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

Digital videos are one of the most widespread forms of multimedia in day to day life. These are widely transferred over social networking websites such as Facebook, Instagram, WhatsApp, YouTube, etc. through the Internet. Availability of modern and easy to use editing tools have facilitated the modification of the contents of the digital videos. Therefore, it has become an essential concern for the legitimacy, trustworthiness, and authenticity of these digital videos. Digital video forgery detection aims to identify the manipulations in the video and to check its authenticity. These techniques can be divided into active and passive techniques. In this paper, a comprehensive survey on video forgery detection using passive techniques have been presented. The primary goal of this survey is to study and analyze the existing passive video forgery detection techniques. Firstly, the preliminary information required for understanding video forgery detection is presented. Later, a brief survey of existing passive video forgery detection techniques based on the features, forgery identified, datasets used, and performance parameters detail along with their limitations are reviewed. Then, anti-forensics strategy and deepfake detection in the video are discussed. After that, standard benchmark video forgery datasets and the generalized architecture for passive video forgery detection techniques are discussed. Finally, few open challenges in the field of passive video forgery detection are also described.

A comprehensive survey on passive techniques for digital video forgery detection

In this paper, a new algorithm is proposed for forgery detection in MPEG videos using spatial and time domain analysis of quantization effect on DCT coefficients of I and residual errors of P frames. The proposed algorithm consists of three modules, including double compression detection, malicious tampering detection and decision fusion. Double compression detection module employs spatial domain analysis using first significant digit distribution of DCT coefficients in I frames to detect single and double compressed videos using an SVM classifier. Double compression does not necessarily imply the existence of malignant tampering in the video. Therefore, malicious tampering detection module utilizes time domain analysis of quantization effect on residual errors of P frames to identify malicious inter-frame forgery comprising frame insertion or deletion. Finally, decision fusion module is used to classify input videos into three categories, including single compressed videos, double compressed videos without malicious tampering and double compressed videos with malicious tampering. The experimental results and the comparison of the results of the proposed method with those of other methods show the efficiency of the proposed algorithm.

Malicious inter-frame video tampering detection in MPEG videos using time and spatial domain analysis of quantization effects

Detection of double compression in MPEG-4 videos based on block artifact measurement

Dense moment feature index and best match algorithms for video copy-move forgery detection

Among different types of video manipulations, video inter-frame forgery is a powerful and common tampering operation. Several forensic and anti-forensic techniques have been proposed to deal with this challenge. In this paper, we first improve an existing video frame deletion detection algorithm. The improvement is attributed to the combination of two properties resulted from video frame deletion, the periodicity and the magnitude of the fingerprint in the P-frame prediction error. We then analyze a typical anti-forensic method of video frame deletion, and prove that the fingerprint of frame deletion still can be discovered after being anti-forensically modified. We thus further propose a counter anti-forensics approach by estimating the true prediction error and comparing it with the prediction error stored in videos. We show that the detection algorithm is not only useful in detecting video frame deletion, but also useful for detecting video frame insertion. Compared with the existing counter anti-forensics, our proposed approach is robust when different motion estimation algorithms are used in the initial compression. Furthermore, the forensics and counter anti-forensics are combined to perform a two-phase test to detect video inter-frame forgery. A Video Inter-frame Forgery (VIF) game, which is zero-sum, simultaneous-move, is defined to analyze the interplay between the forger and the investigator. Mixed strategy Nash equilibrium is introduced to solve the VIF game and we can obtain the optimal strategies for both players. Experimental results show that the proposed forensic and counter anti-forensic methods not only outperform existing methods in detecting frame deletion and anti-forensics, but also outperform them in the VIF game.

Xiangui Kang

Papers

Forensics and counter anti-forensics of video inter-frame forgery