A machine learning forensics technique to detect post-processing in digital videos

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. (C) 2020 Elsevier B.V. All rights reserved.