Evaluation of Image Forgery Detection Using Multi-Scale Weber Local Descriptors

Due to the maturing of digital image processing techniques, there are many tools that can forge an image easily without leaving visible traces and lead to the problem of the authentication of digital images. Based on the assumption that forgery alters the texture micro-patterns in a digital image and texture descriptors can be used for modeling this change; we employed two stat-of-the-art local texture descriptors: multi-scale Weber's law descriptor (multi-WLD) and multi-scale local binary pattern (multi-LBP) for splicing and copy-move forgery detection. As the tamper traces are not visible to open eyes, so the chrominance components of an image encode these traces and were used for modeling tamper traces with the texture descriptors. To reduce the dimension of the feature space and get rid of redundant features, we employed locally learning based (LLB) algorithm. For identifying an image as authentic or tampered, Support vector machine (SVM) was used. This paper presents the thorough investigation for the validation of this forgery detection method. The experiments were conducted on three benchmark image data sets, namely, CASIA v1.0, CASIA v2.0, and Columbia color. The experimental results showed that the accuracy rate of multi-WLD based method was 94.19% on CASIA v1.0, 96.52% on CASIA v2.0, and 94.17% on Columbia data set. It is not only significantly better than multi-LBP based method, but also it outperforms other stat-of-the-art similar forgery detection methods.

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Uncertainty-Aware Deep Classifiers Using Generative Models

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v34i04.6015 ◽

2020 ◽

Vol 34 (04) ◽

pp. 5620-5627 ◽

Cited By ~ 1

Author(s):

Murat Sensoy ◽

Lance Kaplan ◽

Federico Cerutti ◽

Maryam Saleki

Keyword(s):

Neural Networks ◽

Epistemic Uncertainty ◽

Feature Space ◽

Generative Models ◽

Detection Methods ◽

Generative Adversarial Networks ◽

Data Sets ◽

Bayesian Approaches ◽

Data Set ◽

Auxiliary Data

Deep neural networks are often ignorant about what they do not know and overconfident when they make uninformed predictions. Some recent approaches quantify classification uncertainty directly by training the model to output high uncertainty for the data samples close to class boundaries or from the outside of the training distribution. These approaches use an auxiliary data set during training to represent out-of-distribution samples. However, selection or creation of such an auxiliary data set is non-trivial, especially for high dimensional data such as images. In this work we develop a novel neural network model that is able to express both aleatoric and epistemic uncertainty to distinguish decision boundary and out-of-distribution regions of the feature space. To this end, variational autoencoders and generative adversarial networks are incorporated to automatically generate out-of-distribution exemplars for training. Through extensive analysis, we demonstrate that the proposed approach provides better estimates of uncertainty for in- and out-of-distribution samples, and adversarial examples on well-known data sets against state-of-the-art approaches including recent Bayesian approaches for neural networks and anomaly detection methods.

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A New Copy Move Forgery Detection Technique using Adaptive Over-segementation and Feature Point Matching

Bulletin of Electrical Engineering and Informatics ◽

10.11591/eei.v7i3.754 ◽

2018 ◽

Vol 7 (3) ◽

pp. 345-349

Author(s):

Anil Gupta

Keyword(s):

Digital Image ◽

Feature Point ◽

Detection Methods ◽

Forgery Detection ◽

Point Matching ◽

Detection Scheme ◽

Image Forgery ◽

Image Forgery Detection ◽

Feature Point Matching ◽

Copy Move Forgery Detection

With the development of Image processing editing tools and software, an image can be easily manipulated. The image manipulation detection is vital for the reason that an image can be used as legal evidence, in the field of forensics investigations, and also in numerous various other fields. The image forgery detection based on pixels aims to validate the digital image authenticity with no aforementioned information of the main image. There are several means intended for tampering a digital image, for example, copy-move or splicing, resampling a digital image (stretch, rotate, resize), removal as well as the addition of an object from your image. Copy move image forgery detection is utilized to figure out the replicated regions as well as the pasted parts, however forgery detection may possibly vary dependant on whether or not there is virtually any post-processing on the replicated part before inserting the item completely to another party. Typically, forgers utilize many operations like rotation, filtering, JPEG compression, resizing as well as the addition of noise to the main image before pasting, that make this thing challenging to recognize the copy move image forgery. Hence, forgery detector needs to be robust to any or all manipulations and also the latest editing software tools. This research paper illustrates recent issues in the techniques of forgery detection and proposes a advanced copy–move forgery detection scheme using adaptive over-segmentation and feature point matching. The proposed scheme integrates both block-based and key point-based forgery detection methods.

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Closing the Performance Gap between Siamese Networks for Dissimilarity Image Classification and Convolutional Neural Networks

Sensors ◽

10.3390/s21175809 ◽

2021 ◽

Vol 21 (17) ◽

pp. 5809

Author(s):

Loris Nanni ◽

Giovanni Minchio ◽

Sheryl Brahnam ◽

Davide Sarraggiotto ◽

Alessandra Lumini

Keyword(s):

Image Classification ◽

Image Data ◽

Support Vector ◽

Final Decision ◽

Data Sets ◽

Data Set ◽

Additional Information ◽

New Strategy ◽

Fully Connected ◽

Siamese Networks

In this paper, we examine two strategies for boosting the performance of ensembles of Siamese networks (SNNs) for image classification using two loss functions (Triplet and Binary Cross Entropy) and two methods for building the dissimilarity spaces (FULLY and DEEPER). With FULLY, the distance between a pattern and a prototype is calculated by comparing two images using the fully connected layer of the Siamese network. With DEEPER, each pattern is described using a deeper layer combined with dimensionality reduction. The basic design of the SNNs takes advantage of supervised k-means clustering for building the dissimilarity spaces that train a set of support vector machines, which are then combined by sum rule for a final decision. The robustness and versatility of this approach are demonstrated on several cross-domain image data sets, including a portrait data set, two bioimage and two animal vocalization data sets. Results show that the strategies employed in this work to increase the performance of dissimilarity image classification using SNN are closing the gap with standalone CNNs. Moreover, when our best system is combined with an ensemble of CNNs, the resulting performance is superior to an ensemble of CNNs, demonstrating that our new strategy is extracting additional information.

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Coral Image Segmentation with Point-Supervision via Latent Dirichlet Allocation with Spatial Coherence

Journal of Marine Science and Engineering ◽

10.3390/jmse9020157 ◽

2021 ◽

Vol 9 (2) ◽

pp. 157

Author(s):

Xi Yu ◽

Bing Ouyang ◽

Jose C. Principe

Keyword(s):

Image Segmentation ◽

Deep Learning ◽

Latent Dirichlet Allocation ◽

Spatial Coherence ◽

Image Data ◽

Feature Space ◽

Data Sets ◽

Marine Animals ◽

Data Set ◽

Dirichlet Allocation

Deep neural networks provide remarkable performances on supervised learning tasks with extensive collections of labeled data. However, creating such large well-annotated data sets requires a considerable amount of resources, time and effort, especially for underwater images data sets such as corals and marine animals. Therefore, the overreliance on labels is one of the main obstacles for widespread applications of deep learning methods. In order to overcome this need for large annotated dataset, this paper proposes a label-efficient deep learning framework for image segmentation using only very sparse point-supervision. Our approach employs a latent Dirichlet allocation (LDA) with spatial coherence on feature space to iteratively generate pseudo labels. The method requires, as an initial condition, a Wide Residual Network (WRN) trained with sparse labels and mutual information constraints. The proposed method is evaluated on the sparsely labeled coral image data set collected from the Pulley Ridge region in the Gulf of Mexico. Experiments show that our method can improve image segmentation performance against sparsely labeled samples and achieves better results compared with other semi-supervised approaches.

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New algorithm for biological objects' shape evaluation and data reduction

Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis ◽

10.11118/actaun201058010013 ◽

2010 ◽

Vol 58 (1) ◽

pp. 13-20 ◽

Cited By ~ 2

Author(s):

Stanislav Bartoň ◽

Libor Severa ◽

Jaroslav Buchar

Keyword(s):

High Resolution ◽

Digital Image ◽

Least Squares Method ◽

Image Data ◽

Data Sets ◽

Data Set ◽

Digital Photo ◽

Biological Objects ◽

Acceptable Accuracy ◽

2D Images

The paper presents the software procedure (using MAPLE 11) intended for considerable reduction of digital image data set to more easily treatable extent. The example with image of peach stone is presented. Peach stone, displayed on the digital photo, was represented as a polygon described by the coordinates of the pixels creating its perimeter. The photos taken in high resolution (and corresponding data sets) contain coordinates of thousands of pixels - polygon's vertexes. Presented approach substitutes this polygon by the new one, where smaller number of vertexes is used. The task is solved by use of adapted least squares method. The presented algorithm enables reduction of number of vertexes to 10 % of its original extent with acceptable accuracy +/− one pixel (distance between initial and final polygon). The procedure can be used for processing of similar types of 2D images and acceleration of following computations.

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Deep Features for Training Support Vector Machines

Journal of Imaging ◽

10.3390/jimaging7090177 ◽

2021 ◽

Vol 7 (9) ◽

pp. 177

Author(s):

Loris Nanni ◽

Stefano Ghidoni ◽

Sheryl Brahnam

Keyword(s):

Computer Vision ◽

Support Vector Machines ◽

Vision System ◽

Image Data ◽

Support Vector ◽

Data Sets ◽

Data Set ◽

Training Support ◽

Reduction Techniques ◽

Vector Machines

Features play a crucial role in computer vision. Initially designed to detect salient elements by means of handcrafted algorithms, features now are often learned using different layers in convolutional neural networks (CNNs). This paper develops a generic computer vision system based on features extracted from trained CNNs. Multiple learned features are combined into a single structure to work on different image classification tasks. The proposed system was derived by testing several approaches for extracting features from the inner layers of CNNs and using them as inputs to support vector machines that are then combined by sum rule. Several dimensionality reduction techniques were tested for reducing the high dimensionality of the inner layers so that they can work with SVMs. The empirically derived generic vision system based on applying a discrete cosine transform (DCT) separately to each channel is shown to significantly boost the performance of standard CNNs across a large and diverse collection of image data sets. In addition, an ensemble of different topologies taking the same DCT approach and combined with global mean thresholding pooling obtained state-of-the-art results on a benchmark image virus data set.

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Closing the Performance Gap between Siamese Networks for Dissimilarity Image Classification and Convolutional Neural Networks

10.20944/preprints202108.0094.v1 ◽

2021 ◽

Author(s):

Loris Nanni ◽

Giovanni Minchio ◽

Sheryl Brahnam ◽

Davide Sarraggiotto ◽

Alessandra Lumini

Keyword(s):

Image Classification ◽

Image Data ◽

Support Vector ◽

Final Decision ◽

Data Sets ◽

Data Set ◽

Additional Information ◽

New Strategy ◽

Fully Connected ◽

Siamese Networks

In this paper, we examine two strategies for boosting the performance of ensembles of Siamese networks (SNNs) for image classification using two loss functions (Triplet and Binary Cross Entropy) and two methods for building the dissimilarity spaces (FULLY and DEEPER). With FULLY, the distance between a pattern and a prototype is calculated by comparing two images using the fully connected layer of the Siamese network. With DEEPER, each pattern is described using a deeper layer combined with dimensionality reduction. The basic design of the SNNs takes advantage of supervised k-means clustering for building the dissimilarity spaces that train a set of support vector machines, which are then combined by sum rule for a final decision. The robustness and versatility of this approach are demonstrated on several cross-domain image data sets, including a portrait data set, two bioimage and two animal vocalization data sets. Results show that the strategies employed in this work to increase the performance of dissimilarity image classification using SNN is closing the gap with standalone CNNs. Moreover, when our best system is combined with an ensemble of CNNs, the resulting performance is superior to an ensemble of CNNs, demonstrating that our new strategy is extracting additional information.

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Comparison of Novelty Detection Methods for Detection of Various Rotary Machinery Faults

Sensors ◽

10.3390/s21103536 ◽

2021 ◽

Vol 21 (10) ◽

pp. 3536

Author(s):

Jakub Górski ◽

Adam Jabłoński ◽

Mateusz Heesch ◽

Michał Dziendzikowski ◽

Ziemowit Dworakowski

Keyword(s):

Novelty Detection ◽

Feature Space ◽

Detection Methods ◽

Data Sets ◽

Similar Distribution ◽

Detection Algorithms ◽

Rotary Machinery ◽

Detection Approach ◽

Input Reconstruction ◽

Quantitative Indicators

Condition monitoring is an indispensable element related to the operation of rotating machinery. In this article, the monitoring system for the parallel gearbox was proposed. The novelty detection approach is used to develop the condition assessment support system, which requires data collection for a healthy structure. The measured signals were processed to extract quantitative indicators sensitive to the type of damage occurring in this type of structure. The indicator’s values were used for the development of four different novelty detection algorithms. Presented novelty detection models operate on three principles: feature space distance, probability distribution, and input reconstruction. One of the distance-based models is adaptive, adjusting to new data flowing in the form of a stream. The authors test the developed algorithms on experimental and simulation data with a similar distribution, using the training set consisting mainly of samples generated by the simulator. Presented in the article results demonstrate the effectiveness of the trained models on both data sets.

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Multi-phase classification by a least-squares support vector machine approach in tomography images of geological samples

Solid Earth ◽

10.5194/se-7-481-2016 ◽

2016 ◽

Vol 7 (2) ◽

pp. 481-492 ◽

Cited By ~ 8

Author(s):

Faisal Khan ◽

Frieder Enzmann ◽

Michael Kersten

Keyword(s):

Support Vector Machine ◽

Least Squares ◽

Image Data ◽

Main Beam ◽

Support Vector ◽

Geological Samples ◽

Data Set ◽

Corrected Image ◽

Phase Classification ◽

Multi Phase

Abstract. Image processing of X-ray-computed polychromatic cone-beam micro-tomography (μXCT) data of geological samples mainly involves artefact reduction and phase segmentation. For the former, the main beam-hardening (BH) artefact is removed by applying a best-fit quadratic surface algorithm to a given image data set (reconstructed slice), which minimizes the BH offsets of the attenuation data points from that surface. A Matlab code for this approach is provided in the Appendix. The final BH-corrected image is extracted from the residual data or from the difference between the surface elevation values and the original grey-scale values. For the segmentation, we propose a novel least-squares support vector machine (LS-SVM, an algorithm for pixel-based multi-phase classification) approach. A receiver operating characteristic (ROC) analysis was performed on BH-corrected and uncorrected samples to show that BH correction is in fact an important prerequisite for accurate multi-phase classification. The combination of the two approaches was thus used to classify successfully three different more or less complex multi-phase rock core samples.

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An Expert System Based on Fisher Score and LS-SVM for Cardiac Arrhythmia Diagnosis

Computational and Mathematical Methods in Medicine ◽

10.1155/2013/849674 ◽

2013 ◽

Vol 2013 ◽

pp. 1-6 ◽

Cited By ~ 19

Author(s):

Ersen Yılmaz

Keyword(s):

Expert System ◽

Cardiac Arrhythmia ◽

Feature Space ◽

Support Vector ◽

Feature Subset ◽

Fisher Score ◽

Data Set ◽

Second Stage ◽

Vector Machines ◽

Two Stages

An expert system having two stages is proposed for cardiac arrhythmia diagnosis. In the first stage, Fisher score is used for feature selection to reduce the feature space dimension of a data set. The second stage is classification stage in which least squares support vector machines classifier is performed by using the feature subset selected in the first stage to diagnose cardiac arrhythmia. Performance of the proposed expert system is evaluated by using an arrhythmia data set which is taken from UCI machine learning repository.

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