Video content authentication techniques: a comprehensive survey

Singh, Raahat Devender; Aggarwal, Naveen

doi:10.1007/s00530-017-0538-9

Cited by 87 publications

(38 citation statements)

References 126 publications

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“…There are already a number of recent surveys which review tampering detection methods [14,15,16,17,18]. Tampering detection methods are broadly categorised as active or passive, with more focus on passive tampering detection methods.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

A review of digital video tampering: From simple editing to full synthesis

Johnston

Elyan

2019

Digital Investigation

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Video tampering methods have witnessed considerable progress in recent years. This is partly due to the rapid development of advanced deep learning methods, and also due to the large volume of video footage that is now in the public domain. Historically, convincing video tampering has been too labour intensive to achieve on a large scale. However, recent developments in deep learning-based methods have made it possible not only to produce convincing forged video but also to fully synthesize video content. Such advancements provide new means to improve visual content itself, but at the same time, they raise new challenges for state-of-the-art tampering detection methods. Video tampering detection has been an active field of research for some time, with periodic reviews of the subject. However, little attention has been paid to video tampering techniques themselves. This paper provides an objective and in-depth examination of current techniques related to digital video manipulation. We thoroughly examine their development, and show how current evaluation techniques provide opportunities for the advancement of video tampering detection. A critical and extensive review of photo-realistic video synthesis is provided with emphasis on deep learning-based methods. Existing tampered video datasets are also qualitatively reviewed and critically discussed. Finally, conclusions are drawn upon an exhaustive and thorough review of tampering methods with

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Section: Accepted Manuscriptmentioning

confidence: 99%

A review of digital video tampering: From simple editing to full synthesis

Johnston

Elyan

2019

Digital Investigation

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“…? Most of related digital forensic studies focused on multimedia tamper detection, source camera identification, or hidden data recovery [1][2][3]. The previous studies for the multimedia tamper detection can be classified into two groups: active intrusive approach and passive blind approach.…”

Section: Corrupted Partially Recoveredmentioning

confidence: 99%

“…While many studies for detecting the forged document, identifying the source digital device of multimedia, and recovering deleted files have been done [1][2][3], a little progress has been done for creation-time reconstruction of files recovered without the metadata of creation-time. In this article, we propose a creation order reconstruction method for recovered files upon the FAT32 file system with Windows operation systems.…”

Section: Introductionmentioning

confidence: 99%

Extraction of Creation-Time for Recovered Files on Windows FAT32 File System

2019

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In this article, we propose a creation order reconstruction method of deleted files for the FAT32 file system with Windows operating systems. Creation order of files is established using a correlation between storage locations of the files and their directory entry locations. This method can be utilized to derive the creation-time bound of files recovered without the creation-time information. In this article, we first examine the file allocation behavior of Windows FAT32 file system. Next, based on the examined behavior, we propose a novel method that finds the creation order of deleted files after being recovered without the creation-time information. Due to complex behaviors of Windows FAT32 file system, the method may find multiple creation orders although the actual creation order is unique. In experiments with a commercial device, we confirm that the actual creation order of each recovered file belongs to one of the creation orders found by the method.

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“…For detecting the manipulated video with minimum errors a DNN was employed,although in [27] it was shown that this approach could not be transmitted to new video manipulation approaches. Singh and Aggarwal [28] after reviewing the video content authentication methods, noticed that for the genuine doctored videos there is no reliable database available. For image rebroadcast detection ahuge dataset was produced in [29].…”

Section: Related Workmentioning

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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Video content authentication techniques: a comprehensive survey

Cited by 87 publications

References 126 publications

A review of digital video tampering: From simple editing to full synthesis

A review of digital video tampering: From simple editing to full synthesis

Extraction of Creation-Time for Recovered Files on Windows FAT32 File System

DNN Based Moth Search Optimization for Video Forgery Detection

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