Digital video tampering detection

In this digital day and age, we are becoming increasingly dependent on multimedia content, especially digital images and videos, to provide a reliable proof of occurrence of events. However, the availability of several sophisticated yet easy-to-use content editing software has led to great concern regarding the trustworthiness of such content. Consequently, over the past few years, visual media forensics has emerged as an indispensable research field, which basically deals with development of tools and techniques that help determine whether or not the digital content under consideration is authentic, i.e., an actual, unaltered representation of reality. Over the last two decades, this research field has demonstrated tremendous growth and innovation. This paper presents a comprehensive and scrutinizing bibliography addressing the published literature in the field of passive-blind video content authentication, with primary focus on forgery/tamper detection, video re-capture and phylogeny detection, and video anti-forensics and counter anti-forensics. Moreover, the paper intimately analyzes the research gaps found in the literature, provides worthy insight into the areas, where the contemporary research is lacking, and suggests certain courses of action that could assist developers and future researchers explore new avenues in the domain of video forensics. Our objective is to provide an overview suitable for both the researchers and practitioners already working in the field of digital video forensics, and for those researchers and general enthusiasts who are new to this field and are not yet completely equipped to assimilate the detailed and complicated technical aspects of video forensics.

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Video content authentication techniques: a comprehensive survey

Passive forensics in image and video using noise features

In this paper, we present a passive blind scheme consisting of two different algorithms to detect frame and region duplication forgeries in videos. We have examined the video frame duplication forgery in three different forms such as duplication of a sequence of consecutive video frames at long continue running position, duplication of many such sequences having different lengths at many different locations and duplication from other videos having different and same dimensions which can raise a serious problem in the real world scenario. The algorithm I of proposed scheme has detected these three different forms of copy-moved frame duplication forgery in videos by obtaining the mean features of each video frame for evaluating the correlation between sequences. In this paper, we have also analysed forged regular and irregular region within same frame at different locations and from other frame to one or more sequences of consecutive frames of the same video at same locations. It creates a challenge to detect this copy-move forgery due to slightly change in pixel intensity values in the duplicated region and providing high correlation as authentic region. The algorithm II of proposed scheme has detected these copy-moved region duplication forgeries in videos by locating the position of error with threshold process in order to calculate the similarities between regions of two frames or within affected frame. In this paper, the experimental results show the higher detection accuracy and execution time efficiency of proposed scheme than the latest algorithms with satisfactory performance.

Video frame and region duplication forgery detection based on correlation coefficient and coefficient of variation

With the extensive use of multimedia on internet and easy approachability of powerful image and video editing software, doctored visual contents have been extensively appearing in our electronic-mail in-boxes, Whatsapp, Facebook or any other social media. Recently, attempting blind tampering in visual contents have been progressively adopted. This paper presents a collaborative survey on detection of such attempts. Our aim is to establish an effective path, for researchers working in the field of image and video forensics, to unfold new aspects of forgery. This paper will avail the comprehensive study that will assist the researchers to go through the various challenges encountered in the previous work. The focus of this paper is to review the splicing and copy–move forgery detection methods in images as well as inter and intra-frame forgery challenges in videos, highlighting the commonly used datasets and hence assisting new researchers to work on with. The efficacy of the paper is that such collaborative survey under one umbrella is not available yet.

Image and Video Forensics: A Critical Survey

In this paper, we propose novel algorithmic models based on information fusion and feature transformation in cross-modal subspace for different types of residue features extracted from several intra-frame and inter-frame pixel sub-blocks in video sequences for detecting digital video tampering or forgery. An evaluation of proposed residue features -- the noise residue features and the quantization features, their transformation in cross-modal subspace, and their multimodal fusion, for emulated copy-move tamper scenario shows a significant improvement in tamper detection accuracy as compared to single mode features without transformation in cross-modal subspace.

Blind Video Tamper Detection Based on Fusion of Source Features

In this paper, we propose a novel multimedia sensor fusion approach based on heterogeneous sensors for biometric access control applications. The proposed fusion technique uses multiple acoustic and visual sensors for extracting dominant biometric cues, and combines them with nondominant cues. The performance evaluation of the proposed fusion protocol and a novel cascaded authentication approach using a 3D stereovision database shows a significant improvement in performance and robustness.

Identity retrieval in biometric access control systems using multimedia fusion

In this paper, we propose a novel formulation involving fusion of noise and quantization residue features for detecting tampering or forgery in video sequences. We reiterate the importance of feature selection techniques in conjunction with fusion to enhance the tamper detection accuracy. We examine three different feature selection techniques, the independent component analysis (ICA), fisher linear discriminant analysis (FLD) and canonical correlation analysis (CCA) for achieving a more discriminate subspace for extracting tamper signatures from quantization and noise residue features. The evaluation of proposed residue features, the feature selection techniques and their subsequent fusion for copy-move tampering emulated on low bandwidth Internet video sequences, show a significant improvement in tamper detection accuracy with fusion formulation.

Julian Goodwin

Papers

Blind Video Tamper Detection Based on Fusion of Source Features

Identity retrieval in biometric access control systems using multimedia fusion

Digital Image Tamper Detection Based on Multimodal Fusion of Residue Features