-of-the-art performances in all tested scenarios.

scholarly journals Gaussian Transformer: A Lightweight Approach for Natural Language Inference

2019 ◽  
Vol 33 ◽  
pp. 6489-6496 ◽  
Author(s):  
Maosheng Guo ◽  
Yu Zhang ◽  
Ting Liu
Keyword(s):  
Neural Networks ◽  
Natural Language ◽  
State Of The Art ◽  
Research Area ◽  
High Order Interaction ◽  
Training Time ◽  
Attention Networks ◽  
Local Dependency ◽  
Active Research ◽  
Active Research Area

Natural Language Inference (NLI) is an active research area, where numerous approaches based on recurrent neural networks (RNNs), convolutional neural networks (CNNs), and self-attention networks (SANs) has been proposed. Although obtaining impressive performance, previous recurrent approaches are hard to train in parallel; convolutional models tend to cost more parameters, while self-attention networks are not good at capturing local dependency of texts. To address this problem, we introduce a Gaussian prior to selfattention mechanism, for better modeling the local structure of sentences. Then we propose an efficient RNN/CNN-free architecture named Gaussian Transformer for NLI, which consists of encoding blocks modeling both local and global dependency, high-order interaction blocks collecting the evidence of multi-step inference, and a lightweight comparison block saving lots of parameters. Experiments show that our model achieves new state-of-the-art performance on both SNLI and MultiNLI benchmarks with significantly fewer parameters and considerably less training time. Besides, evaluation using the Hard NLI datasets demonstrates that our approach is less affected by the undesirable annotation artifacts.

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