Video Inter-frame Forgery Detection Approach for Surveillance and Mobile Recorded Videos

Kingra, Staffy; Aggarwal, Naveen; Singh, Raahat Devender

doi:10.11591/ijece.v7i2.pp831-841

Cited by 9 publications

(5 citation statements)

References 28 publications

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“…Table 6 lists the average accuracy value of the proposed models and the set of methods that handled the inter-frame video forgery. [11] 2017 90 [12] 2017 97.5…”

Section: Results Performance Evaluationmentioning

confidence: 99%

“…Presented in this section is a concise review of relevant research to identify the mentioned video forgeries. [11] Proposed forensic technique allowed the detection of inter-frame forgery, in H.264 and Moving Picture Experts Group (MPEG-2) encoded videos. This implemented an objectivity approach for the automated detection of position and the manipulation by using optical flow and the residual gradient.…”

Section: Literature Reviewmentioning

confidence: 99%

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Enhancement Digital Forensic Approach for Inter-Frame Video Forgery Detection Using a Deep Learning Technique

Oraibi

Radhi²

2022

eijs

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The digital world has been witnessing a fast progress in technology, which led to an enormous increase in using digital devices, such as cell phones, laptops, and digital cameras. Thus, photographs and videos function as the primary sources of legal proof in courtrooms concerning any incident or crime. It has become important to prove the trustworthiness of digital multimedia. Inter-frame video forgery one of common types of video manipulation performed in temporal domain. It deals with inter-frame video forgery detection that involves frame deletion, insertion, duplication, and shuffling. Deep Learning (DL) techniques have been proven effective in analysis and processing of visual media. Dealing with video data needs to handle the third dimension (the time dimension), which means extracting temporal features as well as spatial features. The proposed model is built based on the Three Dimension Convolution Neural Network (3D-CNN). Through pre-processing operation that introduced difference frames that pick up the difference in successive adjacent frames, which provide a large quantity of temporal information and lead to enhance the effectiveness of the proposed model. The model achieves high accuracy of 99%.

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“…Table 6 lists the average accuracy value of the proposed models and the set of methods that handled the inter-frame video forgery. [11] 2017 90 [12] 2017 97.5…”

Section: Results Performance Evaluationmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Enhancement Digital Forensic Approach for Inter-Frame Video Forgery Detection Using a Deep Learning Technique

Oraibi

Radhi²

2022

eijs

View full text Add to dashboard Cite

show abstract

“…Using the spike count regardless of the number of frames in the video, the proposed technique automatically detects video manipulation. This method achieved an accuracy of 83% [101].…”

Section: Residual Gradient and Optical Flow Gradientmentioning

confidence: 89%

Video Forgery Detection for Surveillance Cameras: A Review

Tayfor,

Rashid,

Qader

et al. 2023

Preprint

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Although video recording is now a standard feature in smartphones and other digital devices, digital records are affordable and straightforward to take and distribute on social media. Furthermore, digital recordings have recently become an essential part of our daily lives, ranging from personal experiences to surveillance footage that may now be utilised as evidence of proof. Thus, it is essential to create detection algorithms for this kind of captured video because the number of high-standard counterfeit videos on social networks and other media is increasing exponentially. Evaluating the integrity of surveillance videos is crucial with the advanced equipment of surveillance systems. As a result, the rapid increase in surveillance cameras has made progress in physical security, including in building homes, stores, governmental buildings, agencies, etc. Moreover, surveillance videos have lately been extensively used as significant evidence in the courts. The evidence from the video can be crucial for a court of justice and investigators to comprehend the incidents as they happened. Unfortunately, due to the widespread of video editing programs, the video forgery/ tampering process has become incredibly simple. Consequently, the courts refuse to accept these videos as evidence because there is some margin of doubt about their authenticity and integrity. Therefore, to avoid such a situation in the future, more efforts are needed in video forensics. For this reason, this paper reviews primarily all the techniques and methods proposed so far to find out forgery in surveillance recordings and how the authenticity of such videos is proven.

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“… SYSU-OBJFORG Dataset The precision is up to 83.37% The problem of selecting the tampered frames from the double compressed equivalents remained. [ 92 ] 2017 Prediction Residual and Optical Flow Group 1 was captured in H.264 on a Sony XPERIA Z2 from security cameras dubbed ‘Presto’ and ‘Hikvision.’ Group 2 was initially collected in H.264 utilizing a SONY XPERIA Z2 from surveillance cameras named ‘Presto’ and ‘Hikvision.’ The accuracy reaches up to 86% When employing low-quality movies, the proposed technique’s efficiency is minimal. [ 206 ] 2017 Frame Duplication /Mirroring Detection Virtual Dub, an open-source video editor, was used to construct a collection of falsified videos for the tests.…”

Section: Video Forensics Overviewmentioning

confidence: 99%

A comprehensive taxonomy on multimedia video forgery detection techniques: challenges and novel trends

El-Shafai,

Fouda,

El-Rabaie

et al. 2023

Multimed Tools Appl

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Thousands of videos are posted on websites and social media every day, including Twitter, Facebook, WhatsApp, Instagram, and YouTube. Newspapers, law enforcement publications, criminal investigations, surveillance systems, Banking, the museum, the military, imaging in medicine, insurance claims, and consumer photography are just a few examples of places where important visual data may be obtained. Thus, the emergence of powerful processing tools that can be easily made available online poses a huge threat to the authenticity of videos. Therefore, it’s vital to distinguish between true and fake data. Digital video forgery detection techniques are used to validate and check the realness of digital video content. Deep learning algorithms lately sparked a lot of interest in the field of digital forensics, such as Recurrent Neural Networks (RNN), Deep Convolutional Neural Networks (DCNN), and Adaptive Neural Networks (ANN). In this paper, we give a soft taxonomy as well as a thorough overview of recent research on multimedia falsification detection systems. First, the basic knowledge needed to comprehend video forgery is provided. Then, a summary of active and passive video manipulation detection approaches is provided. Anti-forensics, compression video methods, datasets required for video forensics, and challenges of video detection approaches are also addressed. Following that, we presented an overview of deepfake, and the datasets required for detection were also provided. Also, helpful software packages and forensics tools for video detection are covered. In addition, this paper provides an overview of video analysis tools that are used in video forensic applications. Finally, we highlight research difficulties as well as interesting research avenues. In short, this survey provides detailed information and a broader investigation to extract data and detect fraud video contents under one umbrella.

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Video Inter-frame Forgery Detection Approach for Surveillance and Mobile Recorded Videos

Cited by 9 publications

References 28 publications

Enhancement Digital Forensic Approach for Inter-Frame Video Forgery Detection Using a Deep Learning Technique

Enhancement Digital Forensic Approach for Inter-Frame Video Forgery Detection Using a Deep Learning Technique

Video Forgery Detection for Surveillance Cameras: A Review

A comprehensive taxonomy on multimedia video forgery detection techniques: challenges and novel trends

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