Object-Based Video Tampering Localization via Trace Consistency Analysis

With the rapid advancement of object-based video inpainting and splicing tampering techniques, the dissemination of malicious videos on the internet poses significant risks. Existing localization methods, however, exhibit limitations such as restriction to specific datasets, limited performance in detecting unknown forgeries, and lack of robustness when dealing with reprocessed videos. In this paper, we propose an effective video tampering localization network that comprehensively considers the common characteristics of inpainting and splicing and extracts more generalized features of forgery traces, significantly enhancing localization performance. Specifically, we design four modules to independently extract inherent difference features, including edge artifacts, pixel distribution, texture features, and frequency information. For feature fusion learning, we employ a two-stage approach: first, a Convolutional Neural Network (CNN)-based module is used to extract local features; then, a Vision Transformer (ViT)-based module is utilized to extract global correlation features. Experimental results demonstrate that our method significantly outperforms existing state-of-theart methods. Ablation studies verify the necessity of each feature component, and the two-stage feature fusion approach shows better performance compared to directly embedding a CNN into a ViT hybrid architecture.