Copy-move forgery detection using convolutional neural network and K-mean clustering

Pourkashani, Ava; Shahbahrami, Asadollah; Akoushideh, Alireza

doi:10.11591/ijece.v11i3.pp2604-2612

Cited by 11 publications

(9 citation statements)

References 19 publications

(25 reference statements)

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“…On the other hand, if the pixel intensity histogram is distributed throughout the histogram range, the image will have a higher contrast level. This indicator was used to assess the image enhancement methods, using a variety of illuminations for optimal performance [28], [85], [86], [105], [140], [166].…”

Section: Analysis Of the Image Enhancement Methodsmentioning

confidence: 99%

Integrated NIR-HE based SPOT-5 image enhancement method for features preservation and edge detection

Zulkifle

Hassan

Ahmad

et al. 2021

IJEECS

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Recently, many researchers have directed their attention to methods of predicting shorelines by the use of multispectral images. Thus, a simple and optimised method using image enhancements is proposed to improve the low contrast of the Satellite pour l'Observation de la Terre-5 (SPOT-5) images in the detection of shorelines. The near-infrared (NIR) channel is important in this study to ensure the contrast of the vegetated area and sea classification, due to the high reflectance of leaves in the near infrared wavelength region. This study used five scenes of interest to show the different results in shoreline detection. The results demonstrated that the proposed method performed in an enhanced manner as compared to current methods when dealing with the low contrast ratio of SPOT-5 images. As a result, by utilising the near-infrared histogram equalization (NIR-HE), the contrast of all datasets was efficiently restored, producing a higher efficiency in edge detection, and achieving higher overall accuracy. The improved filtering method showed significantly better shoreline detection results than the other filter methods. It was concluded that this method would be useful for detecting and monitoring the shoreline edge in Tanjung Piai.

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Section: Analysis Of the Image Enhancement Methodsmentioning

confidence: 99%

Integrated NIR-HE based SPOT-5 image enhancement method for features preservation and edge detection

Zulkifle

Hassan

Ahmad

et al. 2021

IJEECS

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“…Clustering is a simple and yet efficient unsupervised approache that assigns the data subjects into high similar groups, i.e., clusters. However, handling the underlying diversity of clustering analysis, objectives, terms, and assumptions of various clustering algorithms can be daunting [4], [5]. Therefore, there is a demand to neatly determine a correct congruence between the aggregation algorithms and the biomedical applications.…”

Section: Introductionmentioning

confidence: 99%

Automatic BIRCH thresholding with features transformation for hierarchical breast cancer clustering

Alzu’bi

Barham²

2022

IJECE

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<p>Breast cancer is one of the most common diseases diagnosed in women over the world. The balanced iterative reducing and clustering using hierarchies (BIRCH) has been widely used in many applications. However, clustering the patient records and selecting an optimal threshold for the hierarchical clusters still a challenging task. In addition, the existing BIRCH is sensitive to the order of data records and influenced by many numerical and functional parameters. Therefore, this paper proposes a unique BIRCH-based algorithm for breast cancer clustering. We aim at transforming the medical records using the breast screening features into sub-clusters to group the subject cases into malignant or benign clusters. The basic BIRCH clustering is firstly fed by a set of normalized features then we automate the threshold initialization to enhance the tree-based sub-clustering procedure. Additionally, we present a thorough analysis on the performance impact of tuning BIRCH with various relevant linkage functions and similarity measures. Two datasets of the standard breast cancer wisconsin (BCW) benchmarking collection are used to evaluate our algorithm. The experimental results show a clustering accuracy of 97.7% in 0.0004 seconds only, thereby confirming the efficiency of the proposed method in clustering the patient records and making timely decisions.</p>

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“…Thus, this work focuses on detecting the primary attack. Recently, several methods have been presented for detecting primary tampering attacks [8]- [10]. However, these methods have focused on detecting whether an image is tampered or not and failed to localize tampered region within a tampered image [11], [12].…”

Section: Introductionmentioning

confidence: 99%

Efficient resampling features and convolution neural network model for image forgery detection

Manjunatha.¹,

Patil

2022

IJEECS

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The extended utilization of picture-enhancing or manipulating tools has led to ease of manipulating multimedia data which includes digital images. These manipulations will disturb the truthfulness and lawfulness of images, resulting in misapprehension, and might disturb social security. The image forensic approach has been employed for detecting whether or not an image has been manipulated with the usage of positive attacks which includes splicing, and copy-move. This paper provides a competent tampering detection technique using resampling features and convolution neural network (CNN). In this model range spatial filtering (RSF)-CNN, throughout preprocessing the image is divided into consistent patches. Then, within every patch, the resampling features are extracted by utilizing affine transformation and the Laplacian operator. Then, the extracted features are accumulated for creating descriptors by using CNN. A wide-ranging analysis is performed for assessing tampering detection and tampered region segmentation accuracies of proposed RSF-CNN based tampering detection procedures considering various falsifications and post-processing attacks which include joint photographic expert group (JPEG) compression, scaling, rotations, noise additions, and more than one manipulation. From the achieved results, it can be visible the RSF-CNN primarily based tampering detection with adequately higher accurateness than existing tampering detection methodologies.

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Copy-move forgery detection using convolutional neural network and K-mean clustering

Cited by 11 publications

References 19 publications

Integrated NIR-HE based SPOT-5 image enhancement method for features preservation and edge detection

Integrated NIR-HE based SPOT-5 image enhancement method for features preservation and edge detection

Automatic BIRCH thresholding with features transformation for hierarchical breast cancer clustering

Efficient resampling features and convolution neural network model for image forgery detection

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