scholarly journals Forgery Detection in Digital Images by Multi-Scale Noise Estimation

2021 ◽  
Vol 7 (7) ◽  
pp. 119
Author(s):  
Marina Gardella ◽  
Pablo Musé ◽  
Jean-Michel Morel ◽  
Miguel Colom
Keyword(s):  
Noise Model ◽  
Correlated Noise ◽  
Forgery Detection ◽  
Color Channel ◽  
Image Block ◽  
Multi Scale ◽  
Compressed Images ◽  
Inconsistency Analysis ◽  
Highly Correlated ◽  
The Moment

A complex processing chain is applied from the moment a raw image is acquired until the final image is obtained. This process transforms the originally Poisson-distributed noise into a complex noise model. Noise inconsistency analysis is a rich source for forgery detection, as forged regions have likely undergone a different processing pipeline or out-camera processing. We propose a multi-scale approach, which is shown to be suitable for analyzing the highly correlated noise present in JPEG-compressed images. We estimate a noise curve for each image block, in each color channel and at each scale. We then compare each noise curve to its corresponding noise curve obtained from the whole image by counting the percentage of bins of the local noise curve that are below the global one. This procedure yields crucial detection cues since many forgeries create a local noise deficit. Our method is shown to be competitive with the state of the art. It outperforms all other methods when evaluated using the MCC score, or on forged regions large enough and for colorization attacks, regardless of the evaluation metric.

scholarly journals Multi-scale Simulation in Railway Planning and Operation

2012 ◽  
Vol 23 (6) ◽  
pp. 511-517 ◽  
Author(s):  
Yong Cui ◽  
Ullrich Martin
Keyword(s):  
Simulation Model ◽  
Process Simulation ◽  
Level Of Detail ◽  
Smooth Transition ◽  
Simulation Methods ◽  
Multi Scale ◽  
Railway Planning ◽  
The Moment ◽  
Discrete Scaling ◽  
Planning And Operation

Simulation methods are widely used in railway planning and operation. However, at the moment there are no applicable solutions in the process simulation for a smooth transition among different infrastructure levels on the basis of a unified structure with consistent algorithm. In this paper, a multi-scale simulation model is designed with consideration of the level of detail of the investigated infrastructure model and the homogeneity of the processes running in the simulation model. A comprehensive and synthesized view of railway planning and operation is therefore obtained. Within the multi-scale simulation model, railway planning and operation processes can be simulated, evaluated and optimized consistently. KEY WORDS: railway planning, simulation, multi-scale, aggregation, discrete scaling, continuous scaling, homogenous process, inhomogeneous process

scholarly journals Change Detection Based on Multi-Grained Cascade Forest and Multi-Scale Fusion for SAR Images

2019 ◽  
Vol 11 (2) ◽  
pp. 142 ◽  
Author(s):  
Wenping Ma ◽  
Hui Yang ◽  
Yue Wu ◽  
Yunta Xiong ◽  
Tao Hu ◽  
...  
Keyword(s):  
Change Detection ◽  
Representation Learning ◽  
Local Information ◽  
Learning Ability ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Image Block ◽  
Sar Images ◽  
Difference Image ◽  
Multi Scale

In this paper, a novel change detection approach based on multi-grained cascade forest(gcForest) and multi-scale fusion for synthetic aperture radar (SAR) images is proposed. It detectsthe changed and unchanged areas of the images by using the well-trained gcForest. Most existingchange detection methods need to select the appropriate size of the image block. However, thesingle size image block only provides a part of the local information, and gcForest cannot achieve agood effect on the image representation learning ability. Therefore, the proposed approach choosesdifferent sizes of image blocks as the input of gcForest, which can learn more image characteristicsand reduce the influence of the local information of the image on the classification result as well.In addition, in order to improve the detection accuracy of those pixels whose gray value changesabruptly, the proposed approach combines gradient information of the difference image with theprobability map obtained from the well-trained gcForest. Therefore, the image edge information canbe enhanced and the accuracy of edge detection can be improved by extracting the image gradientinformation. Experiments on four data sets indicate that the proposed approach outperforms otherstate-of-the-art algorithms.

Moment-Based Hybrid Polynomial Chaos Method for Interval and Random Uncertain Analysis of Periodical Composite Structural-Acoustic System with Multi-Scale Parameters

2020 ◽  
pp. 2050041
Author(s):  
Ning Chen ◽  
Jiaojiao Chen ◽  
Shengwen Yin
Keyword(s):  
Polynomial Chaos ◽  
Random Variable ◽  
Acoustic System ◽  
Passenger Compartment ◽  
Scale Parameters ◽  
Multi Scale ◽  
Arbitrary Polynomial ◽  
Random Moment ◽  
The Moment ◽  
Probability Density Function Pdf

An interval and random moment-based arbitrary polynomial chaos method (IRMAPCM) is proposed in this paper for the analysis of periodical composite structural-acoustic systems with multi-scale uncertain-but-bounded parameters. In IRMAPCM, the response of structural-acoustic system is approximated as moment-based arbitrary polynomial chaos (maPC) expansion. IRMAPCM can construct the polynomial basis according to the moment of the random variable without knowing the Probability Density Function (PDF), which can avoid the errors introduced by estimating the PDF. Numerical examples of a hexahedral box and an automobile passenger compartment are given to investigate the effectiveness of IRMAPCM for the prediction of the sound pressure response of structural-acoustic systems.

A Novel Distribution Parameter Fitting Algorithm of Correlation Noise Model for Distributed Video Coding

2014 ◽  
Vol 599-601 ◽  
pp. 1360-1363
Author(s):  
Xiang Yan Liang ◽  
Zhen Hua Tang ◽  
Ya Dan Luo ◽  
Tuan Fa Qin
Keyword(s):  
Video Coding ◽  
Euclidean Distance ◽  
Rate Distortion ◽  
Distributed Video Coding ◽  
Distribution Parameter ◽  
Noise Model ◽  
Correlated Noise ◽  
Minimum Euclidean Distance ◽  
Parameter Fitting ◽  
Fitting Algorithm

In order to improve the accuracy of correlated noise (CN) model for distributed video coding (DVC), this paper proposes a novel distribution parameter fitting algorithm based on the minimum Euclidean distance. The presented method can obtain the final fitted distribution parameter by using the minimum Euclidean distance to compare the Laplace probability density function (PDF) with the PDF computed utilizing the actual residual frame data. Experiment results show that the proposed distribution parameter fitting algorithm can improve the rate-distortion (R-D) performance of DVC significantly.

scholarly journals Closely Spaced MEG Source Localization and Functional Connectivity Analysis Using a New Prewhitening Invariance of Noise Space Algorithm

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Junpeng Zhang ◽  
Yuan Cui ◽  
Lihua Deng ◽  
Ling He ◽  
Junran Zhang ◽  
...  
Keyword(s):  
Source Localization ◽  
Neural Plasticity ◽  
Source Analysis ◽  
Correlated Noise ◽  
Localization Accuracy ◽  
Cortical Sources ◽  
Noise Simulation ◽  
Time Courses ◽  
Adverse Influence ◽  
Highly Correlated

This paper proposed a prewhitening invariance of noise space (PW-INN) as a new magnetoencephalography (MEG) source analysis method, which is particularly suitable for localizing closely spaced and highly correlated cortical sources under real MEG noise. Conventional source localization methods, such as sLORETA and beamformer, cannot distinguish closely spaced cortical sources, especially under strong intersource correlation. Our previous work proposed an invariance of noise space (INN) method to resolve closely spaced sources, but its performance is seriously degraded under correlated noise between MEG sensors. The proposed PW-INN method largely mitigates the adverse influence of correlated MEG noise by projecting MEG data to a new space defined by the orthogonal complement of dominant eigenvectors of correlated MEG noise. Simulation results showed that PW-INN is superior to INN, sLORETA, and beamformer in terms of localization accuracy for closely spaced and highly correlated sources. Lastly, source connectivity between closely spaced sources can be satisfactorily constructed from source time courses estimated by PW-INN but not from results of other conventional methods. Therefore, the proposed PW-INN method is a promising MEG source analysis to provide a high spatial-temporal characterization of cortical activity and connectivity, which is crucial for basic and clinical research of neural plasticity.

scholarly journals Robust forgery detection for compressed images using CNN supervision

2020 ◽  
Vol 2 ◽  
pp. 100112
Author(s):  
Boubacar Diallo ◽  
Thierry Urruty ◽  
Pascal Bourdon ◽  
Christine Fernandez-Maloigne
Keyword(s):  
Forgery Detection ◽  
Compressed Images

Performance of topological quantum error correction in the presence of correlated noise

2018 ◽  
Vol 18 (9&10) ◽  
pp. 743-778
Author(s):  
Muhammad Ahsan ◽  
Syed Abbas Zilqurnain Naqvi
Keyword(s):  
Error Correction ◽  
Noise Level ◽  
Quantum Computer ◽  
Quantum Error Correction ◽  
Noise Model ◽  
Correlated Noise ◽  
Computational Accuracy ◽  
Topological Quantum ◽  
Large Numbers ◽  
Quantum Error

We investigate the efficacy of topological quantum error-correction in correlated noise model which permits collective coupling of all the codeword qubits to the same non-Markovian environment. In this noise model, the probability distribution over set of phase-flipped qubits, decays sub-exponentially in the size of the set and carries non-trivial likelihood of the occurring large numbers of qubits errors. We find that in the presence of noise correlation, one cannot guarantee arbitrary high computational accuracy simply by incrementing the codeword size while retaining constant noise level per qubit operation. However, if instead, per-operation qubit error probability in an n-qubits long codeword is reduced O(\sqrt{n}) times below the accuracy threshold, arbitrarily accurate quantum computation becomes feasible with acceptable scaling of the codeword size. Our results suggest that progressively reducing noise level in qubits and gates is as important as continuously integrating more qubits to realize scalable and reliable quantum computer.

Sign in / Sign up

Export Citation Format

Share Document