Image splicing detection based on Markov features in QDCT domain

Li, Ce; Ma, Qiang; Xiao, Lei; Li, Ming; Zhang, Aihua

doi:10.1016/j.neucom.2016.04.068

Cited by 84 publications

(27 citation statements)

References 16 publications

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“…In the existing localization framework, there are four steps. Firstly, many schemes adopt preprocessing operation to extract the feature effectively, such as color space conversion, discrete wavelet transform (DWT) [He, Wei, Wei et al (2012)], discrete cosine transform (DCT) [Alahmadi, Hussain, Aboalsamh et al (2013); Li, Qiang, Xiao et al (2017)], principle component analysis (PCA) [Pyatykh, Hesser and Zheng (2013)]. Secondly, image is often divided into image blocks with small size with various segment in order to achieve high accuracy of localization.…”

Section: Framework Of Splicing Image Localizationmentioning

confidence: 99%

Splicing Image and Its Localization: A Survey

Wang¹,

Li²

2019

Journal of Information Hiding and Privacy Protection

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With the rapid development of information technology, digital images have become an important medium for information transmission. However, manipulating images is becoming a common task with the powerful image editing tools and software, and people can tamper the images content without leaving any visible traces of splicing in order to gain personal goal. Images are easily spliced and distributed, and the situation will be a great threat to social security. The survey covers splicing image and its localization. The present status of splicing image localization approaches is discussed along with a recommendation for future research.

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Section: Framework Of Splicing Image Localizationmentioning

confidence: 99%

Splicing Image and Its Localization: A Survey

Wang¹,

Li²

2019

Journal of Information Hiding and Privacy Protection

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“…Unlike earlier work, e.g. [8], we use LIBSVM classifier, and the K ‐fold cross‐validation is applied to improve the classification performance of LIBSVM.…”

Section: Proposed Approachmentioning

confidence: 99%

“…Compared with the quaternion used in Li et al [8], octonion matrix of the image has a real part and seven imaginary parts and can simultaneously represent the colour information of six image channels. In this paper, the RGB model and the HSI model are selected.…”

Section: Proposed Approachmentioning

confidence: 99%

“…There are many classification methods of binary classification problem, such as random forest [31], support vector machine (SVM) methods, deep learning and so on. While simultaneously maximising the geometric margin between two different classes, SVM can minimise the empirical classification error [8]. Compared with other classifiers, SVM can get a very good performance by simply adjusting the parameters, and the LIBSVM is a library for SVM.…”

Section: Proposed Approachmentioning

confidence: 99%

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Image splicing detection based on Markov features in discrete octonion cosine transform domain

et al. 2018

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To improve the poor robustness and low accuracy of the existing algorithms of image splicing detection, a novel passive image forgery detection method is proposed in this study, which is based on DOCT (discrete octonion cosine transform) and Markov. By introducing the octonion and DOCT, the colour information of six image channels (the RGB model and the HSI model) can be exhaustively extracted, which enhances the robustness of the algorithm. On the issue of improving the detection accuracy, the standard deviation is used to characterise the relationship of the colour information between the parts of DOCT coefficient matrix, and the K‐fold cross‐validation is introduced to improve the identification performance of the classifier. The steps of the algorithm are as follows: Firstly, the 8 × 8 block DOCT transform is used to the original image to obtain parts of block DOCT coefficient. Secondly, the standard deviation is used to process the corresponding parts of all blocks of the image. Finally, the Markov feature vector of the DOCT coefficient is extracted and feds to the LIBSVM (a library for support vector machines). When using LIBSVM for classification, K‐fold cross‐validation is executed to select the best parameter pairs. The experiment results demonstrate that the algorithm is superior to the other state‐of‐the‐art splicing detection methods.

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“…Video can be anticipated as an arrangement of images named frames, appeared over a period of time. As such, the altering recognition techniques delivered for image forensics [4][5][6][7][8] could be connected at frame level. The characteristics trajectories encrypt image belongings, to be specific color, quality, and form.…”

Section: Introductionmentioning

confidence: 99%

Feature Extraction Techniques in Associate with Opposition Based Whale Optimization Algorithm

HimaBindu¹,

Anuradha²,

Murty³

2019

ISI

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Near-duplicate video (NDV) detection is an important issue of copyright protection. However, the traditional detection methods are very imprecise and complex. To solve the problem, this paper introduces the opposition-based solution generation strategy into the conventional whale optimization algorithm (OWOA), creating a novel NDV detection method called the OWOA. The author detailed how to use the hybrid method to extract different types of features, ranging from color, shape to texture, and compared the OWOA with traditional feature extraction methods through experiments. The comparison shows that the OWOA achieved the optimal performance in DNV detection. The research findings can greatly assist regulatory authorities in monitoring and detecting edited contents.

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Image splicing detection based on Markov features in QDCT domain

Cited by 84 publications

References 16 publications

Splicing Image and Its Localization: A Survey

Splicing Image and Its Localization: A Survey

Image splicing detection based on Markov features in discrete octonion cosine transform domain

Feature Extraction Techniques in Associate with Opposition Based Whale Optimization Algorithm

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