Video forensic tools exploiting features from video-container to video-encoder level

The escalation of multimedia contents exchange, especially of videos belonging to mobile devices, and the availability of a great amount of editing software has raised grave doubts on their digital life-cycle. In this thesis, we firstly introduce a new dataset for multimedia forensics and then develop forensic tools that analyse the video-container and the video-signal in order to evaluate possible tampering that have been introduced in the life-cycle of a video content. The first contribution consists on the release of a new Dataset of videos and images captured from to 35 modern smartphones/tablets belonging to 11 different brands: Apple, Asus, Huawei, Lenovo, LG electronics, Microsoft, Oneplus, Samsung, Sony, Wiko and Xiaomi. Overall, we collected 11732 native images; 7565 of them were shared through Facebook, in both high and low quality, and through WhatsApp, resulting in a total of 34427 images. Furthermore we acquired 648 native videos, 622 of which were shared through YouTube at the maximum available resolution, and 644 through WhatsApp, resulting in a total of 1914 videos. The uniqueness of the VISION dataset was tested on well known forensic tool, i.e., the detection of the Sensor Pattern Noise (SPN) left by the acquisition device for the source identification of native/social media contents. The second contribution is based on the analysis of the container structure of videos acquired by means of mobile devices. We argue that the atoms belonging to the container, in terms of order and value, are fragile and that it is more difficult to hide their modifications than the regular metadata. This characteristic can be exploited to perform Source Identification and Integrity Verification of videos taken from devices belonging to well known operating systems and manufactures. In the third contribution we focus on the video-signal and on its encoding process. We used codecs that perform a hybrid video coding scheme, and developed a classification technique able to perform group of picture length estimation and double compression detection. The proposed technique is one of the fastest approaches that use videos encoded with B-frames, with both constant bit rate and variable bit rate.