Effective Recognition of Stereo Image Concealed Media of Interpolation Error with Difference Expansion

2016 ◽  
pp. 157-165
Author(s):  
Hemalatha J. ◽  
Kavitha Devi M. K.
Keyword(s):  
Support Vector Machine ◽  
Reversible Data Hiding ◽  
Stereo Image ◽  
Interpolation Error ◽  
Support Vector ◽  
Histogram Shifting ◽  
Difference Expansion ◽  
Statistical Feature ◽  
High Payload ◽  
Steganalysis Method

In this chapter, a new data conceal technique is anticipated for digital images. The method computes the interpolation error of the image by using histogram shifting method and difference expansion. With the expectation of embedding high payload and less distortion, the undisclosed data has embedded in the interpolating error. Additionally for hiding the data, reversible data hiding technique is used. The histogram deviation is used as evidence for resulting the data conceal in the stereo images. To our best knowledge, by extracting the statistical feature from the image subsample works as steganalysis scheme. To enhance the revealing rate precision the well known support vector machine acts as classifier. In addition to that the experimental results show that the proposed steganalysis method has enhanced the detection exactness of the stego images.

scholarly journals Steganalysis of Inactive Voice-Over-IP Frames Based on Poker Test

Symmetry ◽  
2018 ◽  
Vol 10 (8) ◽  
pp. 336
Author(s):  
Jie Liu ◽  
Hui Tian ◽  
Chin-Chen Chang ◽  
Tian Wang ◽  
Yonghong Chen ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Voice Over Ip ◽  
Experimental Results ◽  
Support Vector ◽  
Voice Over Internet Protocol ◽  
Statistical Features ◽  
Test Statistics ◽  
Low Bit Rate ◽  
Audio Streams ◽  
Steganalysis Method

This paper concentrates on the detection of steganography in inactive frames of low bit rate audio streams in Voice over Internet Protocol (VoIP) scenarios. Both theoretical and experimental analyses demonstrate that the distribution of 0 and 1 in encoding parameter bits becomes symmetric after a steganographic process. Moreover, this symmetry affects the frequency of each subsequence of parameter bits, and accordingly changes the poker test statistical features of encoding parameter bits. Employing the poker test statistics of each type of encoding parameter bits as detection features, we present a steganalysis method based on a support vector machine. We evaluate the proposed method with a large quantity of speech samples encoded by G.723.1 and compare it with the entropy test. The experimental results show that the proposed method is effective, and largely outperforms the entropy test in any cases.

scholarly journals Deteksi Tipe Modulasi Digital Pada Automatic Modulation Recognition Menggunakan Support Vector Machine dan Conjugate Gradient Polak Ribiere-Backpropagation

2019 ◽  
Vol 18 (2) ◽  
Author(s):  
Komang Tri Wahyuni ◽  
I Made Oka Widyantara ◽  
NMAE Dewi Wirastuti
Keyword(s):  
Support Vector Machine ◽  
Conjugate Gradient ◽  
Support Vector ◽  
Modulation Recognition ◽  
Statistical Feature ◽  
Automatic Modulation Recognition

Pada penelitian ini menggunakan data digital yang dibangkitkan secara random dalam seleksi ciri tipe modulasi. Adapun tipe modulasi yang digunakan adalah QPSK, 16QAM dan 64QAM. Pada proses ekstrasi ciri menggunakan pendekatan statistical feature set dengan metode Mean, Varian, Kurtosis dan Skewness, sedangkan seleksi ciri menggunakan Multi Class Support Vector Machine (SVM) dengan 5 kelas dalam klasifikasi diantaranya adalah (i) Bukan Fitur, (ii) Mean, (iii) Varian, (iv) Kurtosis dan (v) Skewness. Dalam mendeteksi tipe modulasi menggunakan Jaringan Syaraf Tiruan Backpropagation dengan proses pembelajaran menggunakan algoritma Conjugate Gradien Polak Ribiere. Dari hasil komparasi hasil pelatihan terhadap 401 data latih antara pembelajaran Conjugate Gradient Polak Ribiere dengan pembelajaran Gradient Discent adalah menggunakan Conjugate Gradient Polak Ribiere jauh lebih baik dengan nilai akurasi 86,20%, dan laju errornya 13,80% sedangkan pada pembelajaran dengan Conjugate Discent pada iterasi yang sama yaitu 781 tingkat akurasinya sebesar 67,83% dan laju errornya 32,17%. Dari hasil pengujian tersebut terdapat 4 kelompok fitur yang mampu mengenali tipe modulasi diantaranya adalah (i) Mean, Varian, Kurtosis, (ii) Mean, Varian, Skewness, (iii) Varian, Kurtosis, Skewnes dan (iv) Mean, Kurtosis, Skewness.     

scholarly journals Separable and Error-Free Reversible Data Hiding in Encrypted Image with High Payload

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Zhaoxia Yin ◽  
Bin Luo ◽  
Wien Hong
Keyword(s):  
Data Hiding ◽  
Reversible Data Hiding ◽  
Additional Data ◽  
Data Extraction ◽  
Receiver Side ◽  
Histogram Shifting ◽  
Encrypted Image ◽  
Free Data ◽  
Peak Points ◽  
High Payload

This paper proposes a separable reversible data-hiding scheme in encrypted image which offers high payload and error-free data extraction. The cover image is partitioned into nonoverlapping blocks and multigranularity encryption is applied to obtain the encrypted image. The data hider preprocesses the encrypted image and randomly selects two basic pixels in each block to estimate the block smoothness and indicate peak points. Additional data are embedded into blocks in the sorted order of block smoothness by using local histogram shifting under the guidance of the peak points. At the receiver side, image decryption and data extraction are separable and can be free to choose. Compared to previous approaches, the proposed method is simpler in calculation while offering better performance: larger payload, better embedding quality, and error-free data extraction, as well as image recovery.

Support vector machine? – not only for MRI-images

2020 ◽  
Author(s):  
V Vasilevska ◽  
K Schlaaf ◽  
H Dobrowolny ◽  
G Meyer-Lotz ◽  
HG Bernstein ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Support Vector

KLASIFIKASI SMS SPAM MENGGUNAKAN SUPPORT VECTOR MACHINE

2019 ◽  
Vol 15 (2) ◽  
pp. 275-280
Author(s):  
Agus Setiyono ◽  
Hilman F Pardede
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Decision Tree ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Spam Detection ◽  
Support Vector Machine Algorithm ◽  
Data Mining Techniques ◽  
To Receive

It is now common for a cellphone to receive spam messages. Great number of received messages making it difficult for human to classify those messages to Spam or no Spam.  One way to overcome this problem is to use Data Mining for automatic classifications. In this paper, we investigate various data mining techniques, named Support Vector Machine, Multinomial Naïve Bayes and Decision Tree for automatic spam detection. Our experimental results show that Support Vector Machine algorithm is the best algorithm over three evaluated algorithms. Support Vector Machine achieves 98.33%, while Multinomial Naïve Bayes achieves 98.13% and Decision Tree is at 97.10 % accuracy.

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