scholarly journals DeepFake video production and SIFT-based analysis

2020 ◽  
Vol 12 (1) ◽  
pp. 22-27
Author(s):  
Miljan Đorđević ◽  
Milan Milivojević ◽  
Ana Gavrovska
Keyword(s):  
Video Production ◽  
Video Sequences ◽  
Original Video ◽  
Video Content ◽  
Scale Invariant ◽  
Still Images ◽  
Historical Figures ◽  
Sift Keypoints ◽  
Scale Invariant Feature

Nowadays advantages in face-based modification using DeepFake algorithms made it possible to replace a face of one person with a face of another person. Thus, it is possible to make not only copy-move modifications, but to implement artificial intelligence and deep learning for replacing face movements from one person to another. Still images can be converted into video sequences. Consequently, the contemporaries, historical figures or even animated characters can be lively presented. Deepfakes are becoming more and more successful and it is difficult to detect them in some cases. In this paper we explain the video sequences we produced (e.g. using X2Face method, and First Order Motion Model for Image Animation) and perform deepfake video analysis using SIFT (Scale Invariant Feature Transform) based approach. The experiments show the simplicity in video forgery production, as well as the possible role of SIFT keypoints detection in differentiation between the deeply forged and original video content.

scholarly journals Performance of SURF and SIFT Keypoints for the Automated Differentiation of Abnormality in Chest Radiographs

2021 ◽  
Author(s):  
Satyavratan Govindarajan ◽  
Ramakrishnan Swaminathan
Keyword(s):  
Lung Field ◽  
Statistical Features ◽  
Scale Invariant ◽  
Combined Features ◽  
Feature Transform ◽  
Automated Screening ◽  
Sift Keypoints ◽  
Linear Svm ◽  
Scale Invariant Feature ◽  
Lung Field Segmentation

In this work, automated abnormality detection using keypoint information from Speeded-Up Robust feature (SURF) and Scale Invariant Feature Transform (SIFT) descriptors in chest Radiographic (CR) images is investigated and compared. Computerized image analysis using artificial intelligence is crucial to detect subtle and non-specific alterations of Tuberculosis (TB). For this, the healthy and TB CRs are subjected to lung field segmentation. SURF and SIFT keypoints are extracted from the segmented lung images. Statistical features from keypoints, its scale and orientation are computed. Discrimination of TB from healthy is performed using SVM. Results show that the SURF and SIFT methods are able to extract local keypoint information in CRs. Linear SVM is found to perform better with precision of 88.9% and AUC of 91% in TB detection for combined features. Hence, the application of keypoint techniques is found to have clinical relevance in the automated screening of non-specific TB abnormalities using CRs.

Online topological segmentation of visual sequences using the algebraic connectivity of graphs

Robotica ◽  
2015 ◽  
Vol 34 (10) ◽  
pp. 2400-2413
Author(s):  
Jaime Boal ◽  
Álvaro Sánchez-Miralles
Keyword(s):  
Visual Information ◽  
Automatic Segmentation ◽  
Variable Length ◽  
Algebraic Connectivity ◽  
Scale Invariant ◽  
Color Histograms ◽  
Matching Procedure ◽  
Sift Keypoints ◽  
Scale Invariant Feature ◽  
Insight Into

SUMMARYIn the context of topological mapping, the automatic segmentation of an environment into meaningful and distinct locations is still regarded as an open problem. This paper presents an algorithm to extract places online from image sequences based on the algebraic connectivity of graphs or Fiedler value, which provides an insight into how well connected several consecutive observations are. The main contribution of the proposed method is that it is a theoretically supported alternative to tuning thresholds on similarities, which is a difficult task and environment dependent. It can accommodate any type of feature detector and matching procedure, as it only requires non-negative similarities as input, and is therefore able to deal with descriptors of variable length, to which statistical techniques are difficult to apply. The method has been validated in an office environment using exclusively visual information. Two different types of features, a bag-of-words model built from scale invariant feature transform (SIFT) keypoints, and a more complex fingerprint based on vertical lines, color histograms, and a few Star keypoints, are employed to demonstrate that the method can be applied to both fixed and variable length descriptors with similar results.

Robust Object Tracking by Particle Filter with Scale Invariant Features

2012 ◽  
Vol 151 ◽  
pp. 458-462
Author(s):  
Ming Xin ◽  
Sheng Wei Li ◽  
Miao Hui Zhang
Keyword(s):  
Particle Filter ◽  
Brute Force ◽  
Search Region ◽  
Scale Invariant ◽  
Invariant Feature ◽  
Object Search ◽  
Real Time Tracking ◽  
Sift Features ◽  
Sift Keypoints ◽  
Scale Invariant Feature

Few literatures employ SIFT (scale-invariant feature transform) for tracking because it is time-consuming. However, we found that SIFT can be adapted to real-time tracking by employing it on a subarea of the whole image. In this paper the particle filter based method exploits SIFT features to handle challenging scenarios such as partial occlusions, scale variations and moderate deformations. As proposed in our method, not a brute-force feature extraction in the whole image, we firstly extract SIFT keypoints in the object search region only for once, through matching SIFT features between object search region and object template, the number of matched keypoints is obtained, which is utilized to compute the particle weights. Finally, we can obtain an optimal estimate to object location by the particle filter framework. Comparative experiments with quantitative evaluations are provided, which indicate that the proposed method is both robust and faster.

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.

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