Image Forgery Detection

Image forgery detection aims to identify and locate manipulated regions within digital images, a crucial task given the proliferation of fake media. Current research heavily utilizes deep learning, employing convolutional neural networks and transformer architectures, often incorporating techniques like contrastive learning and unsupervised clustering to improve robustness and generalization across diverse forgery types. These advancements focus on handling increasingly sophisticated forgeries, including those created using deep learning-based image synthesis and editing tools, and improving the accuracy and reliability of detection and localization. The field's impact extends to combating misinformation, enhancing digital forensics, and ensuring the trustworthiness of visual information across various applications.