An Inter-Frame Forgery Detection Algorithm for Surveillance Video

Li, Qian; Wang, Rangding; Xu, Dawen

doi:10.3390/info9120301

Cited by 20 publications

(7 citation statements)

References 28 publications

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“…The proposed work and the related works are also tested on DERF data set alone after removing YouTube videos from the simulated data set and the results are published in Table 6. Although the proposed work is leading, other works [13, 16, 17, 18] are also comparatively good. So we conclude that our method is more robust to post‐processing attacks than other inter‐frame forgery detection methodologies and also yields good results for videos with no post‐processing attack.…”

Section: Resultsmentioning

confidence: 88%

“…As most of the public data sets available are meant for intra-frame regional forgery, we simulated an inter-frame forged data set from publicly available videos. The frame-level forgery was generated by inserting/deleting/duplicating a random sequence of frames as [12][13][14][15][16][17][18] did. The original videos used for tampering creation were mostly taken from a public data set DERF (https://media.xiph.org/ video/derf/) and some of them from YouTube.…”

Section: Data Setmentioning

confidence: 99%

“…Moreover, this method fails for a carefully prepared copy–move forgery with largely static scenes. Li et al [17] extracted the two‐dimensional (2D) phase congruency of each frame and calculated the correlation among the adjacent frames. Also, the authors used the k ‐means clustering algorithm for categorising normal and abnormal points.…”

Section: Related Workmentioning

confidence: 99%

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Inter‐frame forgery detection and localisation in videos using earth mover's distance metric

Selvaraj¹,

Muneeswaran²

2020

IET image process

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Video forensics is one of the hot topics in multimedia forensics. Nowadays, spreading fake videos across the internet is becoming a profession mainly in politics and entertainment. In this study, a novel two-stage inter-frame video forgery detection technique is proposed. The first stage analyses spatio-temporal feature flow consistency to detect suspicious tamper points. Identifying the type of forgery and validating the recovered video are done in the second stage. Earth mover's distance is used as a similarity metric in both stages. The authors concentrate on a robust inter-frame forgery detection approach which can be applied for any challenging video. Compression at a higher rate, noise addition, and filtering are the anti-forensic tricks used by forgers to fool forensic techniques. However, most of the literature in video forgery detection has handled these issues as postprocessing attacks and reported lesser accuracies for it. Hence the authors propose a robust and efficient forgery detection technique capable of identifying all kinds of inter-frame forgeries in videos. Experimental evaluation of the public video data set shows that the proposed approach outperforms existing approaches with an improved rate of robustness. 10 IET Image Process.

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Section: Resultsmentioning

confidence: 88%

Section: Data Setmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Inter‐frame forgery detection and localisation in videos using earth mover's distance metric

Selvaraj¹,

Muneeswaran²

2020

IET image process

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“…Bakas and Naskar [ 20 ] detected frame insertion, duplication and deletion using a 3D convolutional neural network that used another CNN layer, which was used for temporal information extraction from videos. Li et al [ 21 ] extracted features and localized abnormal points. In the extracting feature phase, the 2-D phase congruency of each frame was detected, since it was a good image characteristic.…”

Section: Related Workmentioning

confidence: 99%

Detecting and Locating Passive Video Forgery Based on Low Computational Complexity Third-Order Tensor Representation

2021

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Great attention is paid to detecting video forgeries nowadays, especially with the widespread sharing of videos over social media and websites. Many video editing software programs are available and perform well in tampering with video contents or even creating fake videos. Forgery affects video integrity and authenticity and has serious implications. For example, digital videos for security and surveillance purposes are used as evidence in courts. In this paper, a newly developed passive video forgery scheme is introduced and discussed. The developed scheme is based on representing highly correlated video data with a low computational complexity third-order tensor tube-fiber mode. An arbitrary number of core tensors is selected to detect and locate two serious types of forgeries which are: insertion and deletion. These tensor data are orthogonally transformed to achieve more data reductions and to provide good features to trace forgery along the whole video. Experimental results and comparisons show the superiority of the proposed scheme with a precision value of up to 99% in detecting and locating both types of attacks for static as well as dynamic videos, quick-moving foreground items (single or multiple), zooming in and zooming out datasets which are rarely tested by previous works. Moreover, the proposed scheme offers a reduction in time and a linear computational complexity. Based on the used computer’s configurations, an average time of 35 s. is needed to detect and locate 40 forged frames out of 300 frames.

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“…Here the accuracy value for our proposed approach is determined by using following equation. (26) (26) Here,the number of forged videos that are normally designated as forged is represented as TP, FP indicates that the number of real videos that are incorrectly recognised as forged, the number of real videos that are correctly identified as real is represented as TN, and the number of videos that are mistakenly identified as authentic is indicated as FN [42].…”

Section: A Evaluation Standardsmentioning

confidence: 99%

DNN Based Moth Search Optimization for Video Forgery Detection

Raveendra¹,

Nagireddy²

2019

IJEAT

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Recently, video tampering process becomes easier due to the rapid advancements in user-friendly editing software and multimedia technology (e.g., Mokey by Imagineer Systems, and Photoshop and Premiere by Adobe). This technologies may highly tamper the original videos, so that the audience gets mislead. Nowadays, MPEG-4 codec is included in a large proportions of video cameras and surveillance systems. Therefore the double compression detection process included as an initial step in the video forensic is receiving a high significance. In this paper, the double compression artifacts is detected by adopting the Markov based features, which identifies the interpolated original videos. The double compressed frames are then segmented by introducing an SLIC super pixel segmentation technique. Here, the feature extraction is performed by applying the scale information that is obtained from the multi-scale Gabor filters. The features of this Gabor scale accurately extract the structural features and also reduce too much of redundancy. This extracted features are then provided to DNN (deep neural network) for forgery detection. In this video forensic process, DNN classifier is included for forgery detection. The CNN classifier is included in various existing forgery detection techniques. But, in our work we include DNN because it contains number of hidden layers which provide accurate results for this forgery detection process. To improve the DNN performance, Moth Search Optimization (MSO) algorithm is introduced in this forgery detection technique. Every nook and corner of this world we can able to find the surveillance cameras for security purpose. But, some fraudsters perform forgeries in this recorded videos for their own benefits. To identify this, a lot of forgery detection techniques are coming into existence. So in this work, we introduce the DNN based MSO to perform the forgery detection in videos.This implementation is processed in python simulation platform. The parametric evaluations are taken in terms of F1- Score, average accuracy, Precision, Recall and. Experimental results will provide improved performance in video forgery detection.

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An Inter-Frame Forgery Detection Algorithm for Surveillance Video

Cited by 20 publications

References 28 publications

Inter‐frame forgery detection and localisation in videos using earth mover's distance metric

Inter‐frame forgery detection and localisation in videos using earth mover's distance metric

Detecting and Locating Passive Video Forgery Based on Low Computational Complexity Third-Order Tensor Representation

DNN Based Moth Search Optimization for Video Forgery Detection

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