A Review on Video/Image Authentication and Temper Detection Techniques

Parmar, Zarna; Upadhyay, Saurabh

doi:10.5120/10506-5281

Cited by 4 publications

(4 citation statements)

References 8 publications

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“…The following methods are utilized to incorporate the watermark with the original video, as illustrated in Fig. 25 by the authors of [141]:…”

Section: -Video Watermarking In Spatial Domainmentioning

confidence: 99%

“…Furthermore, several transformation watermarking techniques combine two or more transformations with improving efficiency by combining the advantages of different transformations. For example, the frequency-domain video watermark system follows the same stages as the spatial domain scheme [141], except that the watermark is encoded and decoded to and from the wavelet block (HL and LH) bands. Watermarks are used in the HL and LH bands for two reasons:…”

Section: -Video Watermarking In Frequency Domainmentioning

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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“…The following methods are utilized to incorporate the watermark with the original video, as illustrated in Fig. 25 by the authors of [141]:…”

Section: -Video Watermarking In Spatial Domainmentioning

confidence: 99%

Section: -Video Watermarking In Frequency Domainmentioning

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

View full text Add to dashboard Cite

show abstract

“…On one hand, video editing is a very useful and important tool for manipulating video scenes in film industry. On the other hand, it enables to forge video contents to distort the evidences for a court and propaganda on social, print and electronic media Therefore, authenticity of a video is a key issue when it is presented in a court as a prof of a crime [12].…”

Section: Introductionmentioning

confidence: 99%

Robust Video Content Authentication using Video Binary Pattern and Extreme Learning Machine

Sadddique¹,

Asghar²,

Mehmood³

et al. 2019

IJACSA

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Recently, due to easy accessibility of smartphones, digital cameras and other video recording devices, a radical enhancement has been experienced in the field of digital video technology. Digital videos have become very vital in court of law and media (print, electronic and social). On the other hand, a widely-spread availability of Video Editing Tools (VETs) have made video tampering very easy. Detection of this tampering is very important, because it may affect the understanding and interpretation of video contents. Existing techniques used for detection of forgery in video contents can be broadly categorized into active and passive. In this research a passive technique for video tampering detection in spatial domain is proposed. The technique comprises of two phases: 1) Extraction of features with proposed Video Binary Pattern (VBP) descriptor, and 2) Extreme Learning Machine (ELM) based classification. Experimental results on different datasets reveal that the proposed technique achieved accuracy 98.47%.

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“…Temper detection [22], copyright control [4], owner identification [6], broadcast monitoring [11], etc., are some of the interesting examples of watermarking applications.…”

Section: Introductionmentioning

confidence: 99%

Protection of medical images and patient related information in healthcare: Using an intelligent and reversible watermarking technique

Arsalan

Qureshi

Khan

et al. 2017

Applied Soft Computing

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Rajarajan, M. (2017). Protection of medical images and patient related information in healthcare: Using an intelligent and reversible watermarking technique. Applied Soft Computing Journal, 51, pp. 1668-179. doi: 10.1016/j.asoc.2016.11.044 This is the accepted version of the paper.This version of the publication may differ from the final published version. Permanent AbstractThis work presents an intelligent technique based on reversible watermarking for protecting patient and medical related information. In the proposed technique 'IRW-Med', the concept of companding function is exploited for reducing embedding distortion, while Integer Wavelet Transform (IWT) is used as an embedding domain for achieving reversibility. Histogram processing is employed to avoid underflow/overflow. In addition, the learning capabilities of Genetic Programming (GP) are exploited for intelligent wavelet coefficient selection. In this context, GP is used to evolve models that not only make an optimal tradeoff between imperceptibility and capacity of the watermark, but also exploit the wavelet coefficient hidden dependencies and information related to the type of sub band. The novelty of the proposed IRW-Med technique lies in its ability to generate a model that can find optimal wavelet coefficients for embedding, and also acts as a companding factor for watermark embedding. The proposed IRW-Med is thus able to embed watermark with low distortion, take out the hidden information, and also recovers the original image. The proposed IRW-Med technique is effective with respect to capacity and imperceptibility and effectiveness is demonstrated through experimental comparisons with existing techniques using standard images as well as a publically available medical image dataset.

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A Review on Video/Image Authentication and Temper Detection Techniques

Cited by 4 publications

References 8 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

Robust Video Content Authentication using Video Binary Pattern and Extreme Learning Machine

Protection of medical images and patient related information in healthcare: Using an intelligent and reversible watermarking technique

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