Image Splicing Detection using Uniform Local Binary Pattern and Wavelet Transform

Image splicing detection is of fundamental importance in digital forensics and therefore has attracted increasing attention recently. In this article, a color image splicing detection approach is proposed based on Markov transition probability of quaternion component separation in quaternion discrete cosine transform (QDCT) domain and quaternion wavelet transform (QWT) domain. First, Markov features of the intra-block and inter-block between block QDCT coefficients are obtained from the real parts and three imaginary parts of QDCT coefficients, respectively. Then, additional Markov features are extracted from the luminance (Y) channel in the quaternion wavelet transform domain to characterize the dependency of position among quaternion wavelet sub-band coefficients. Finally, an ensemble classifier (EC) is exploited to classify the spliced and authentic color images. The experiment results demonstrate that the proposed approach can outperform some state-of-the-art methods.

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

Symmetry ◽

10.3390/sym11111392 ◽

2019 ◽

Vol 11 (11) ◽

pp. 1392

Author(s):

Subramaniam ◽

Jalab ◽

Ibrahim ◽

Mohd Noor

Keyword(s):

Wavelet Transform ◽

Fractional Calculus ◽

Discrete Wavelet Transform ◽

Discrete Wavelet ◽

Forgery Detection ◽

Image Splicing ◽

Information Carrier ◽

Image Splicing Detection ◽

Splicing Detection ◽

Redundant Discrete Wavelet Transform

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.

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Digital Image Splicing Detection Based on Markov Features in QDCT and QWT Domain

International Journal of Digital Crime and Forensics ◽

10.4018/ijdcf.2018100107 ◽

2018 ◽

Vol 10 (4) ◽

pp. 90-107 ◽

Cited By ~ 6

Author(s):

Ruxin Wang ◽

Wei Lu ◽

Jixian Li ◽

Shijun Xiang ◽

Xianfeng Zhao ◽

...

Keyword(s):

Wavelet Transform ◽

Color Image ◽

Transition Probability ◽

Ensemble Classifier ◽

Image Splicing ◽

Image Splicing Detection ◽

Detection Approach ◽

Quaternion Wavelet Transform ◽

Splicing Detection ◽

Markov Features

Image splicing detection is of fundamental importance in digital forensics and therefore has attracted increasing attention recently. In this article, a color image splicing detection approach is proposed based on Markov transition probability of quaternion component separation in quaternion discrete cosine transform (QDCT) domain and quaternion wavelet transform (QWT) domain. First, Markov features of the intra-block and inter-block between block QDCT coefficients are obtained from the real parts and three imaginary parts of QDCT coefficients, respectively. Then, additional Markov features are extracted from the luminance (Y) channel in the quaternion wavelet transform domain to characterize the dependency of position among quaternion wavelet sub-band coefficients. Finally, an ensemble classifier (EC) is exploited to classify the spliced and authentic color images. The experiment results demonstrate that the proposed approach can outperform some state-of-the-art methods.

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Image Splicing Forgery Detection Scheme Using New Local Binary Pattern Varient

Academic Journal of Nawroz University ◽

10.25007/ajnu.v9n3a780 ◽

2020 ◽

Vol 9 (3) ◽

pp. 208

Author(s):

Araz R. Abrahim ◽

Mohd Sh. Mohd Rahim ◽

Ahmed S. Sami

Keyword(s):

Local Binary Pattern ◽

Detection Method ◽

Forgery Detection ◽

Image Splicing ◽

Detection Scheme ◽

Local Ternary Pattern ◽

Image Splicing Detection ◽

Color Features ◽

Splicing Detection ◽

Robust To Noise

In this research develop passive image splicing detection method based on a new descriptor called Adaptive Threshold Mean Ternary Pattern (ATMTP). It was developed based on strength and weaknesses of both Local Binary Pattern (LBP) and Local Ternary Pattern (LTP). ATMTP extraction feature is normally achieved by using proposed mean based thresholding and adaptive ternary thresholding, the former is robust to noise while the latter is robust to noise and other photometric attacks. It is designed to withstand against photometric manipulations, be it single or double attacks. In this research the ATMTP color features extracted from R, G, and B channels have revealed that the present method achieved higher accuracy on standard datasets CASIA V2.0 out of 99.03%, Sensitivity 99.6%, and specificity 98.1%. Finally, in terms of accuracy, the proposed SFD scheme outperformed the best recent works in this area.

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