Video interframe forgery detection: Classification, technique & new dataset

2021 ◽  
pp. 1-20
Author(s):  
Himani Sharma ◽  
Navdeep Kanwal
Keyword(s):  
Original Video ◽  
Current Frame ◽  
Detection Techniques ◽  
A New Technique ◽  
Moment Features ◽  
Duplication Detection ◽  
Hu Moment ◽  
Frame Duplication ◽  
Duplicate Video

Multimedia communication as well as other related innovations are gaining tremendous growth in the modern technological era. Even though digital content has traditionally proved to be a piece of legitimate evidence. But the latest technologies have lessened this trust, as a variety of video editing tools have been developed to modify the original video. Therefore, in order to resolve this problem, a new technique has been proposed for the detection of duplicate video sequences. The present paper utilizes gray values to extract Hu moment features in the current frame. These features are further used for classification of video as authentic or forged. Afterwards there was also need to validate the proposed technique using training and test dataset. But the scarcity of training and test datasets, however, is indeed one of the key problems to validate the effectiveness of video tampering detection techniques. In this perspective, the Video Forensics Library for Frame Duplication (VLFD) dataset has been introduced for frame duplication detection purposes. The proposed dataset is made of 210 native videos, in Ultra-HD and Full-HD resolution, captured with different cameras. Every video is 6 to 15 seconds in length and runs at 30 frames per second. All the recordings have been acquired in three different scenarios (indoor, outdoor, nature) and in landscape mode(s). VLFD includes both authentic and manipulated video files. This dataset has been created as an initial repository for manipulated video and enhanced with new features and new techniques in future.

Fetal cardiotocography monitoring using Legendre neural networks

2019 ◽  
Vol 64 (6) ◽  
pp. 669-675 ◽  
Author(s):  
Abdulaziz Alsayyari
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Uterine Contraction ◽  
High Efficiency ◽  
Fetal Monitoring ◽  
Data Sets ◽  
Legendre Series ◽  
Electronic Fetal Monitoring ◽  
A New Technique

Abstract A new technique for electronic fetal monitoring (EFM) using an efficient structure of neural networks based on the Legendre series is presented in this paper. Such a structure is achieved by training a Legendre series-based neural network (LNN) to classify the different fetal states based on recorded cardiotocographic (CTG) data sets given by others. These data sets consist of measurements of fetal heart rate (FHR) and uterine contraction (UC). The applied LNN utilizes a Legendre series expansion for the input vectors and, hence, has the capability to produce explicit equations describing multi-input multi-output systems. Simulations of the proposed technique in EFM demonstrate its high efficiency. Training the LNN requires a few number of iterations (5–10 epochs). The applied technique makes the classification of the fetal state available through equations combining the trained LNN weights and the current measured CTG record. A comparison of performance between the proposed LNN and other popular neural network techniques such as the Volterra neural network (VNN) in EFM is provided. The comparison shows that, the LNN outperforms the VNN in case of less computational requirements and fast convergence with a lower mean square error.

Traffic Signs Recognition Based on Affine Invariant Hu's Moment Features

2013 ◽  
Vol 321-324 ◽  
pp. 945-949
Author(s):  
Min Liu ◽  
Jian Xu Mao
Keyword(s):  
Geometric Distortion ◽  
Affine Invariant ◽  
Traffic Sign ◽  
Traffic Signs ◽  
Sign Recognition ◽  
Hu Moments ◽  
Recognition Efficiency ◽  
Moment Features ◽  
Hu Moment ◽  
Moment Feature

Traffic signs effected by shooting environment and natural environment , and varying degrees of geometric distortion. This work introduced a new method to extract the traffic signs' Hu moment features that based on affine invariant. First , according to the shape coordinates x,y of traffic sign is independent of each other before affine transformation . Then getting traffic signs only have rotating effect by ICA transformation . Finally , recognizing traffic sign by compare the Hu's moment feature. Results show this method can greatly improve the feature extraction accuracy of Hu moments and traffic sign recognition efficiency

A NEW TECHNIQUE FOR CLASSIFICATION OF DIGITAL SIGNAL TYPES

2008 ◽  
Vol 17 (05) ◽  
pp. 957-971
Author(s):  
ATAOLLAH EBRAHIMZADEH ◽  
ABOLFAZL RANJBAR ◽  
MEHRDAD ARDEBLILPOUR
Keyword(s):  
High Performance ◽  
Cross Validation ◽  
Digital Signal ◽  
Higher Order ◽  
New Technique ◽  
Communication Signals ◽  
Communication Signal ◽  
A New Technique ◽  
Fold Cross Validation

Classification of the communication signals has seen under increasing demands. In this paper, we present a new technique that identifies a variety of digital communication signal types. This technique utilizes a radial basis function neural network (RBFN) as the classifier. Swarm intelligence, as an evolutionary algorithm, is used to construct RBFN. A combination of the higher-order moments and the higher-order cumulants up to eight are selected as the features of the considered digital signal types. In conjunction with RBFN, we have used k-fold cross-validation to improve the generalization potentiality. Simulation results show that the proposed technique has high performance for classification of different communication signals even at very low signal-to-noise ratios.

scholarly journals An Improved Fingerprinting Algorithm for Detection of Video Frame Duplication Forgery

2012 ◽  
Vol 4 (3) ◽  
pp. 20-32 ◽  
Author(s):  
Yongjian Hu ◽  
Chang-Tsun Li ◽  
Yufei Wang ◽  
Bei-bei Liu
Keyword(s):  
Computational Efficiency ◽  
State Of The Art ◽  
Experimental Results ◽  
Video Frame ◽  
Detection Accuracy ◽  
Forgery Detection ◽  
Detection Algorithms ◽  
Dct Coefficients ◽  
Duplication Detection ◽  
Frame Duplication

Frame duplication is a common way of digital video forgeries. State-of-the-art approaches of duplication detection usually suffer from heavy computational load. In this paper, the authors propose a new algorithm to detect duplicated frames based on video sub-sequence fingerprints. The fingerprints employed are extracted from the DCT coefficients of the temporally informative representative images (TIRIs) of the sub-sequences. Compared with other similar algorithms, this study focuses on improving fingerprints representing video sub-sequences and introducing a simple metric for the matching of video sub-sequences. Experimental results show that the proposed algorithm overall outperforms three related duplication forgery detection algorithms in terms of computational efficiency, detection accuracy and robustness against common video operations like compression and brightness change.

Identification and Classification of Cyber Threats Through SSH Honeypot Systems

2020 ◽  
pp. 105-129
Author(s):  
José María Jorquera Valero ◽  
Manuel Gil Pérez ◽  
Alberto Huertas Celdrán ◽  
Gregorio Martínez Pérez
Keyword(s):  
Machine Learning ◽  
Early Detection ◽  
Machine Learning Algorithms ◽  
Intrusion Detection Systems ◽  
Security Systems ◽  
Detection Techniques ◽  
Detection Systems ◽  
Cyber Threats ◽  
Detection Capabilities

As the number and sophistication of cyber threats increases year after year, security systems such as antivirus, firewalls, or Intrusion Detection Systems based on misuse detection techniques are improved in detection capabilities. However, these traditional systems are usually limited to detect potential threats, since they are inadequate to spot zero-day attacks or mutations in behaviour. Authors propose using honeypot systems as a further security layer able to provide an intelligence holistic level in detecting unknown threats, or well-known attacks with new behaviour patterns. Since brute-force attacks are increasing in recent years, authors opted for an SSH medium-interaction honeypot to acquire a log set from attacker's interactions. The proposed system is able to acquire behaviour patterns of each attacker and link them with future sessions for early detection. Authors also generate a feature set to feed Machine Learning algorithms with the main goal of identifying and classifying attacker's sessions, and thus be able to learn malicious intentions in executing cyber threats.

A Comparative Study of Unsupervised Video Shot Boundary Detection Techniques Using Probabilistic Fuzzy Entropy Measures

2013 ◽  
pp. 228-240 ◽  
Author(s):  
Biswanath Chakraborty ◽  
Siddhartha Bhattacharyya ◽  
Susanta Chakraborty
Keyword(s):  
Boundary Detection ◽  
Fuzzy Entropy ◽  
Detection Technique ◽  
Shot Boundary Detection ◽  
Detection Techniques ◽  
Entropy Measures ◽  
Video Shot ◽  
Shot Boundary ◽  
Improved Performance ◽  
A New Technique

The performance of video shot boundary detection technique in unsupervised video sequence can be improved by the use of different probabilistic fuzzy entropies. In this chapter, the authors present a new technique for identifying as to whether there are any appreciable changes from one video context to another in the available sequence of image frames extracted from a mixture of a numbers of video files. They then compared their technique with an existing technique and found improved performance of the video shot boundary detection techniques using probabilistic fuzzy entropies.

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