Digital image splicing detection based on Markov features in DCT and DWT domain

He, Zhongwei; Lu, Wei; Sun, Wei; Huang, Jiwu

doi:10.1016/j.patcog.2012.05.014

Cited by 286 publications

(163 citation statements)

References 23 publications

Supporting

Mentioning

159

Contrasting

Unclassified

Order By: Relevance

“…Table 3 shows that the accuracy rates exhibited different trends. The best result was obtained using the expanded DCT Markov + DWT Markov [29], which reduced to 100-D by applying Support Vector Machine Recursive Feature Extraction (SVM-RFE) (93.55%). The next highest accuracy rate was achieved using the proposed method (91.88%) with 40-D.…”

Section: Comparison With Other Methodsmentioning

confidence: 99%

Fractional Differential Texture Descriptors Based on the Machado Entropy for Image Splicing Detection

Ibrahim

Moghaddasi

Jalab

et al. 2015

Entropy

View full text Add to dashboard Cite

Image splicing is a common operation in image forgery. Different techniques of image splicing detection have been utilized to regain people's trust. This study introduces a texture enhancement technique involving the use of fractional differential masks based on the Machado entropy. The masks slide over the tampered image, and each pixel of the tampered image is convolved with the fractional mask weight window on eight directions. Consequently, the fractional differential texture descriptors are extracted using the gray-level co-occurrence matrix for image splicing detection. The support vector machine is used as a classifier that distinguishes between authentic and spliced images. Results prove that the achieved improvements of the proposed algorithm are compatible with other splicing detection methods.

show abstract

Section: Comparison With Other Methodsmentioning

confidence: 99%

Fractional Differential Texture Descriptors Based on the Machado Entropy for Image Splicing Detection

Ibrahim

Moghaddasi

Jalab

et al. 2015

Entropy

View full text Add to dashboard Cite

show abstract

“…Image splicing adds a part of an image into another image in order to hide or change the content of the second image [21].…”

Section: )mentioning

confidence: 99%

Detection of Scaled Region Duplication Image Forgery using Color based Segmentation with LSB Signature

Diaa¹,

Mohammed²

2017

ijacsa

View full text Add to dashboard Cite

Abstract-Due to the availability of powerful image editing softwares, forgers can tamper the image content easily. There are various types of image forgery, such as image splicing and region duplication forgery. Region duplication is one of the most common manipulations used for tampering digital images. It is vital in image forensics to authenticate the digital image. In this paper, a novel region duplication forgery detection approach is proposed. By segmenting the input image based on the colour features, sufficient number of centroids are produced, that exist even in small or smooth regions. Then, the Least Significant Bit (LSB) of all the colours of pixels in each segment are extracted to build the signature vector. Finally, the hamming distance is calculated through exploiting the signature vector of image to find the dissimilarity. Various experimental results are provided to demonstrate the superior performance of the proposed scheme under some post processing operations such as scaling attack.

show abstract

“…Most of these techniques are devised for detecting one specific type of image forgery. For example techniques that can detect image forgery where objects are morphed in the image from the same image cannot detect the forgery where object morphing source is different from current image source [2].A lot of work has been previously done in forgery detection, particularly copy-move forgery detection. Popescu et al [3] devised a method for forgery detection by dividing the image into several blocks, applying the PCA transform (for dimension reduction) and detecting the forgery by detecting similarity between the blocks.…”

Section: Related Workmentioning

confidence: 99%

Image Forgery Authentication and Classification using Hybridization of HMM and SVM Classifier

Hashmi¹,

Keskar²

2015

IJSIA

View full text Add to dashboard Cite

show abstract

Digital image splicing detection based on Markov features in DCT and DWT domain

Cited by 286 publications

References 23 publications

Fractional Differential Texture Descriptors Based on the Machado Entropy for Image Splicing Detection

Fractional Differential Texture Descriptors Based on the Machado Entropy for Image Splicing Detection

Detection of Scaled Region Duplication Image Forgery using Color based Segmentation with LSB Signature

Image Forgery Authentication and Classification using Hybridization of HMM and SVM Classifier

Contact Info

Product

Resources

About