Motion vector-based video steganalysis using spatial-temporal correlation

2013 ◽  
Author(s):  
Hui Ye ◽  
Weiming Zhang ◽  
Yuanzhi Yao ◽  
Cong Kong ◽  
Hao Huang ◽  
...  
Keyword(s):  
Motion Vector ◽  
Temporal Correlation ◽  
Video Steganalysis

Adaptive Return Prediction Block Matching Algorithms for Video Coding

2011 ◽  
Vol 145 ◽  
pp. 277-281
Author(s):  
Vaci Istanda ◽  
Tsong Yi Chen ◽  
Wan Chun Lee ◽  
Yuan Chen Liu ◽  
Wen Yen Chen
Keyword(s):  
Motion Estimation ◽  
Video Compression ◽  
Web Site ◽  
Motion Vector ◽  
Temporal Correlation ◽  
Block Matching ◽  
Asynchronous Learning ◽  
Learning And Teaching ◽  
Block Motion ◽  
Reconstruction Image

As the development of network learning, video compression is important for both data transmission and storage, especially in a digit channel. In this paper, we present the return prediction search (RPS) algorithm for block motion estimation. The proposed algorithm exploits the temporal correlation and characteristic of returning origin to obtain one or two predictive motion vector and selects one motion vector, which presents better result, to be the initial search center. In addition, we utilize the center-biased block matching algorithms to refine the final motion vector. Moreover, we used adaptive threshold technique to reduce the computational complexity in motion estimation. Experimental results show that RPS algorithm combined with 4SS, BBGDS, and UCBDS effectively improves the performance in terms of mean-square error measure with less average searching points. On the other hand, accelerated RPS (ARPS) algorithm takes only 38% of the searching computations than 3SS algorithm, and the reconstruction image quality of the ARPS algorithm is superior to 3SS algorithm about 0.30dB in average overall test sequences. In addition, we create an asynchronous learning environment which provides students and instructors flexibility in learning and teaching activities. The purpose of this web site is to teach and display our researchable results. Therefore, we believe this web site is one of the keys to help the modern student achieve mastery of complex Motion Estimation.

Digital video steganalysis using motion vector recovery-based features

2012 ◽  
Vol 51 (20) ◽  
pp. 4667 ◽  
Author(s):  
Yu Deng ◽  
Yunjie Wu ◽  
Linna Zhou
Keyword(s):  
Digital Video ◽  
Motion Vector ◽  
Video Steganalysis

Domain Adaptation for Video Steganalysis against Motion Vector Based Steganography

2014 ◽  
Vol 998-999 ◽  
pp. 1138-1145
Author(s):  
Ke Ren Wang ◽  
Wen Xiang Li
Keyword(s):  
Real World ◽  
Domain Adaptation ◽  
Motion Vector ◽  
Component Analysis ◽  
Potential Effect ◽  
Opposite Case ◽  
Real World Applications ◽  
Performance Results ◽  
Video Steganalysis

Video steganalysis takes effect when videos corrupted by the target steganography method are available. Nevertheless, classical classifiers deteriorate in the opposite case. This paper presents a method to cope with the problem of steganography method mismatch for the detection of motion vector (MV) based steganography. Firstly, Adding-or-Subtracting-One (AoSO) feature against MV based steganography and Transfer Component Analysis (TCA) for domain adaptation are revisited. Distributions of AoSO feature against various MV based steganography methods are illustrated, followed by the potential effect of TCA based AoSO feature. Finally, experiments are carried out on various cases of steganography method mismatch. Performance results demonstrate that TCA+AoSO feature significantly outperforms AoSO feature, and is more favorable for real-world applications.

Video Steganalysis Exploiting Motion Vector Calibration-Based Features

2012 ◽  
Vol 482-484 ◽  
pp. 168-172 ◽  
Author(s):  
Yu Deng ◽  
Yun Jie Wu ◽  
Lin Na Zhou
Keyword(s):  
Support Vector Machine ◽  
Feature Vector ◽  
Motion Vector ◽  
Support Vector ◽  
Motion Vectors ◽  
Feature Vectors ◽  
Video Steganalysis

The motion vector (MV)-based steganography embeds the secret messages by modifying the motion vectors. So the traditional video steganalytic schemes cannot detect the presence of the hidden messages by MV-based steganography. In this paper, a novel calibration-based steganalytic scheme against MV-based steganography is presented. The features are derived from the shift differences between the original and calibrated MVs, and then the feature vector is constructed. Using the extracted feature vectors, the support vector machine (SVM) is trained to detect the presence of stego videos. Compared with other features, the proposed features have better performance even with the low embedding strength.

Digital video steganalysis based on motion vector statistical characteristics

Optik ◽  
2013 ◽  
Vol 124 (14) ◽  
pp. 1705-1710 ◽  
Author(s):  
Yu Deng ◽  
Yunjie Wu ◽  
Haibin Duan ◽  
Linna Zhou
Keyword(s):  
Digital Video ◽  
Motion Vector ◽  
Statistical Characteristics ◽  
Video Steganalysis
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