Digital Video Tampering Detection and Localization: Review, Representations, Challenges and Algorithm

Akhtar, Naheed; Saddique, Mubbashar; Asghar, Khurshid; Bajwa, Usama Ijaz; Hussain, Muhammad; Habib, Zulfiqar

doi:10.3390/math10020168

Cited by 29 publications

(22 citation statements)

References 152 publications

(131 reference statements)

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“… This work did not cover video forensics challenges, and several key works of value, such as anti-forensics and video forgery problems, were neglected in this study. [ 5 ] 2022 Author focus on inter-frame and intra-frame detection techniques. In this survey, active approaches, deepfake, novel trends, and forensics tools were not introduced.…”

Section: Related Workmentioning

confidence: 99%

“…The most extensively used video encoders today, such as H.264/AVC, have an adaptive GOP structure, which allows the GOP size to expand by up to 250 frames when the video content changes. Many of the approaches listed above work well for GOPs with a fixed structure size, and a few of them are good for identifying forgeries in films with changing GOP structure, but they cannot detect the deletion of an entire GOP or numerous GOPs [ 5 ]. Noise …”

Section: Video Forensics Overviewmentioning

confidence: 99%

“…They can’t look at many forgeries in the video at the same time, either. Digital video forgeries such as upscale crop, splicing, frame replication, and frame mirroring have yet to be properly researched [ 5 ]. Video frame count …”

Section: Video Forensics Overviewmentioning

confidence: 99%

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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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Section: Related Workmentioning

confidence: 99%

Section: Video Forensics Overviewmentioning

confidence: 99%

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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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“…Fragile watermarking has a few specific applications, such as checking for tampering or changes to works-in-progress. Fragile watermarking has a few specific applications, such as checking for tampering or changes to works-in-progress, even if it was just due to noise (Akhtar et al, 2022).…”

Section: Fragile Watermarkingmentioning

confidence: 99%

The Most Common Characteristics of Fragile Video Watermarking

Hassan

Rahma

2023

ARO

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The progress of network and multimedia technologies has been phenomenal during the previous two decades. Unauthorized users will be able to copy, retransmit, modify reproduction, and upload the contents more easily as a result of this innovation. Malicious attackers are quite concerned about the development and widespread use of digital video. Digital watermarking technology gives solutions to the aforementioned problems. Watermarking methods can alleviate these issues by embedding a secret watermark in the original host data, allowing the genuine user or file owner to identify any manipulation. In this study, lots of papers have been analyzed and studied carefully, in the period 2011–2022. The historical basis of the subject should not be forgotten so studying old research will give a clear idea of the topic. To aid future researchers in this subject, we give a review of fragile watermarking approaches and some related papers presented in recent years. This paper presents a comparison of many relevant works in this field based on some of the outcomes and improvements gained in these studies, which focuses on the common characteristics that increase the effect of watermarking techniques such as invisibility, tamper detection, recovery, and security

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“…In a broader sense, detection involves the process of discovery. Video images [1], acoustic signals [2], radio signals [3], or even smell [4] can be used for detection and localization. This review focuses on the methods of detecting and localizing sound sources-that is, the acoustic signal.…”

Section: Introductionmentioning

confidence: 99%

A Survey of Sound Source Localization and Detection Methods and Their Applications

Jekateryńczuk,

Piotrowski

2023

Sensors

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This study is a survey of sound source localization and detection methods. The study provides a detailed classification of the methods used in the fields of science mentioned above. It classifies sound source localization systems based on criteria found in the literature. Moreover, an analysis of classic methods based on the propagation model and methods based on machine learning and deep learning techniques has been carried out. Attention has been paid to providing the most detailed information on the possibility of using physical phenomena, mathematical relationships, and artificial intelligence to determine sound source localization. Additionally, the article underscores the significance of these methods within both military and civil contexts. The study culminates with a discussion of forthcoming trends in the realms of acoustic detection and localization. The primary objective of this research is to serve as a valuable resource for selecting the most suitable approach within this domain.

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Digital Video Tampering Detection and Localization: Review, Representations, Challenges and Algorithm

Cited by 29 publications

References 152 publications

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

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

The Most Common Characteristics of Fragile Video Watermarking

A Survey of Sound Source Localization and Detection Methods and Their Applications

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