scholarly journals A Review : Video Tampering Attacks and Detection Techniques

2019 ◽  
pp. 158-168
Author(s):  
Ruksana Habeeb ◽  
L. C. Manikandan
Keyword(s):  
Digital Video ◽  
Review Paper ◽  
Image Editing ◽  
Forgery Detection ◽  
Tampering Detection ◽  
Detection Techniques ◽  
Visual Evidence ◽  
Different Types ◽  
Video Forgery

Technological advancements of various video and image editing tools has reached such a level that the tampering of digital video or image can be performed easily without degrading their quality or leaving any visual evidence. This review paper presents an overview of various types of video forgery and the different types of techniques that are employed for its detection. Passive and active forgery detection techniques are commonly used methods for detecting the tampering in a digital video. Passive and active tampering detection techniques are utilized for detecting the integrity as well as the authenticity of a given video. The aim of this review is to provide some productive information about video tampering attacks for upcoming researchers.

scholarly journals An Overview of Digital Video Tampering Detection Using Passive Methods and D-Hash Algorithm

2018 ◽  
Vol 7 (4.6) ◽  
pp. 373
Author(s):  
Anto Crescentia.A ◽  
Sujatha. G
Keyword(s):  
Digital Video ◽  
Forgery Detection ◽  
Tampering Detection ◽  
Integrity Verification ◽  
Hash Algorithm ◽  
Area Of Concern ◽  
Detection Strategies ◽  
Inter Frame ◽  
Passive Techniques ◽  
Integrity Check

Video tampering and integrity detection can be defined as methods of alteration of the contents of the video which will enable it to hide objects, an occasion or adjust the importance passed on by the collection of images in the video. Modification of video contents is growing rapidly due to the expansion of the video procurement gadgets and great video altering programming devices. Subsequently verification of video files is transforming into something very vital. Video integrity verification aims to search out the hints of altering and subsequently asses the realness and uprightness of the video. These strategies might be ordered into active and passive techniques. Therefore our area of concern in this paper is to present our views on different passive video tampering detection strategies and integrity check. Passive video tampering identification strategies are grouped into consequent three classifications depending on the type of counterfeiting as: Detection of double or multiple compressed videos, Region altering recognition and Video inter-frame forgery detection. So as to detect the tampering of the video, it is split into frames and hash is generated for a group of frames referred to as Group of Pictures. This hash value is verified by the receiver to detect tampering.    

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.

Passive Video Tampering Detection Using Noise Features

2016 ◽  
pp. 279-303
Author(s):  
Ramesh Chand Pandey ◽  
Sanjay Kumar Singh ◽  
K. K. Shukla
Keyword(s):  
Digital Signature ◽  
Digital Video ◽  
Low Cost ◽  
Digital Watermark ◽  
Detection Technique ◽  
Video Editing ◽  
Tampering Detection ◽  
Detection Techniques ◽  
Processing Pipeline ◽  
Active Video

With increasing availability of low-cost video editing softwares and tools, the authenticity of digital video can no longer be trusted. Active video tampering detection technique utilize digital signature or digital watermark for the video tampering detection, but when the videos do not include such signature then it is very challenging to detect tampering in such video. To detect tampering in such video, passive video tampering detection techniques are required. In this chapter we have explained passive video tampering detection by using noise features. When video is captured with camera it passes through a Camera processing pipeline and this introduces noise in the video. Noise changes abruptly from authentic to forged frame blocks and provides a clue for video tampering detection. For extracting the noise we have considered different techniques like denoising algorithms, wavelet based denoising filter, and neighbor prediction.

Image Forgery Detection and Localization

2021 ◽  
pp. 176-182
Author(s):  
Aditi Shedge ◽  
Shaily Shah ◽  
Shubham Pandey ◽  
Mansi Pandey ◽  
Rupali Satpute
Keyword(s):  
Human Brain ◽  
Sources Of Information ◽  
Forgery Detection ◽  
Image Splicing ◽  
Tampering Detection ◽  
Image Forgery ◽  
Detection Techniques ◽  
Image Forgery Detection ◽  
Crime Investigation ◽  
Detection And Localization

A human brain responds at a much faster rate to images and the information it contains. An image is considered as proof of past events that have occurred, but in today's world where editing tools are made available so easily tampering of images and hiding the original content has become too mainstream. The identification of these tampered images is very important as images are considered as vital sources of information in crime investigation and in various other fields. The image forgery detection techniques check the credibility of the image. Various research has been carried out in dealing with image forgery and tampering detection techniques, this paper highlights various the type of forgery and how they can be detected using various techniques. The fusion of various algorithms so that a complete reliable type of algorithm can be developed to deal mainly with copy-move and image splicing forgery. The copy-move and image splicing method are main focus of this paper.

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
Sign in / Sign up

Export Citation Format

Share Document