Development of a video tampering dataset for forensic investigation.

In the current times the level of video forgery has increased on the internet with the increase in the role of malware that has made it possible for any user to upload, download and share objects online including audio, images, and video. Specifically, Video Editor and Adobe Photoshop are some of the multimedia software and tools that are used to edit or tamper medial files. Added to this, manipulation of video sequence in a way that objects within the frame are inserted or deleted are among the common malicious video forgery operations. In the present study, literature concerning video forgery is reviewed primarily those that use several video forgery detection in the form of passive blind method on three types of forgery namely cloning forgery, source cameral identification and splice forgery. The present study employed a video authentication method that detects and determines both region duplication and frame duplication in terms of video forgery, and locates factors that impact video forgery. In the present study, video processing into sub-blocks and the moments geometric features for every macro-block were extracted. This led to the enhanced accuracy of detection. Moreover, the optimum sorting algorithm led to minimized computational time taking account number of blocks and features numbers into consideration.

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Detection of video forgery: a review of literature

A novel genetic-concept-based algorithm is proposed for fragile audio watermarking to reduce the distortion of least significant bits substitution and consequently improve the peak signal-to-noise ratio (PSNR) and increase the payload of results. The result of testing shows that in comparison with ordinary substitution techniques and other presented techniques, the payload is considerably increased and PSNR (as an indicator of imperceptibility) is noticeably raised.

Genetic algorithm for fragile audio watermarking

Video authentication aims to ensure the trustworthiness of the video by verifying the integrity and source of video data. It has gained much attention in the recent years. In this paper we present the issues in the designing of a video authentication system. These issues include the classification of tampering attacks, levels of tampering attack and robustness. Further we present the categorization of existing video authentication techniques with their shortcomings. Moreover we have also given the challenging scenarios in which the video authentication would be a critical task.

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Video Authentication: Issues and Challenges

This paper proposed a new detection method to detect a video copy-move forgery detection with a significant improvement in terms of its accuracy based on the optical flow inconsistencies that considers image motion for detection clone an object movement based on displacement paths. Finally, the displacement paths are compared between each other using Dynamic Time Warping (DTW) matching algorithm to detect the clone object. The experimental results show that the accuracy of this method is higher than the preceding relevant study and very encouraging.

Detection clone an object movement using an optical flow approach

With the innovations and development in sophisticated video editing technology and a wide spread of video information and services in our society, it is becoming increasingly significant to assure the trustworthiness of video information. Therefore in surveillance, medical and various other fields, video contents must be protected against attempt to manipulate them. Such malicious alterations could affect the decisions based on these videos. A lot of techniques are proposed by various researchers in the literature that assure the authenticity of video information in their own way. In this paper we present a brief survey on video authentication techniques with their classification. These authentication techniques are broadly classified into four categories: digital signature based techniques, watermark based techniques, intelligent techniques and other techniques. Furthermore we give the shortcomings of different categories of video authentication techniques in brief.

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Video Authentication- An Overview

In law enforcement applications such as surveillance and forensics, video is often presented as evidence. It is therefore of paramount importance to establish the authenticity and reliability of the video data. This paper presents an intelligent video authentication algorithm which integrates learning based Support Vector Machine classification with Singular Value Decomposition watermarking. During video capture and storage, intrinsic local correlation information is extracted from the frames and embedded in the frames at local levels. Tamper detection and classification is performed using the inherent video information and embedded correlation information. The proposed algorithm is independent of the choice of watermark and does not require any key to store. Further, it is robust to global tampering such as frame addition and removal, local attacks such as object alteration and can differentiate between acceptable operations and malicious tampering. Experiments are performed on an extensive database which contains non-tampered videos and videos with several types of tampering. The results show that the proposed algorithm outperforms existing video authentication algorithms.

Integrating SVM classification with SVD watermarking for intelligent video authentication

With the innovations and development in sophisticated video editing technology, it is becoming increasingly significant to assure the trustworthiness of video information. Today digital videos are also increasingly transmitted over non secure channels such as Internet. Therefore in surveillance, medical and various other fields, video contents must be protected against attempt to manipulate them. This paper presents an intelligent video authentication algorithm using support vector machine, which is a non-linear classifier. The proposed algorithm does not require the computation and storage of any secret key or embedding of any watermark. It computes the local information of the difference frames of given video statistically and classifies the video as tampered or non-tampered. It covers both kinds of tampering attacks, spatial and temporal. It uses a database of more than 4000 tampered and non-tampered video frames and gives excellent results with 99.12 classification accuracy.

Saurabh Upadhyay

Papers

Video Authentication: Issues and Challenges

Video Authentication- An Overview

Integrating SVM classification with SVD watermarking for intelligent video authentication

Learning based video authentication using statistical local information

Video Authentication Using Relative Correlation Information and SVM