scholarly journals Segmentation Based Video Steganalysis to Detect Motion Vector Modification

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Peipei Wang ◽  
Yun Cao ◽  
Xianfeng Zhao
Keyword(s):  
Motion Vector ◽  
Experimental Results ◽  
Statistical Characteristics ◽  
Video Content ◽  
Video Steganography ◽  
Fixed Length ◽  
Content Diversity ◽  
The Given ◽  
Video Steganalysis ◽  
Content Adaptive

This paper presents a steganalytic approach against video steganography which modifies motion vector (MV) in content adaptive manner. Current video steganalytic schemes extract features from fixed-length frames of the whole video and do not take advantage of the content diversity. Consequently, the effectiveness of the steganalytic feature is influenced by video content and the problem of cover source mismatch also affects the steganalytic performance. The goal of this paper is to propose a steganalytic method which can suppress the differences of statistical characteristics caused by video content. The given video is segmented to subsequences according to block’s motion in every frame. The steganalytic features extracted from each category of subsequences with close motion intensity are used to build one classifier. The final steganalytic result can be obtained by fusing the results of weighted classifiers. The experimental results have demonstrated that our method can effectively improve the performance of video steganalysis, especially for videos of low bitrate and low embedding ratio.

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

scholarly journals Automatic Image Captioning Based on ResNet50 and LSTM with Soft Attention

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Yan Chu ◽  
Xiao Yue ◽  
Lei Yu ◽  
Mikhailov Sergei ◽  
Zhengkui Wang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Recurrent Neural Network ◽  
Attention Mechanism ◽  
Experimental Results ◽  
Challenging Problem ◽  
Image Captioning ◽  
Training Images ◽  
Fixed Length ◽  
The Given

Captioning the images with proper descriptions automatically has become an interesting and challenging problem. In this paper, we present one joint model AICRL, which is able to conduct the automatic image captioning based on ResNet50 and LSTM with soft attention. AICRL consists of one encoder and one decoder. The encoder adopts ResNet50 based on the convolutional neural network, which creates an extensive representation of the given image by embedding it into a fixed length vector. The decoder is designed with LSTM, a recurrent neural network and a soft attention mechanism, to selectively focus the attention over certain parts of an image to predict the next sentence. We have trained AICRL over a big dataset MS COCO 2014 to maximize the likelihood of the target description sentence given the training images and evaluated it in various metrics like BLEU, METEROR, and CIDEr. Our experimental results indicate that AICRL is effective in generating captions for the images.

Detecting “DeepFakes” in H.264 Video Data Using Compression Ghost Artifacts

2020 ◽  
Vol 2020 (4) ◽  
pp. 116-1-116-7
Author(s):  
Raphael Antonius Frick ◽  
Sascha Zmudzinski ◽  
Martin Steinebach
Keyword(s):  
Image Forensics ◽  
Video Data ◽  
Experimental Results ◽  
Video Sequences ◽  
The Internet ◽  
Video Content ◽  
High Quality ◽  
The Public

In recent years, the number of forged videos circulating on the Internet has immensely increased. Software and services to create such forgeries have become more and more accessible to the public. In this regard, the risk of malicious use of forged videos has risen. This work proposes an approach based on the Ghost effect knwon from image forensics for detecting forgeries in videos that can replace faces in video sequences or change the mimic of a face. The experimental results show that the proposed approach is able to identify forgery in high-quality encoded video content.

IMPROVED TIME-MULTIPLEXED FPGA ARCHITECTURE AND ALGORITHM FOR MINIMIZING COMMUNICATION COST DESIGNS

2013 ◽  
Vol 22 (05) ◽  
pp. 1350033
Author(s):  
CHI-CHOU KAO ◽  
YEN-TAI LAI
Keyword(s):  
Greedy Algorithm ◽  
Sequential Circuits ◽  
Precedence Constraints ◽  
Communication Cost ◽  
Experimental Results ◽  
Time Sharing ◽  
Fpga Architecture ◽  
The Given

The Time-Multiplexed FPGA (TMFPGA) architecture can improve dramatically logic utilization by time-sharing logic but it needs a large amount of registers among sub-circuits for partitioning the given sequential circuits. In this paper, we propose an improved TMFPGA architecture to simplify the precedence constraints so that the number of the registers among sub-circuits can be reduced for sequential circuits partitioning. To demonstrate the practicability of the architecture, we also present a greedy algorithm to minimize the maximum number of the registers. Experimental results demonstrate the effectives of the algorithm.

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 A Multilayer CARU Framework to Obtain Probability Distribution for Paragraph-Based Sentiment Analysis

2021 ◽  
Vol 11 (23) ◽  
pp. 11344
Author(s):  
Wei Ke ◽  
Ka-Hou Chan
Keyword(s):  
Probability Distribution ◽  
Information Extraction ◽  
Sentiment Analysis ◽  
State Of The Art ◽  
Final Analysis ◽  
The State ◽  
Experimental Results ◽  
Content Adaptive

Paragraph-based datasets are hard to analyze by a simple RNN, because a long sequence always contains lengthy problems of long-term dependencies. In this work, we propose a Multilayer Content-Adaptive Recurrent Unit (CARU) network for paragraph information extraction. In addition, we present a type of CNN-based model as an extractor to explore and capture useful features in the hidden state, which represent the content of the entire paragraph. In particular, we introduce the Chebyshev pooling to connect to the end of the CNN-based extractor instead of using the maximum pooling. This can project the features into a probability distribution so as to provide an interpretable evaluation for the final analysis. Experimental results demonstrate the superiority of the proposed approach, being compared to the state-of-the-art models.

Experimental Investigations of the Machinability of Titanium Alloy TC4 with Different Hydrogen Contents

2011 ◽  
Vol 418-420 ◽  
pp. 1307-1311
Author(s):  
Jun Hu ◽  
Yong Jie Bao ◽  
Hang Gao ◽  
Ke Xin Wang
Keyword(s):  
Surface Roughness ◽  
Titanium Alloy ◽  
Cutting Force ◽  
Hydrogen Content ◽  
Experimental Results ◽  
Cutting Conditions ◽  
Experimental Investigations ◽  
Cutting Condition ◽  
The Given

The experiments were carried out in the paper to investigate the effect of adding hydrogen in titanium alloy TC4 on its machinability. The hydrogen contents selected were 0, 0.25%, 0.49%, 0.63%, 0.89% and 1.32%, respectively. Experiments with varing hydrogen contents and cutting conditions concurrently. Experimental results showed that the cutting force of the titanium alloy can be obviously reduced and the surface roughness can be improved by adding appropriate hydrogen in the material. In the given cutting condition, the titanium alloy TC4 with 0.49% hydrogen content showed better machinability.

scholarly journals Hierarchical Classification Based on Label Distribution Learning

2019 ◽  
Vol 33 ◽  
pp. 5533-5540 ◽  
Author(s):  
Changdong Xu ◽  
Xin Geng
Keyword(s):  
Hierarchical Classification ◽  
Experimental Results ◽  
Challenging Problem ◽  
Training Set ◽  
True Label ◽  
Major Bottleneck ◽  
Novel Method ◽  
Class Labels ◽  
Label Distribution ◽  
The Given

Hierarchical classification is a challenging problem where the class labels are organized in a predefined hierarchy. One primary challenge in hierarchical classification is the small training set issue of the local module. The local classifiers in the previous hierarchical classification approaches are prone to over-fitting, which becomes a major bottleneck of hierarchical classification. Fortunately, the labels in the local module are correlated, and the siblings of the true label can provide additional supervision information for the instance. This paper proposes a novel method to deal with the small training set issue. The key idea of the method is to represent the correlation among the labels by the label distribution. It generates a label distribution that contains the supervision information of each label for the given instance, and then learns a mapping from the instance to the label distribution. Experimental results on several hierarchical classification datasets show that our method significantly outperforms other state-of-theart hierarchical classification approaches.

Mining Allocating Patterns in Investment Portfolios

2009 ◽  
pp. 110-135
Author(s):  
Yanbo J. Wang ◽  
Xinwei Zheng ◽  
Frans Coenen
Keyword(s):  
Data Mining ◽  
Portfolio Management ◽  
Association Rule ◽  
Common Type ◽  
Experimental Results ◽  
Transaction Database ◽  
Investment Portfolios ◽  
The Given

An association rule (AR) is a common type of mined knowledge in data mining that describes an implicative co-occurring relationship between two sets of binary-valued transaction-database attributes, expressed in the form of an ? rule. A variation of ARs is the (WARs), which addresses the weighting issue in ARs. In this chapter, the authors introduce the concept of “one-sum” WAR and name such WARs as allocating patterns (ALPs). An algorithm is proposed to extract hidden and interesting ALPs from data. The authors further indicate that ALPs can be applied in portfolio management. Firstly by modelling a collection of investment portfolios as a one-sum weighted transaction- database that contains hidden ALPs. Secondly the authors show that ALPs, mined from the given portfolio-data, can be applied to guide future investment activities. The experimental results show good performance that demonstrates the effectiveness of using ALPs in the proposed application.

Detection of motion vector based video steganography

2014 ◽  
Vol 74 (23) ◽  
pp. 10479-10494 ◽  
Author(s):  
Arijit Sur ◽  
Sista Venkat Madhav Krishna ◽  
Nilkanta Sahu ◽  
Shuvendu Rana
Keyword(s):  
Motion Vector ◽  
Video Steganography
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