Image Forgery

Image forgery detection aims to identify and locate manipulated areas within digital images, combating the spread of misinformation and protecting copyright. Current research heavily utilizes deep learning models, including convolutional neural networks (CNNs) and transformers, often incorporating techniques like attention mechanisms and contrastive learning to improve feature extraction and localization accuracy. The field is actively addressing challenges such as the creation of realistic forgery datasets for training and the development of robust methods that generalize well across various manipulation techniques and image sources, impacting fields ranging from digital forensics to media verification.