State of the art in passive digital image forgery detection: copy-move image forgery

Sadeghi, Somayeh; Dadkhah, Sajjad; Jalab, Hamid A.; Mazzola, Giuseppe; Uliyan, Diaa M.

doi:10.1007/s10044-017-0678-8

Cited by 53 publications

(22 citation statements)

References 71 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…geometric transformation and post-processing attacks. These schemes are introduced in details [37] as follows: I. Block-Based Methods divide the image into square or circle blocks to extract features from these blocks as shown in Figure 2. The main advantage of this approach is that give high detection accuracy for the textured forged regions.…”

Section: Related Workmentioning

confidence: 99%

“…These features are distinctive to represent corners, edges or blobs in the image. Then, a robust texture descriptor is built to increase a reliability against geometric transformation attacks [37]. [35].…”

Section: Related Workmentioning

confidence: 99%

“…The limitations are the high computational complexity and struggle to detect duplicated regions with scale and rotation attacks. The frequency features are: Discrete cosine transform (DCT) [47], Fourier Transform (FT) [37], Discrete wavelet transform (DWT) [27], Curvelet Transform (CT) [1] and Wiener Filter. The limitation of this approach is that features are sensitive to blur attack.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Blur Invariant Features For Exposing Region Duplication Forgery Using ANMS And Local Phase Quantization

Uliyan¹,

Abushariah²,

Altamimi³

2018

IJCAI

Self Cite

View full text Add to dashboard Cite

In digital image forensics, local interest points can be employed to faithfully detect region duplication forgery. Authentic images may be abused by copy-move forgery to fully contained duplicated regions such as objects. Recent existing local interest point forgery detection methods fail to detect this type of forgery in the retouched regions by some geometric transformations. To solve this challenge, local interest points should be detected which cover all the regions with high primitives like corners and edges. These primitives represent the internal structure of any object in the image which makes them have a discriminating property under geometric transformations such as scale and rotation operation. They can be exposed based on Scale-Invariant Features Transform (SIFT) algorithm. Here, we provide an image forgery detection technique by using local interest points. First, the image is segmented based on fuzzy C means to divide the image into homogenous regions that have the same texture. Second, local interest points are exposed by extracting Adaptive non-maximal suppression (ANMS) from dividing blocks in the segmented image to detect such corners of objects. We also demonstrate that ANMS Keypoints can be effectively utilized to detect blurred and scaled forged regions. The ANMS features of the image are shown to exhibit the internal structure of copy moved region. We provide a new texture descriptor called local phase Quantization (LPQ) that is robust to image blurring and also to eliminate the false positives of duplicated regions. Experimental results show that our scheme has the ability to reveal region duplication forgeries under scaling, rotation and blur manipulation of JPEG images on MICC-F220 and CASIA v. 2 Image Datasets. Povzetek: Predstavljena je izvirna metoda za odkrivanje ponarejenih področij v sliki.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Blur Invariant Features For Exposing Region Duplication Forgery Using ANMS And Local Phase Quantization

Uliyan¹,

Abushariah²,

Altamimi³

2018

IJCAI

Self Cite

View full text Add to dashboard Cite

show abstract

“…Image forgery detection refers to the process of identifying inconsistent regions in an image to authenticate the input digital image [ 1 , 2 ]. The detection of image forgery is divided into two types [ 3 , 4 ], namely active and passive authentication.…”

Section: Introductionmentioning

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

“…Copy-move refers to a forgery where a region(s) from an image is cloned in the same original image. The intention of copy-move is to duplicate, conceals or emphasizes the pasted region on the tampering image [2,3]. Discovering copy-move tampering is more challenging because there are no significant visible alterations in the forged image texture.…”

Section: Introductionmentioning

confidence: 99%

Improved Image Splicing Forgery Detection by Combination of Conformable Focus Measures and Focus Measure Operators Applied on Obtained Redundant Discrete Wavelet Transform Coefficients

et al. 2019

View full text Add to dashboard Cite

The image is the best information carrier in the current digital era and the easiest to manipulate. Image manipulation causes the integrity of this information carrier to be ambiguous. The image splicing technique is commonly used to manipulate images by fusing different regions in one image. Over the last decade, it has been confirmed that various structures in science and engineering can be demonstrated more precisely by fractional calculus using integrals or derivative operators. Many fractional-order-based techniques have been used in the image-processing field. Recently, a new specific fractional calculus, called conformable calculus, was delivered. Herein, we employ the combination of conformable focus measures (CFMs), and focus measure operators (FMOs) in obtaining redundant discrete wavelet transform (RDWT) coefficients for improving the image splicing forgery detection. The process of image splicing disorders the content of tampered image and causes abnormality in the image features. The spliced region’s boundaries are usually blurring to avoid detection. To make use of the blurred information, both CFMs and FMOs are used to calculate the degree of blurring of the tampered region’s boundaries for image splicing detection. The two public image datasets IFS-TC and CASIA TIDE V2 are used for evaluation of the proposed method. The obtained results of the proposed method achieved accuracy rate 98.30% for Cb channel on IFS-TC image dataset and 98.60% of the Cb channel on CASIA TIDE V2 with 24-D feature vector. The proposed method exhibited superior results compared with other image splicing detection methods.

show abstract

State of the art in passive digital image forgery detection: copy-move image forgery

Cited by 53 publications

References 71 publications

Blur Invariant Features For Exposing Region Duplication Forgery Using ANMS And Local Phase Quantization

Blur Invariant Features For Exposing Region Duplication Forgery Using ANMS And Local Phase Quantization

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

Improved Image Splicing Forgery Detection by Combination of Conformable Focus Measures and Focus Measure Operators Applied on Obtained Redundant Discrete Wavelet Transform Coefficients

Contact Info

Product

Resources

About