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

Wang, Ruxin; Lu, Wei; Li, Jixian; Xiang, Shijun; Zhao, Xianfeng; Wang, Jinwei

doi:10.4018/ijdcf.2018100107

Cited by 13 publications

(4 citation statements)

References 29 publications

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“…According to the fundamental principles and steps [8, 29, 30] of QDCT, combining with the nature of DOCT, the specific algorithm of DOCT is presented in this study. The DOCT formula is shown in

o false(u, v false) = α false(u false) α false(v false) \sum_{x = 0}^{M - 1} {false\sum}_{y = 0}^{N - 1} u_{normalo} \times {bold-italich}_{normalo} false(x, y false) \times H false(u, v, x, y false)

where h o ( x , y ) is a two‐dimensional M × N octonion matrix.…”

Section: Proposed Approachmentioning

confidence: 99%

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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“…According to the fundamental principles and steps [8, 29, 30] of QDCT, combining with the nature of DOCT, the specific algorithm of DOCT is presented in this study. The DOCT formula is shown in

o false(u, v false) = α false(u false) α false(v false) \sum_{x = 0}^{M - 1} {false\sum}_{y = 0}^{N - 1} u_{normalo} \times {bold-italich}_{normalo} false(x, y false) \times H false(u, v, x, y false)

where h o ( x , y ) is a two‐dimensional M × N octonion matrix.…”

Section: Proposed Approachmentioning

confidence: 99%

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

et al. 2018

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“…The existing methods can be summarized in the below groups. JPEG compression artifacts, 12,13 edge inconsistencies, 14 color consistency, 15 visual similarity, 9,16 EXIF inconsistency, 17 camera model 18,19 and noise-pattern. [20][21][22] Specifically, most current methods focus on mining contextual features in all kinds of ways between images, ignoring the potential context relation information within images.…”

Section: Introductionmentioning

confidence: 99%

Center region aware cross former for image forgery localization

Wu,

Gong,

Zhang

et al. 2024

Fifteenth International Conference on Signal Processing Systems (ICSPS 2023)

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Image forgery has been a serious issue in real life during this boosting big data era. There have been many methods depending on detecting footprints such as edge inconsistency, camera noise, JPEG artifacts, etc., proposed. Unlike the existing methods, we propose a more general method, CRAC-formerNet to capture more stable manipulation traces, not only including RGB domain but also from high-frequency features generated from Steganalysis Rich Model (SRM) 1 and District Cosine transformer in the frequency domain. A shared query is generated from features from both the RGB and frequency domains. The keys and values are generated from each domain respectively. Moreover, features of manipulated and manipulated regions should belong to different distributions, especially features in edge regions. We use a center contrastive loss to learn fine-grained features. A manipulated region proposal module is proposed for improving computing efficiency when calculating the loss. We empirically demonstrate that our method achieves competitive results on four benchmark image manipulation datasets.

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“…Thereby an issue is exacerbated on account of the video because of its impressively huge volume (contrasted with content and images), which make it an extraordinary test for each web-based video platform as well as for systems that analyze and list a lot of web video content [5]. A video format can be expected as a sequence of images namely frames, considered over a period of time, whereby the tampering detection methods produced for image forensics [6][7][8][9][10] could be connected at the frame level. The feature vectors encode the properties of the image, so as to be specific color, texture, and shape.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Combined Shape, Color and Textural Features for Video Duplication

HimaBindu¹,

Anuradha²,

Murty³

2019

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The aim of the work is to identify the duplication in a video database with the aid of feature extraction techniques. The process includes extraction of image features (shape, color, and texture) for duplicate identification. The color contains 256 features, shape contains 200 features, the texture contains two different features namely gray-level co-occurrence matrix (GLCM) (22 features in 4 degrees) and grey-level run length matrix (GLRLM) (11 features) are extracted. In this paper, the preliminary work is to convert video into frames and then each frame into blocks subsequently including feature extraction. A query video is then considered for the same process of feature extraction and compared with the normal video. The distance between query video and normal video if found to be similar then the video identified as duplicate video. The results are performed for various evaluation matrix and plotted graphs are shown. The sensitivity value for whole feature extractions is 0.88, the specificity value for whole feature extractions is 0.83 and the accuracy value for whole extractions is 0.86. The entire process implemented in the working platform of MATLAB.

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

Cited by 13 publications

References 29 publications

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

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

Center region aware cross former for image forgery localization

Assessment of Combined Shape, Color and Textural Features for Video Duplication

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