Image Manipulation Localization

Image manipulation localization (IML) aims to pinpoint tampered regions within digital images by identifying subtle inconsistencies or artifacts introduced during manipulation. Current research focuses on developing robust models, often employing convolutional neural networks (CNNs) or Vision Transformers (ViTs), sometimes enhanced with handcrafted features or multi-modal fusion techniques, to improve accuracy and generalization across diverse manipulation types. These advancements are crucial for combating the spread of misinformation and enhancing the trustworthiness of digital media, with applications ranging from forensic investigations to social media verification. The field is actively addressing challenges related to limited and low-quality training data, as well as improving the robustness of models to various post-processing techniques.