Surveillance Video Authentication Using Universal Image Quality Index of Temporal Average

Fadl, Sondos M.; Han, Qi; Li, Qiong

doi:10.1007/978-3-030-11389-6_25

Cited by 6 publications

(2 citation statements)

References 23 publications

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“…The outcomes of each of the three assessment criteria were compared to the procedures in [108]- [111] and proved the best outcomes in terms of Precision, Recall, and F1 Score values. Moreover, the proposed technique has the shortest execution time compared to the previous techniques mentioned in [108]- [111] because it compares the temporal averages of nonoverlapping subsequence frames rather than examining each frame individually [112].…”

Section: Temporal Domainmentioning

confidence: 99%

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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Section: Temporal Domainmentioning

confidence: 99%

Video Forgery Detection for Surveillance Cameras: A Review

Tayfor,

Rashid,

Qader

et al. 2023

Preprint

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“…Inter-frame video forgery detection majorly uses one of the following features/principles: (a) Image-based features, (b) Video compression features and (c) Deep learning based techniques. Techniques adopting image-based features (Liu and Huang 2017;Fadl et al 2019), operate by extracting image features from all the decompressed frames in a test video, and then exploits the inherent correlation between adjacent frames of the video based on those features. Video compression based features (Yu et al 2016;Aghamaleki and Behrad 2016; are nothing other than video compression footprints, generated during video encoding and decoding process, utilize for forgery detection.…”

Section: Introductionmentioning

confidence: 99%

Object-based forgery detection in surveillance video using capsule network

Bakas

Naskar

Nappi

et al. 2021

J Ambient Intell Human Comput

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Due to the rapid growth of digital multimedia technology, the importance of multimedia data such as digital images/videos is increasing rapidly in diverse domains. This includes surveillance CCTV footages, which act as primary evidences in many court cases, including highly sensitive contexts. Today, with the wide availability of low-cost image/video manipulating software, digital images/videos have become highly vulnerable to manipulation/modification attacks; one such attack is the class of object-based forgery in surveillance videos. In this paper, we propose a Capsule Network based digital forensic technique for detection of object-based forgery in surveillance videos. In the proposed technique, we use motion residual, computed from every video frame, to extract intra-and inter-frame inherent statistical characteristics of the video sequence, as the input of capsule network. Our experimental results indicate that the proposed technique achieves significant performance in terms of authentic, double compressed and forged frame detection, irrespective of the group of pictures length and degree of compression in videos.

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