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A machine learning forensics technique to detect post-processing in digital videos
یک روش پزشکی قانونی برای یادگیری ماشین برای تشخیص پس از پردازش در فیلم های دیجیتال-2020 Technology has brought great benefits to human beings and has served to improve the quality of
life and carry out great discoveries. However, its use can also involve many risks. Examples include
mobile devices, digital cameras and video surveillance cameras, which offer excellent performance and
generate a large number of images and video. These files are generally shared on social platforms and
are exposed to any manipulation, compromising their authenticity and integrity. In a legal process, a
manipulated video can provide the necessary elements to accuse an innocent person of a crime or to
exempt a guilty person from criminal acts. Therefore, it is essential to create robust forensic methods,
which will strengthen the justice administration systems and thus make fair decisions. This paper
presents a novel forensic technique to detect the post-processing of digital videos with MP4, MOV
and 3GP formats. Concretely, detect the social platform and editing program used to execute possible
manipulation attacks. The proposed method is focused on supervised machine learning techniques. To
achieve our goal, we take advantage that the social platforms and editing programs, execute filtering
and compression processes on the videos when they are shared or manipulated. The result of these
transformations leaves a characteristic pattern in the videos that allow us to detect the social platform
or editing program efficiently. Three phases are involved in the method: 1) Dataset preparation; 2) data
features extraction; 3) Supervised model creation. To evaluate the scalability of the technique in real
scenarios, we used a robust, heterogeneous and far superior dataset than that used in the literature. Keywords: Editing programs detection | Machine learning processing | Multimedia container structure | Social networks detection | Video forensics | Video post-processing detection |
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