ACE algorithm in the application of video forensics

Jia, Shan; Feng, Chunhui; Wang, Chao; Xu, Yinghui; T, Wang

doi:10.1201/b18512-40

Cited by 2 publications

(1 citation statement)

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“…We download 150 videos with noticeable brightness changes from the video website and adopt the metrics of ACE forensics [35] to determine the brightness changes of videos. We found that these videos have a higher intensity of dynamic brightness changes than the experimental video library.…”

Section: Methodsmentioning

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: Methodsmentioning

confidence: 99%