Block Dependency Feature Based Classification Scheme for Uncalibrated Image Steganalysis

2012 ◽  
pp. 189-195 ◽  
Author(s):  
Deepa D. Shankar ◽  
T. Gireeshkumar ◽  
K. Praveen ◽  
R. Jithin ◽  
Ashji S. Raj
Keyword(s):  
Classification Scheme ◽  
Image Steganalysis ◽  
Feature Based ◽  
Feature Based Classification

A Feature-Based Classification Scheme for Fixtures

CIRP Annals ◽  
1992 ◽  
Vol 41 (1) ◽  
pp. 189-192 ◽  
Author(s):  
A.Y.C. Nee ◽  
A. Senthil Kurnar ◽  
S. Prombanpong ◽  
K.Y. Puah
Keyword(s):  
Classification Scheme ◽  
Feature Based ◽  
Feature Based Classification

scholarly journals Doppler-Spectrum Feature-Based Human–Vehicle Classification Scheme Using Machine Learning for an FMCW Radar Sensor

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 2001 ◽  
Author(s):  
Eugin Hyun ◽  
YoungSeok Jin
Keyword(s):  
Classification Scheme ◽  
Vehicle Classification ◽  
Doppler Spectrum ◽  
Support Vector ◽  
Radar Sensor ◽  
Moving Vehicle ◽  
Fmcw Radar ◽  
Wave Radar ◽  
Spectrum Feature ◽  
Feature Based

In this paper, we propose a Doppler-spectrum feature-based human–vehicle classification scheme for an FMCW (frequency-modulated continuous wave) radar sensor. We introduce three novel features referred to as the scattering point count, scattering point difference, and magnitude difference rate features based on the characteristics of the Doppler spectrum in two successive frames. We also use an SVM (support vector machine) and BDT (binary decision tree) for training and validation of the three aforementioned features. We measured the signals using a 24-GHz FMCW radar front-end module and a real-time data acquisition module and extracted three features from a walking human and a moving vehicle in the field. We then repeatedly measured the classification decision rate of the proposed algorithm using the SVM and BDT, finding that the average performance exceeded 99% and 96% for the walking human and the moving vehicle, respectively.

scholarly journals Color image steganalysis based on embedding change probabilities in differential channels

2020 ◽  
Vol 16 (5) ◽  
pp. 155014772091782 ◽  
Author(s):  
Chunfang Yang ◽  
Yuhan Kang ◽  
Fenlin Liu ◽  
Xiaofeng Song ◽  
Jie Wang ◽  
...  
Keyword(s):  
Color Image ◽  
Adaptive Steganography ◽  
Potential Threat ◽  
Sensitive Data ◽  
Image Steganalysis ◽  
Feature Based ◽  
Occurrence Matrix ◽  
Model Features ◽  
Color Image Steganography ◽  
The Internet Of Things

It is a potential threat to persons and companies to reveal private or company-sensitive data through the Internet of Things by the color image steganography. The existing rich model features for color image steganalysis fail to utilize the fact that the content-adaptive steganography changes the pixels in complex textured regions with higher possibility. Therefore, this article proposes a variant of spatial rich model feature based on the embedding change probabilities in differential channels. The proposed feature is extracted from the residuals in the differential channels to reduce the image content information and enhance the stego signals significantly. Then, the embedding change probability of each element in the differential channels is added to the corresponding co-occurrence matrix bin to emphasize the interference of the residuals in textured regions to the improved co-occurrence matrix feature. The experimental results show that the proposed feature can significantly improve the detection performances for the WOW and S-UNIWARD steganography, especially when the payload size is small. For example, when the payload size is 0.05 bpp, the detection errors can be reduced respectively by 5.20% and 4.90% for WOW and S-UNIWARD by concatenating the proposed feature to the color rich model feature CRMQ1.

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