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

Sadddique, Mubbashar; Asghar, Khurshid; Mehmood, Tariq; Hussain, Muhammad; Habib, Zulfiqar

doi:10.14569/ijacsa.2019.0100833

Cited by 4 publications

(1 citation statement)

References 25 publications

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“…Ren et al [10] presented a technique that identifies duplicate regions using the improved Levenshtein distance, but this experiment cannot be applied to identifying duplicate regions using dynamic backgrounds. To extract noise features using temporal correlation, Sadddique et al [11] suggested a technique that merged Int J Elec & Comp Eng ISSN: 2088-8708  the video binary pattern (VBP) and extreme learning machine (ELM) with the radial basis function (RBF), offering better accuracy and low computation costs. The limitation of this method is that video sequences of extremely short lengths cannot be dealt with effectively.…”

Section: Spatial Domain Forgery Detectionmentioning

confidence: 99%

A survey on passive digital video forgery detection techniques

Jegaveerapandian,

Rani,

Periyaswamy

et al. 2023

IJECE

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Digital media devices such as smartphones, cameras, and notebooks are becoming increasingly popular. Through digital platforms such as Facebook, WhatsApp, Twitter, and others, people share digital images, videos, and audio in large quantities. Especially in a crime scene investigation, digital evidence plays a crucial role in a courtroom. Manipulating video content with high-quality software tools is easier, which helps fabricate video content more efficiently. It is therefore necessary to develop an authenticating method for detecting and verifying manipulated videos. The objective of this paper is to provide a comprehensive review of the passive methods for detecting video forgeries. This survey has the primary goal of studying and analyzing the existing passive techniques for detecting video forgeries. First, an overview of the basic information needed to understand video forgery detection is presented. Later, it provides an in-depth understanding of the techniques used in the spatial, temporal, and spatio-temporal domain analysis of videos, datasets used, and their limitations are reviewed. In the following sections, standard benchmark video forgery datasets and the generalized architecture for passive video forgery detection techniques are discussed in more depth. Finally, identifying loopholes in existing surveys so detecting forged videos much more effectively in the future are discussed.

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Section: Spatial Domain Forgery Detectionmentioning

confidence: 99%

A survey on passive digital video forgery detection techniques

Jegaveerapandian,

Rani,

Periyaswamy

et al. 2023

IJECE

View full text Add to dashboard Cite

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Digital video tampering detection using texture with compressed passive technic

Vistro,

Rehman,

Usman

et al. 2024

WOMEN IN PHYSICS: 7th IUPAP International Conference on Women in Physics

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Classification of Authentic and Tampered Video Using Motion Residual and Parasitic Layers

et al. 2020

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These days, videos can be easily recorded, altered and shared on social and electronic media for deception and false propaganda. However, due to sophisticated nature of the content alteration tools, alterations remain inconspicuous to the naked eye and it is a challenging task to differentiate between authentic and tampered videos. During the process of video tampering the traces of objects, which are removed or modified, remain in the frames of a video. Based on this observation, in this study, a new method is introduced for discriminating authentic and tampered video clips. This method is based on deep model, which consists of three types of layers: motion residual (MR), convolutional neural network (CNN), and parasitic layers. The MR layer highlights the tampering traces by aggregation of frames. The CNN layers encode these tampering traces and are learned using transfer learning. Finally, parasitic layers classify the video clip (VC) as authentic or tampered. The parasitic layers are learned using an efficient learning method based on extreme learning theory; they enhance the performance in terms of efficiency and accuracy. Intensive experiments were performed on various benchmark datasets to validate the performance and the robustness of the method; it achieved 98.89% accuracy. Comparative analysis shows that the proposed method outperforms the state-of-the-art methods.

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

Cited by 4 publications

References 25 publications

A survey on passive digital video forgery detection techniques

A survey on passive digital video forgery detection techniques

Digital video tampering detection using texture with compressed passive technic

Classification of Authentic and Tampered Video Using Motion Residual and Parasitic Layers

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