Binary image steganalysis based on pixel mesh Markov transition matrix

Feng, Bingwen; Lu, Wei; Sun, Wei

doi:10.1016/j.jvcir.2014.10.003

Cited by 32 publications

(12 citation statements)

References 35 publications

(99 reference statements)

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“…A Markov chain is modeled with to construct the features set. To detect a wide spectrum of embedding algorithms, in SRM [19] , a lot of linear and nonlinear high-pass filters are empolyed to obtain image noise residuals and construct a rich model to train the EC classifier [32] . These schemes reflect the effectiveness and the importance of residual extraction in steganalysis for gray and color images.…”

Section: Residual Modelmentioning

confidence: 99%

“…LargeLTP [34] enlarges LTP to and employs Manhattan distance to measure the correlation between neighbor pixels and the center pixel. 8192-d features set is conducted to train ensemble classifier [32] . In LP [41] , binary images are analyzed by the distribution of some special patterns with L-shape.…”

Section: Comparison With Other Steganalysis Schemesmentioning

confidence: 99%

“…[19], a spatial rich model (SRM) is proposed which employs many linear and nonlinear high-pass filters to model noise components. Ensemble classifier (EC) [32] is used as the final steganalyzer to work with high-dimensional feature spaces. Many steganalytic schemes are proposed in recent years for binary images [33−35] .…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Binary Image Steganalysis Based on Symmetrical Local Residual Patterns

Liu

Xiao

et al. 2022

Chinese J of Electronics

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Section: Residual Modelmentioning

confidence: 99%

Section: Comparison With Other Steganalysis Schemesmentioning

confidence: 99%

See 1 more Smart Citation

Binary Image Steganalysis Based on Symmetrical Local Residual Patterns

Liu

Xiao

et al. 2022

Chinese J of Electronics

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“…Steganalysis merupakan metode yang digunakan untuk mempelajari karakteristik penyembunyian suatu data pada media (steganography) dan bagaimana cara untuk mendeteksi bahkan sampai membongkar data tersembunyi tersebut. Metode steganalysis berdasarkan pada Pixel Mesh Markov Transition Matrix (PMMTM) [9] yang merupakan metode pengembangan baru yang digunakan untuk mendeteksi biner pada gambar dalam ruang domain steganography. Ada juga yang menggunakan statistik citra untuk mendeteksi dua kasus ketika gambar tersembunyi disimpan sebagai salah satu potongan besar (Simple Mode) atau tersebar (Shuffle Mode) [10].…”

Section: B Steganalysis Dan Cryptanalysisunclassified

Penerapan Modifikasi Vigenere Cipher Pada Least Significant Bit Steganography Menggunakan Pendekatan Midpoint Cyrcle

Gultom¹,

Supria²,

Nasir³

et al. 2020

Elektrika Borneo

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Dalam perkembangan teknologi informasi yang semakin berkembang seiring dengan berjalannya waktu. Dengan teknologi informasi yang ada telah banyak digunakan orang untuk saling bertukar informasi, baik informasi yang bersifat rahasia dan informasi yang tidak bersifat rahasia. Informasi tersebut memerlukan keamanan yang kuat, khususnya keamanan dalam pengiriman informasi, karena banyak para hacker yang selalu ingin mengetahui rahasia pada informasi yang dikirim. Vigenere cipher salah satu dari metode cryptography yang merupakan metode enkripsi pesan dari plain text menjadi cipher text dengan menggunakan key yang bertujuan agar pesan tidak bisa dibaca oleh pihak yang tidak berwenang. Vigenere cipher memiliki kelemahan terhadap cryptanalysis, salah satunya adalah metode kasiski. Least Significant Bit (LSB) adalah sebuah metode yang digunakan untuk menyisipkan pesan pada bit rendah atau bit yang paling kanan, namun metode rentan terhadap steganalysis. Pada makalah ini, peneliti menggunakan proses modifikasi pada criptography untuk mengenkripsi pesan dengan menggunakan kunci (key) kemudian pesan yang dienkripsi disisipkan pada file multimedia dengan metode least significant bit steganography. Sehingga metode yang diusulkan ini akan lebih meningkatkan keamanan pesan, mengurangi kerentanan terhadap cryptanalysis dan steganalysis. Hasil dari penelitian ini menunjukkan bahwa pesan yang disembunyikan tidak akan bisa dideskripsi maupun dengan steganalysis, karena dengan modifikasi pada vigenere cipher yang menggunakan kunci pribadi hanya dapat dideskripsi dengan kunci pribadi yang sama.

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“…Multimedia forensics [38,61,78,80,81] is an important domain of information security [9,10,12,13,[19][20][21][22][23][24]39]. Both IoT and MBD [17,18,28,30,42,46,47,58,62,[65][66][67][68][69][70][71]75,77,79,83,85] have a lot of multimedia data.…”

Section: Introductionmentioning

confidence: 99%

Copy-move forgery detection using combined features and transitive matching

Lin

Huang

et al. 2018

Multimed Tools Appl

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Recently, the research of Internet of Things (IoT) and Multimedia Big Data (MBD) has been growing tremendously. Both IoT and MBD have a lot of multimedia data, which can be tampered easily. Therefore, the research of multimedia forensics is necessary. Copy-move is an important branch of multimedia forensics. In this paper, a novel copy-move forgery detection scheme using combined features and transitive matching is proposed. First, SIFT and LIOP are extracted as combined features from the input image. Second, transitive matching is used to improve the matching relationship. Third, a filtering approach using image segmentation is proposed to filter out false matches. Fourth, affine transformations are estimated between these image patches. Finally, duplicated regions are located based on those affine transformations. The experimental results demonstrate that the proposed scheme can achieve much better detection results on the public database under various attacks.

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Binary image steganalysis based on pixel mesh Markov transition matrix

Cited by 32 publications

References 35 publications

Binary Image Steganalysis Based on Symmetrical Local Residual Patterns

Binary Image Steganalysis Based on Symmetrical Local Residual Patterns

Penerapan Modifikasi Vigenere Cipher Pada Least Significant Bit Steganography Menggunakan Pendekatan Midpoint Cyrcle

Copy-move forgery detection using combined features and transitive matching

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