scholarly journals A video steganalysis method based on coding cost variation

2021 ◽  
Vol 17 (2) ◽  
pp. 155014772199273
Author(s):  
Jianyi Liu ◽  
Cong Zhang ◽  
Ru Zhang ◽  
Yi Li ◽  
Jie Cheng
Keyword(s):  
Motion Vector ◽  
Absolute Difference ◽  
Detection Accuracy ◽  
Motion Vectors ◽  
Optimal Matching ◽  
Vector Coding ◽  
Cost Variation ◽  
Before And After ◽  
Detection Effect ◽  
Video Steganalysis

Aiming at the problems existing in existing steganalysis algorithms, this article proposes Motion Vector Coding Cost Change video steganalysis features based on Improved Motion Vector Reversion-Based features and Subtractive Probability of Coding Cost Optimal Matching features based on Subtractive Probability of Optimal Matching features from the perspective of the change of coding cost. Motion Vector Coding Cost Change features can be well consistent with the coding cost before recoding by analyzing the sub-pixel coding cost of recoding. By counting the sub-pixel coding costs of motion vectors before and after video recoding, the Sum of Absolute Difference values of motion vectors instead of predicted residuals are applied to steganalysis and detection, and the steganographic algorithm based on motion vectors is effectively detected. Experiments show that Motion Vector Coding Cost Change features have higher detection accuracy than Add-or-Subtract-One, Improved Motion Vector Reversion-Based, and other typical features in various steganography methods, and Subtractive Probability of Coding Cost Optimal Matching features have higher detection effect and better robustness than Subtractive Probability of Optimal Matching features.

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.

scholarly journals Hazardous Drinking among Students over a Decade of University Policy Change: Controlled Before-and-After Evaluation

2018 ◽  
Vol 15 (10) ◽  
pp. 2137 ◽  
Author(s):  
Kypros Kypri ◽  
Brett Maclennan ◽  
Kimberly Cousins ◽  
Jennie Connor
Keyword(s):  
Environmental Changes ◽  
Population Policy ◽  
Code Of Conduct ◽  
Alcohol Policy ◽  
Absolute Difference ◽  
Effect Estimate ◽  
Policy Reforms ◽  
Student Body ◽  
University Policy ◽  
Before And After

Background: Responding to high levels of alcohol-related harm among students, a New Zealand university deployed a security and liaison service, strengthened the Student Code of Conduct, increased its input on the operation of alcohol outlets near campus, and banned alcohol advertising on campus. We estimated the change in the prevalence of alcohol consumption patterns among students at the university compared with other universities. Methods: We conducted a controlled before-and-after study with surveys in residential colleges at the target university in 2004 and 2014, and in random samples of students at the target university and three control universities in 2005 and 2013. The primary outcome was the prevalence of recent intoxication, while we analysed drinking per se and drinking in selected locations to investigate mechanisms of change. Results: The 7-day prevalence of intoxication decreased from 45% in 2004 to 33% in 2014 (absolute difference: 12%; 95% CI: 7% to 17%) among students living in residential colleges, and from 40% in 2005 to 26% in 2013 (absolute difference: 14%; 95% CI: 8% to 20%) in the wider student body of the intervention university. The intervention effect estimate, representing the change at the intervention university adjusted for change at other universities (aOR = 1.30; 95% CI: 0.89 to 1.90), was consistent with a benefit of intervention but was not statistically significant (p = 0.17). Conclusion: In this period of alcohol policy reform, drinking to intoxication decreased substantially in the targeted student population. Policy reforms and coincidental environmental changes may each have contributed to these reductions.

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

scholarly journals Adaptive Interleaved Motion Vector Coding using Motion Characteristics

2011 ◽  
Vol 16 (2) ◽  
pp. 372-383
Author(s):  
Kwang-Hyun Won ◽  
Jung-Youp Yang ◽  
Dae-Yun Park ◽  
Byeung-Woo Jeon
Keyword(s):  
Motion Vector ◽  
Vector Coding ◽  
Motion Characteristics

scholarly journals Modified S2CVA Algorithm Using Cross-Sharpened Images for Unsupervised Change Detection

2018 ◽  
Vol 10 (9) ◽  
pp. 3301 ◽  
Author(s):  
Honglyun Park ◽  
Jaewan Choi ◽  
Wanyong Park ◽  
Hyunchun Park
Keyword(s):  
Change Detection ◽  
False Alarm ◽  
False Alarm Rate ◽  
Seasonal Effects ◽  
Detection Accuracy ◽  
Difference Image ◽  
Before And After ◽  
Change Detection Algorithms ◽  
The Difference ◽  
Direction Information

This study aims to reduce the false alarm rate due to relief displacement and seasonal effects of high-spatial-resolution multitemporal satellite images in change detection algorithms. Cross-sharpened images were used to increase the accuracy of unsupervised change detection results. A cross-sharpened image is defined as a combination of synthetically pan-sharpened images obtained from the pan-sharpening of multitemporal images (two panchromatic and two multispectral images) acquired before and after the change. A total of four cross-sharpened images were generated and used in combination for change detection. Sequential spectral change vector analysis (S2CVA), which comprises the magnitude and direction information of the difference image of the multitemporal images, was applied to minimize the false alarm rate using cross-sharpened images. Specifically, the direction information of S2CVA was used to minimize the false alarm rate when applying S2CVA algorithms to cross-sharpened images. We improved the change detection accuracy by integrating the magnitude and direction information obtained using S2CVA for the cross-sharpened images. In the experiment using KOMPSAT-2 satellite imagery, the false alarm rate of the change detection results decreased with the use of cross-sharpened images compared to that with the use of only the magnitude information from the original S2CVA.

Performance Evaluation of Different Cost Functions in Motion Vector Estimation

2014 ◽  
Vol 5 (1) ◽  
pp. 45-65 ◽  
Author(s):  
Suvojit Acharjee ◽  
Sayan Chakraborty ◽  
Wahiba Ben Abdessalem Karaa ◽  
Ahmad Taher Azar ◽  
Nilanjan Dey
Keyword(s):  
Video Compression ◽  
Motion Vector ◽  
Video Data ◽  
Cost Functions ◽  
Absolute Difference ◽  
Surveillance Video ◽  
Mean Absolute Difference ◽  
Vector Estimation ◽  
The Difference ◽  
Temporal Redundancy

Video is an important medium in terms of information sharing in this present era. The tremendous growth of video use can be seen in the traditional multimedia application as well as in many other applications like medical videos, surveillance video etc. Raw video data is usually large in size, which demands for video compression. In different video compressing schemes, motion vector is a very important step to remove the temporal redundancy. A frame is first divided into small blocks and then motion vector for each block is computed. The difference between two blocks is evaluated by different cost functions (i.e. mean absolute difference (MAD), mean square error (MSE) etc).In this paper the performance of different cost functions was evaluated and also the most suitable cost function for motion vector estimation was found.

scholarly journals Moving Object Detection Using an Object Motion Reflection Model of Motion Vectors

Symmetry ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 34 ◽  
Author(s):  
Jisang Yoo ◽  
Gyu-cheol Lee
Keyword(s):  
Object Detection ◽  
Moving Objects ◽  
Probability Model ◽  
Motion Vector ◽  
Moving Object Detection ◽  
Object Motion ◽  
Moving Object ◽  
Disparity Map ◽  
Motion Vectors ◽  
Reflection Model

Moving object detection task can be solved by the background subtraction algorithm if the camera is fixed. However, because the background moves, detecting moving objects in a moving car is a difficult problem. There were attempts to detect moving objects using LiDAR or stereo cameras, but when the car moved, the detection rate decreased. We propose a moving object detection algorithm using an object motion reflection model of motion vectors. The proposed method first obtains the disparity map by searching the corresponding region between stereo images. Then, we estimate road by applying v-disparity method to the disparity map. The optical flow is used to acquire the motion vectors of symmetric pixels between adjacent frames where the road has been removed. We designed a probability model of how much the local motion is reflected in the motion vector to determine if the object is moving. We have experimented with the proposed method on two datasets, and confirmed that the proposed method detects moving objects with higher accuracy than other methods.

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