Chroma key background detection for digital video using statistical correlation of blurring artifact

Bagiwa, Mustapha Aminu; Wahab, Ainuddin Wahid Abdul; Idris, Mohd Yamani Idna; Khan, Suleman; Choo, Kim‐Kwang Raymond

doi:10.1016/j.diin.2016.09.001

Cited by 22 publications

(12 citation statements)

References 17 publications

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“…These works tackle two types of object-based forgery: object insertion video forgery and object removal video forgery. The following works are proposed to detect object insertion video forgery [26]- [32]. Some approaches use correlation between blurring features [26], or edge features [27], to detect blue screen compositing.…”

Section: Related Workmentioning

confidence: 99%

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Sequential and Patch Analyses for Object Removal Video Forgery Detection and Localization

Aloraini

Sharifzadeh

Schonfeld

2021

IEEE Trans. Circuits Syst. Video Technol.

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In recent years, video surveillance has become essential for security applications used to monitor many organizations and locations, and it is therefore important to ensure the reliability of these surveillance videos. Unfortunately, surveillance videos can be forged with little effort by deleting an object from a video scene while leaving no visible traces. A fundamental challenge in video security is to determine whether or not an object has been removed from a video. This task is particularly challenging due to the lack of ground truth bases that can be used to verify the originality and integrity of video contents. In this paper, we propose a novel approach based on sequential and patch analyses to detect object removal forgery and to localize forged regions in videos. Sequential analysis is performed by modeling video sequences as stochastic processes, where changes in the parameters of these processes are used to detect a video forgery. Patch analysis is performed by modeling video sequences as a mixture model of normal and anomalous patches, with the aim to separate these patches by identifying the distribution of each patch. We localize forged regions by visualizing the movement of removed objects using anomalous patches. We conduct our experiments at both pixel and video levels to determine the effectiveness and efficiency of our approach to detection of video forgery. The experimental results show that our approach achieves excellent detection performance with low-computational complexity and leads to robust results for compressed and lowresolution videos.

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Section: Related Workmentioning

confidence: 99%

“…The following works are proposed to detect object insertion video forgery [26]- [32]. Some approaches use correlation between blurring features [26], or edge features [27], to detect blue screen compositing. The forgery is detected by examining changes in correlation patterns between these features.…”

Section: Related Workmentioning

confidence: 99%

Sequential and Patch Analyses for Object Removal Video Forgery Detection and Localization

Aloraini

Sharifzadeh

Schonfeld

2021

IEEE Trans. Circuits Syst. Video Technol.

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“…Richao et al [8] worked on statistical features. Bagiwa et al [16] explored statistical correlation of blurring artifact. Singh and Aggarwal [17] used pixels correlation, noise inconsistency and discrete fractional fourier transformation.…”

Section: A Related Workmentioning

confidence: 99%

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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“…Su et al [25] look for different correlation patterns, in the green component, around the edges of the spliced area. In [26], instead, inconsistencies of statistical features of quantized DCT coefficients are sought, while in [27] blurring artifacts are considered.…”

Section: Related Workmentioning

confidence: 99%

Autoencoder with recurrent neural networks for video forgery detection

D'Avino¹,

Cozzolino²,

Poggi³

et al. 2017

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Video forgery detection is becoming an important issue in recent years, because modern editing software provide powerful and easy-to-use tools to manipulate videos. In this paper we propose to perform detection by means of deep learning, with an architecture based on autoencoders and recurrent neural networks. A training phase on a few pristine frames allows the autoencoder to learn an intrinsic model of the source. Then, forged material is singled out as anomalous, as it does not fit the learned model, and is encoded with a large reconstruction error. Recursive networks, implemented with the long short-term memory model, are used to exploit temporal dependencies. Preliminary results on forged videos show the potential of this approach.

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Chroma key background detection for digital video using statistical correlation of blurring artifact

Cited by 22 publications

References 17 publications

Sequential and Patch Analyses for Object Removal Video Forgery Detection and Localization

Sequential and Patch Analyses for Object Removal Video Forgery Detection and Localization

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

Autoencoder with recurrent neural networks for video forgery detection

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