FOCAL: A Forgery Localization Framework based on Video Coding Self-Consistency

TL;DR

This paper introduces FOCAL, a video forgery localization framework that assesses coding trace self-consistency using explainable neural networks, improving detection accuracy in temporal and spatial splicing scenarios.

Contribution

The paper proposes a novel forgery detection method based on coding trace self-consistency and a specialized explainable neural network architecture for feature extraction.

Findings

Improves state-of-the-art in temporal splicing localization

Effective in detecting spatial splicing in synthetic and real videos

Demonstrates robustness across different forgery scenarios

Abstract

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.