Copy-move forgery detection using multiresolution local binary patterns

Davarzani, Reza; Yaghmaie, Khashayar; Mozaffari, Saeed; Tapak, Meysam

doi:10.1016/j.forsciint.2013.04.023

Cited by 112 publications

(46 citation statements)

References 17 publications

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“…Then, it has gained increasing attention in many image analyses applications due to its low computational complexity, invariance to monotonic gray-scale changes and texture description ability [20]. LBP has been utilized in various image analyses applications such as dynamic texture recognition [22], face recognition [23], detecting moving objects [24], image forgery [25], image region descriptors [26] and so on. In the original LBP, signed gray level differences of each pixel with its neighboring pixels are described as a binary form.…”

Section: Center-symmetric Local Binary Patterns (Cslbp)mentioning

confidence: 99%

Image authentication using LBP-based perceptual image hashing

Davarzani

Mozaffari

Yaghmaie

2015

JAIDM

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Feature extraction is a main step in all perceptual image hashing schemes in which robust features will lead to better results in perceptual robustness. Simplicity, discriminative power, computational efficiency and robustness to illumination changes are counted as distinguished properties of Local Binary Pattern features. In this paper, we investigate the use of local binary patterns for perceptual image hashing. In feature extraction, we propose to use both sign and magnitude information of local differences. So, the algorithm utilizes a combination of gradient-based and LBP-based descriptors for feature extraction. To provide security needs, two secret keys are incorporated in feature extraction and hash generation steps. Performance of the proposed hashing method is evaluated with an important application in perceptual image hashing scheme: image authentication. Experiments are conducted to show that the present method has acceptable robustness against perceptual content-preserving manipulations. Moreover, the proposed method has this capability to localize the tampering area, which is not possible in all hashing schemes.

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Section: Center-symmetric Local Binary Patterns (Cslbp)mentioning

confidence: 99%

Image authentication using LBP-based perceptual image hashing

Davarzani

Mozaffari

Yaghmaie

2015

JAIDM

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“…SIFT and SURF are the most widely used keypoints for CMFD. Some other local features are also proposed to detect duplicate regions, like Local Binary Pattern (LBP) [19], Binary Robust Invariant Scalable Keypoints (BRISK) [20], and DAISY [21]. Though the computing complexity of these algorithms in matching process is much lower, they also have a main drawback: the performance will be very poor when the copy-move regions are smooth.…”

Section: Introductionmentioning

confidence: 99%

Nonoverlapping Blocks Based Copy-Move Forgery Detection

Sun

Zhao

2018

Security and Communication Networks

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In order to solve the problem of high computational complexity in block-based methods for copy-move forgery detection, we divide image into texture part and smooth part to deal with them separately. Keypoints are extracted and matched in texture regions. Instead of using all the overlapping blocks, we use nonoverlapping blocks as candidates in smooth regions. Clustering blocks with similar color into a group can be regarded as a preprocessing operation. To avoid mismatching due to misalignment, we update candidate blocks by registration before projecting them into hash space. In this way, we can reduce computational complexity and improve the accuracy of matching at the same time. Experimental results show that the proposed method achieves better performance via comparing with the state-of-the-art copy-move forgery detection algorithms and exhibits robustness against JPEG compression, rotation, and scaling.

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“…If we can uncover evidence indicating image alterations, we can conclude that an image has been forged. There have been several studies on detecting various image forgery techniques, such as copy-move [3][4][5], image splicing [6][7][8], scaling [9,10], rotation [11], blurring [12,13], contrast change [14], and color modification [15].…”

Section: Introductionmentioning

confidence: 99%

Wavelet-based color modification detection based on variance ratio

Jeon

Eom

2018

J Image Video Proc.

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Color modification is one of the popular image forgery techniques. It can be used to eliminate criminal evidence in various ways, such as modifying the color of a car used in a crime. If the color of a digital image is modified, the locations of the interpolated and original samples may be changed. Because the original and interpolated pixels have different statistical characteristics, these differences can serve as a basic clue for estimating the degree of color modification. It is assumed that the variance of original samples is greater than that of the interpolated samples. Therefore, we present a novel algorithm for color modification estimation using the variance ratio of color difference images in the wavelet domain. The color difference model is used to emphasize the differences between the original and interpolated samples. For color difference images, we execute a wavelet transform and use the highest frequency subband to calculate variances. We define a variance ratio measurement to quantify the level of color modification. Additionally, changed color local regions can be efficiently detected using the proposed algorithm. Experimental results demonstrate that the proposed method generates accurate estimation results for detecting color modification. Compared to the conventional method, our method provides superior color modification detection performance.

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Copy-move forgery detection using multiresolution local binary patterns

Cited by 112 publications

References 17 publications

Image authentication using LBP-based perceptual image hashing

Image authentication using LBP-based perceptual image hashing

Nonoverlapping Blocks Based Copy-Move Forgery Detection

Wavelet-based color modification detection based on variance ratio

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