Structural Correlation Based Method for Image Forgery Classification and Localization

Pham, Nam; Lee, Jong Weon; Park, Chun-Su

doi:10.3390/app10134458

Cited by 9 publications

(8 citation statements)

References 44 publications

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“…This approach allows to eliminate the increase in time at the stage of recognition. It should be noted that for some tasks of structural image recognition the speed of data processing is not critical [5], [9]- [11], [14]- [18], [20], [27], [28], [32], [42], [43], [53]. The results of this research may be useful, for example, in robotics, remotely piloted aircraft, where the criterion of image processing speed is decisive.…”

Section: Results Of Approbation Of the Proposed Methods During Struct...mentioning

confidence: 91%

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Development of Effective Methods for Structural Image Recognition Using the Principles of Data Granulation and Apparatus of Fuzzy Logic

et al. 2021

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The processes of intelligent data processing in computer vision systems have been researched. The problem of structural image recognition is relevant. This is a promising way to assess the degree of similarity of objects. This approach provides the simplicity of construction and the high reliability of decision making. The main problem of an effective description of characteristic features is the distortion of fragments of analyzed objects. The reasons for changing the input data can be the actions of geometric transformations, the influence of background or interference. The elements of false objects with similar characteristics are formed. The problem of ensuring high-quality recognition requires the implementation of effective means of image processing. Methods of statistical modeling, granulation of data and fuzzy sets, detection and comparison of keypoints on the image, classification and clustering of data, and simulation modelling are used in this research. The implementation of the proposed approaches provides the formation of a concise description of features or a vector representation of unique keypoints. The verification of theoretical foundations and evaluation of the effectiveness of the proposed data processing methods for real image bases is performed using the OpenCV library. The applied significance of the work is substantiated according to the criterion of data processing time without reducing the characteristics of reliability and interference immunity. The developed methods allow to increase the structural recognition of images by several times. Perspectives of research may involve identifying the optimal number of keypoints of the base set.

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Section: Results Of Approbation Of the Proposed Methods During Struct...mentioning

confidence: 91%

“…The existing methods of computer vision are deterministic; the result of their work depends only on the input data [12]- [14].…”

Section: Introductionmentioning

confidence: 99%

Development of Effective Methods for Structural Image Recognition Using the Principles of Data Granulation and Apparatus of Fuzzy Logic

et al. 2021

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“…Among these digital data, images are one of the most important data categories that influence people's daily life. Based on its credibility, an image is regarded as one of the most common sources of evidence that plays a vital role in many fields [1]. In general, people or objects are unlikely to appear in the digital image if they do not exist in the scene of the photo.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Feature Extraction-Based Algorithm for Stitching Tampered/Untampered Image Classification

Jia

Nahli

et al. 2022

Applied Sciences

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With the recent internet connectivity revolution, and the fast-growing prevalence of camera-enabled devices, images play a vital role in several fields of modern life. Photos, which often have been seen as evidence in courts, are nowadays subject to more sophisticated tricky forgery. To detect the image stitching between originally unassociated people/scenes and other combining forgery, an algorithm used to extract multiple specific image features, such as grayscale, complementary color wavelet (CCW) based chroma, sharpness, and natural scene statistics (NSS), is first presented in this paper. It is illustrated that a random forest model can be trained by these extracted features and then be employed to classify the stitching tampered/untampered images. The experimental results show that the proposed algorithm favorably outperforms the techniques reported in the literature, and achieves a state-of-the-art performance with higher accuracy values of 91%, 95.24%, and 88.02% on the Tampering ImageNet, Columbia, and CASIA ITDE V2.0 datasets, respectively. The precision, recall, and F1-score were also improved to a certain extent.

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“…Artificial intelligence has been developing rapidly with many real-world applications such as time series prediction [24], image classification [40,46], and smart cities [28]. Among them, personal identification using biometric traits is a hot trend nowadays and has received increasing attention in the computer vision community.…”

Section: Introductionmentioning

confidence: 99%

Weighted statistical binary patterns for facial feature representation

2021

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We present a novel framework for efficient and robust facial feature representation based upon Local Binary Pattern (LBP), called Weighted Statistical Binary Pattern, wherein the descriptors utilize the straight-line topology along with different directions. The input image is initially divided into mean and variance moments. A new variance moment, which contains distinctive facial features, is prepared by extracting root k-th. Then, when Sign and Magnitude components along four different directions using the mean moment are constructed, a weighting approach according to the new variance is applied to each component. Finally, the weighted histograms of Sign and Magnitude components are concatenated to build a novel histogram of Complementary LBP along with different directions. A comprehensive evaluation using six public face datasets suggests that the present framework outperforms the state-of-the-art methods and achieves 98.51% for ORL, 98.72% for YALE, 98.83% for Caltech, 99.52% for AR, 94.78% for FERET, and 99.07% for KDEF in terms of accuracy, respectively. The influence of color spaces and the issue of degraded images are also analyzed with our descriptors. Such a result with theoretical underpinning confirms that our descriptors are robust against noise, illumination variation, diverse facial expressions, and head poses.

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Structural Correlation Based Method for Image Forgery Classification and Localization

Cited by 9 publications

References 44 publications

Development of Effective Methods for Structural Image Recognition Using the Principles of Data Granulation and Apparatus of Fuzzy Logic

Development of Effective Methods for Structural Image Recognition Using the Principles of Data Granulation and Apparatus of Fuzzy Logic

A Multi-Feature Extraction-Based Algorithm for Stitching Tampered/Untampered Image Classification

Weighted statistical binary patterns for facial feature representation

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