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

Aghamaleki, Javad Abbasi; Behrad, Alireza

doi:10.1007/s11042-016-4004-z

Cited by 29 publications

(9 citation statements)

References 27 publications

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“…Methods in the first category usually extracted image features of each frame, such as texture features [9], color characteristics [9,16], histogram features [16], structural features [17], etc. Methods in the second category mainly utilized the impact of tampering on video features, including video encoding characteristic [18][19][20], double compression [21], motion features such as errors in motion estimation [10], optical flow, predict residual gradients [19], and brightness features such as segmented brightness variance descriptor (BBVD) [2], illumination information [4], etc.…”

Section: Methods Without Considering Noisesmentioning

confidence: 99%

Overcome the Brightness and Jitter Noises in Video Inter-Frame Tampering Detection

Han

Huang

Weng

et al. 2021

Sensors

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Digital video forensics plays a vital role in judicial forensics, media reports, e-commerce, finance, and public security. Although many methods have been developed, there is currently no efficient solution to real-life videos with illumination noises and jitter noises. To solve this issue, we propose a detection method that adapts to brightness and jitter for video inter-frame forgery. For videos with severe brightness changes, we relax the brightness constancy constraint and adopt intensity normalization to propose a new optical flow algorithm. For videos with large jitter noises, we introduce motion entropy to detect the jitter and extract the stable feature of texture changes fraction for double-checking. Experimental results show that, compared with previous algorithms, the proposed method is more accurate and robust for videos with significant brightness variance or videos with heavy jitter on public benchmark datasets.

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Section: Methods Without Considering Noisesmentioning

confidence: 99%

Overcome the Brightness and Jitter Noises in Video Inter-Frame Tampering Detection

Han

Huang

Weng

et al. 2021

Sensors

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“…Aghamaleki and Behrad [9], leveraged on the spatial and time-domain analysis of quantization effect and developed a novel framework for forgery detection in MPEG videos. The model distinguished video segments into single compressed videos, double compressed videos without malicious tampering and double compressed videos with malicious tampering using a decision fusion algorithm.…”

Section: Related Workmentioning

confidence: 99%

Authentication of Video Evidence for Forensic Investigation: A Case of Nigeria

Akumba¹,

Iorliam²,

Agber³

et al. 2021

JIS

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Video shreds of evidence are usually admissible in the court of law all over the world. However, individuals manipulate these videos to either defame or incriminate innocent people. Others indulge in video tampering to falsely escape the wrath of the law against misconducts. One way impostors can forge these videos is through inter-frame video forgery. Thus, the integrity of such videos is under threat. This is because these digital forgeries seriously debase the credibility of video contents as being definite records of events. This leads to an increasing concern about the trustworthiness of video contents. Hence, it continues to affect the social and legal system, forensic investigations, intelligence services, and security and surveillance systems as the case may be. The problem of inter-frame video forgery is increasingly spontaneous as more video-editing software continues to emerge. These video editing tools can easily manipulate videos without leaving obvious traces and these tampered videos become viral. Alarmingly, even the beginner users of these editing tools can alter the contents of digital videos in a manner that renders them practically indistinguishable from the original content by mere observations. This paper, however, leveraged on the concept of correlation coefficients to produce a more elaborate and reliable inter-frame video detection to aid forensic investigations, especially in Nigeria. The model employed the use of the idea of a threshold to efficiently distinguish forged videos from authentic videos. A benchmark and locally manipulated video datasets were used to evaluate the proposed model. Experimentally, our approach performed better than the existing methods. The overall accuracy for all the evaluation metrics such as accuracy, recall, precision and F1-score was 100%. The proposed method implemented in the MATLAB programming language has proven to effectively detect inter-frame forgeries.

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“…This is no longer the case, however, many published techniques, specific to particular types of tampering or source authentication, were assessed on proprietary datasets which remain unreleased [21,26,27,28,29]. In some cases, [30], datasets are detailed sufficiently in the literature so that they can be replicated, provided the sequences used for dataset synthesis are available. This serves to evidence the fragmentation of the tampering detection field.…”

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

Cited by 29 publications

References 27 publications

Overcome the Brightness and Jitter Noises in Video Inter-Frame Tampering Detection

Overcome the Brightness and Jitter Noises in Video Inter-Frame Tampering Detection

Authentication of Video Evidence for Forensic Investigation: A Case of Nigeria

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

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