scholarly journals Optimality of principal component filter banks for discrete multitone communication systems

2002 ◽  
Author(s):  
P.P. Vaidyanathan ◽  
Yun-Pei Lin ◽  
S. Akkarakaran ◽  
See-May Phoong
Keyword(s):  
Communication Systems ◽  
Filter Banks ◽  
Principal Component ◽  
Discrete Multitone

scholarly journals Discrete multitone modulation with principal component filter banks

2002 ◽  
Vol 49 (10) ◽  
pp. 1397-1412 ◽  
Author(s):  
P.P. Vaidyanathan ◽  
Yuan-Pei Lin ◽  
S. Akkarakaran ◽  
See-May Phoong
Keyword(s):  
Filter Banks ◽  
Principal Component ◽  
Discrete Multitone

scholarly journals Detection of Stego Images with Adaptively Embedded Data by Component Analysis Methods

2021 ◽  
Vol 6 (2) ◽  
pp. 146-154
Author(s):  
Dmytro Progonov ◽  
Keyword(s):  
Communication Systems ◽  
Principal Component ◽  
Component Analysis ◽  
Detection Accuracy ◽  
Sensitive Data ◽  
Analysis Methods ◽  
Cover Ratio ◽  
High Pass ◽  
Selection Of ◽  
Image Calibration

Ensuring the effective protection of personal and corporate sensitive data is topical task today. The special interest is taken at sensitive data leakage prevention during files transmission in communication systems. In most cases, these leakages are conducted by usage of advance adaptive steganographic methods. These methods are aimed at minimizing distortions of cover files, such as digital images, during data hiding that negatively impact on detection accuracy of formed stego images. For overcoming this shortcoming, it was proposed to pre-process (calibrate) analyzed images for increasing stego- to-cover ratio. The modern paradigm of image calibration is based on usage of enormous set of high-pass filters. However, selection of filter(s) that maximizes the probability of stego images detection is non-trivial task, especially in case of limited a prior knowledge about embedding methods. For solving this task, we proposed to use component analysis methods for image calibration, namely principal components analysis. Results of comparative analysis of novel maxSRMd2 cover rich model and proposed solution showed that principal component analysis allows increasing detection accuracy up to 1.5% even in the most difficult cases (low cover image payload and absence of cover- stego images pairs in training set).

FIR principal component filter banks

1998 ◽  
Vol 46 (4) ◽  
pp. 930-940 ◽  
Author(s):  
Bo Xuan ◽  
R.I. Bamberger
Keyword(s):  
Filter Banks ◽  
Principal Component
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