Image Forgery Localization

Image forgery localization aims to automatically identify tampered regions within digital images, a crucial task given the increasing ease and realism of image manipulation. Current research focuses on developing robust and generalizable deep learning models, often employing transformer networks, convolutional neural networks (like ConvNeXt), or hybrid architectures that leverage multi-modal data (e.g., combining image and text information) to improve accuracy and explainability. These advancements are vital for combating the spread of misinformation and enhancing the reliability of digital evidence in various fields, including law enforcement, journalism, and online security.