Splicing Localization

Splicing localization focuses on identifying regions within images or videos that have been manipulated by copying and pasting content from other sources. Current research heavily utilizes deep learning, employing convolutional neural networks (CNNs) and vision transformers (ViTs) within multi-stream architectures to analyze various image features (e.g., RGB, edge, depth, texture) and temporal information in videos. These advanced models aim to improve the accuracy and robustness of forgery detection, particularly in the face of compression artifacts and diverse tampering techniques. The ability to reliably detect image and video splicing has significant implications for combating misinformation and ensuring the authenticity of digital media.