A detailed analysis of image and video forgery detection techniques

2022 ◽  
Author(s):  
Shobhit Tyagi ◽  
Divakar Yadav
Keyword(s):  
Detailed Analysis ◽  
Forgery Detection ◽  
Detection Techniques ◽  
Video Forgery Detection ◽  
Video Forgery

A Comprehensive Analysis on Video Forgery Detection Techniques

2020 ◽  
Author(s):  
Vinay Kumar ◽  
Abhishek Singh ◽  
Vineet kansal ◽  
Manish Gaur
Keyword(s):  
Comprehensive Analysis ◽  
Forgery Detection ◽  
Detection Techniques ◽  
Video Forgery Detection ◽  
Video Forgery

scholarly journals A Novel Video Forgery Detection Model Based on Triangular Polarity Feature Classification

2020 ◽  
Vol 12 (1) ◽  
pp. 14-34
Author(s):  
Chee Cheun Huang ◽  
Chien Eao Lee ◽  
Vrizlynn L. L. Thing
Keyword(s):  
Detection System ◽  
Recall Rate ◽  
Video Frame ◽  
Forgery Detection ◽  
Feature Classification ◽  
Localization Accuracy ◽  
Detection Techniques ◽  
Detection Model ◽  
Video Forgery Detection ◽  
Video Forgery

Video forgery has been increasing over the years due to the wide accessibility of sophisticated video editing software. A highly accurate and automated video forgery detection system will therefore be vitally important in ensuring the authenticity of forensic video evidences. This article proposes a novel Triangular Polarity Feature Classification (TPFC) video forgery detection framework for video frame insertion and deletion forgeries. The TPFC framework has high precision and recall rates with a simple and threshold-less algorithm designed for real-world applications. System robustness evaluations based on cross validation and different database recording conditions were also performed and validated. Evaluation on the performance of the TPFC framework demonstrated the efficacy of the proposed framework by achieving a recall rate of up to 98.26% and precision rate of up to 95.76%, as well as high localization accuracy on detected forged videos. The TPFC framework is further demonstrated to be capable of outperforming other modern video forgery detection techniques available today.

A review of video falsifying techniques and video forgery detection techniques

2017 ◽  
Vol 9 (3) ◽  
pp. 191
Author(s):  
Manal A. Mizher ◽  
Ahmad A. Mazhar ◽  
Mei Choo Ang ◽  
Manar A. Mizher
Keyword(s):  
Forgery Detection ◽  
Detection Techniques ◽  
Video Forgery Detection ◽  
Video Forgery

Passive video forgery detection techniques: A survey

2014 ◽  
Author(s):  
Ainuddin Wahid Abdul Wahab ◽  
Mustapha Aminu Bagiwa ◽  
Mohd Yamani Idna Idris ◽  
Suleman Khan ◽  
Zaidi Razak ◽  
...  
Keyword(s):  
Forgery Detection ◽  
Detection Techniques ◽  
Video Forgery Detection ◽  
Video Forgery

A review of video falsifying techniques and video forgery detection techniques

2017 ◽  
Vol 9 (3) ◽  
pp. 191 ◽  
Author(s):  
Manar A. Mizher ◽  
Mei Choo Ang ◽  
Ahmad A. Mazhar ◽  
Manal A. Mizher
Keyword(s):  
Forgery Detection ◽  
Detection Techniques ◽  
Video Forgery Detection ◽  
Video Forgery

scholarly journals DNN Based Moth Search Optimization for Video Forgery Detection

2019 ◽  
Vol 9 (1) ◽  
pp. 1190-1199
Keyword(s):  
Initial Step ◽  
Structural Features ◽  
Surveillance Systems ◽  
Forgery Detection ◽  
Detection Techniques ◽  
Detection Process ◽  
Double Compression ◽  
Search Optimization ◽  
Video Forgery Detection ◽  
Video Forgery

Recently, video tampering process becomes easier due to the rapid advancements in user-friendly editing software and multimedia technology (e.g., Mokey by Imagineer Systems, and Photoshop and Premiere by Adobe). This technologies may highly tamper the original videos, so that the audience gets mislead. Nowadays, MPEG-4 codec is included in a large proportions of video cameras and surveillance systems. Therefore the double compression detection process included as an initial step in the video forensic is receiving a high significance. In this paper, the double compression artifacts is detected by adopting the Markov based features, which identifies the interpolated original videos. The double compressed frames are then segmented by introducing an SLIC super pixel segmentation technique. Here, the feature extraction is performed by applying the scale information that is obtained from the multi-scale Gabor filters. The features of this Gabor scale accurately extract the structural features and also reduce too much of redundancy. This extracted features are then provided to DNN (deep neural network) for forgery detection. In this video forensic process, DNN classifier is included for forgery detection. The CNN classifier is included in various existing forgery detection techniques. But, in our work we include DNN because it contains number of hidden layers which provide accurate results for this forgery detection process. To improve the DNN performance, Moth Search Optimization (MSO) algorithm is introduced in this forgery detection technique. Every nook and corner of this world we can able to find the surveillance cameras for security purpose. But, some fraudsters perform forgeries in this recorded videos for their own benefits. To identify this, a lot of forgery detection techniques are coming into existence. So in this work, we introduce the DNN based MSO to perform the forgery detection in videos.This implementation is processed in python simulation platform. The parametric evaluations are taken in terms of F1- Score, average accuracy, Precision, Recall and. Experimental results will provide improved performance in video forgery detection.

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