Splicing image forgery detection using textural features based on the grey level co‐occurrence matrices

Shen, Xuanjing; Shi, Zenan; Chen, Haipeng

doi:10.1049/iet-ipr.2016.0238

Cited by 64 publications

(18 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Shen et al (2016) [ 15 ] proposed a passive image forgery detection method on the basis of the textural features based gray level co-occurrence matrices (TF-GLCM), on the difference block of the DCT arrays. The TF-GLCM texture descriptor was applied on the difference block DCT arrays.…”

Section: Methodsmentioning

confidence: 99%

New Texture Descriptor Based on Modified Fractional Entropy for Digital Image Splicing Forgery Detection

Jalab

Subramaniam

Ibrahim

et al. 2019

Entropy

View full text Add to dashboard Cite

Forgery in digital images is immensely affected by the improvement of image manipulation tools. Image forgery can be classified as image splicing or copy-move on the basis of the image manipulation type. Image splicing involves creating a new tampered image by merging the components of one or more images. Moreover, image splicing disrupts the content and causes abnormality in the features of a tampered image. Most of the proposed algorithms are incapable of accurately classifying high-dimension feature vectors. Thus, the current study focuses on improving the accuracy of image splicing detection with low-dimension feature vectors. This study also proposes an approximated Machado fractional entropy (AMFE) of the discrete wavelet transform (DWT) to effectively capture splicing artifacts inside an image. AMFE is used as a new fractional texture descriptor, while DWT is applied to decompose the input image into a number of sub-images with different frequency bands. The standard image dataset CASIA v2 was used to evaluate the proposed approach. Superior detection accuracy and positive and false positive rates were achieved compared with other state-of-the-art approaches with a low-dimension of feature vectors.

show abstract

Section: Methodsmentioning

confidence: 99%

New Texture Descriptor Based on Modified Fractional Entropy for Digital Image Splicing Forgery Detection

Jalab

Subramaniam

Ibrahim

et al. 2019

Entropy

View full text Add to dashboard Cite

show abstract

“…11+1 ( ) = +1 ( +1 ( )) (11) The matrix form of system equations for a layer network is denoted as and 0 it is expressed as follows Eqs. (12) and 13,…”

Section: Classification Algorithmmentioning

confidence: 99%

“…X. Shen, Z. Shi, and H. Chen [12] introduced "Splicing Image FD with Textural Features on the Gary Level Co-occurrence Matrices (TF-GLCM). The TF-GLCM is calculated based on the Difference Block DCT (DB-DCT) arrays to capture the textural information and the spatial relationship between image pixels.…”

Section: Related Workmentioning

confidence: 99%

A Novel Method of Scale-Invariant Feature Transform Based Image Forgery Detection

Mahesh¹,

Maloji²,

Rao³

2018

IJIES

View full text Add to dashboard Cite

Now a days Image forgery (IF) makes many problems in a society. Super Pixels Segmentation and Scale-Invariant Feature Transform based Image Forgery Detection (SPS-SIFT-IFD) has introduced to detect the forgeries in the images. The forgery region extraction algorithm replaces the features point with small SP as feature blocks and neighboring blocks that have similar local color features has been identified by the Scale-Invariant Feature Transform (SIFT) technique. Artificial Neural Network (ANN) classifier is used for better detection of forged parts in the original images. It applies the morphological operation to the merged regions to generate the detected forgery regions. The experimental results indicate that the SPS-SIFT-IFD scheme achieve much better detection results even under various challenging conditions compared with the existing Forgery Detection (FD) methods. Finally, SPS-SIFT-IFD method performance is measure, in terms of Recall, Precision, False Measure, Sensitivity, Sensitivity, Accuracy and Gmean.

show abstract

“…It is a classic approach to extract texture features for various image processing applications. Some recent applications reported based on GLCM are image retrieval [25] and image splicing [26]. In proposed method, the co-occurrence of various combinations of differential excitation components in an image segment are considered instead of gray levels to extract second order textural features from gray images.…”

Section: Differential Excitation Texture Feature Extractionmentioning

confidence: 99%

Localization of Copy-Move Forgery in Digital Images through Differential Excitation Texture Features

Suresh¹,

Rao²

2019

IJIES

View full text Add to dashboard Cite

In this digital age, the extensive usage of digital devices and availability of open source image editing software leads to the easy manipulation of digital images. Copy-Move Forgery (CMF) is a guileless and widespread approach to hide or duplicate a certain portion of the image without leaving visual clues. Thus, it is difficult to detect the copy-move forgeries and there is a need for forensic experts to rely on an effective approach for CMF detection for forensic analysis. Hence, an efficient passive block based approach to detect and localize CMF is proposed. In the proposed method, texture features are extracted from Differential Excitation Component on the overlapping blocks of the image. Similarity measure is performed for block matching and mapping is done to identify the duplicated regions. Evaluation is performed qualitatively and quantitatively on CoMoFoD dataset; true detection rate of 0.99 and false detection rate of 0.08 has been achieved. Evaluation validates that proposed method out performs the other existing methods with regard to detection accuracy.

show abstract

Splicing image forgery detection using textural features based on the grey level co‐occurrence matrices

Cited by 64 publications

References 25 publications

New Texture Descriptor Based on Modified Fractional Entropy for Digital Image Splicing Forgery Detection

New Texture Descriptor Based on Modified Fractional Entropy for Digital Image Splicing Forgery Detection

A Novel Method of Scale-Invariant Feature Transform Based Image Forgery Detection

Localization of Copy-Move Forgery in Digital Images through Differential Excitation Texture Features

Contact Info

Product

Resources

About