  Forgery operations on video contents are nowadays within the reach of anyone,
thanks to the availability of powerful and user-friendly editing software.
Integrity verification and authentication of videos represent a major interest
in both journalism (e.g., fake news debunking) and legal environments dealing
with digital evidence (e.g., a court of law). While several strategies and
different forensics traces have been proposed in recent years, latest solutions
aim at increasing the accuracy by combining multiple detectors and features.
This paper presents a video forgery localization framework that verifies the
self-consistency of coding traces between and within video frames, by fusing
the information derived from a set of independent feature descriptors. The
feature extraction step is carried out by means of an explainable convolutional
neural network architecture, specifically designed to look for and classify
coding artifacts. The overall framework was validated in two typical forgery
scenarios: temporal and spatial splicing. Experimental results show an
improvement to the state-of-the-art on temporal splicing localization and also
promising performance in the newly tackled case of spatial splicing, on both
synthetic and real-world videos.
