Forgery Detection

Forgery detection research aims to reliably distinguish authentic media (images, videos, audio, signatures) from fabricated content, focusing on improving accuracy and generalization across diverse forgery techniques and datasets. Current research heavily utilizes deep learning models, particularly Vision Transformers (ViTs) and multimodal large language models (MLLMs), often incorporating techniques like contrastive learning, attention mechanisms, and self-supervised pre-training to enhance performance and explainability. This field is crucial for combating misinformation, protecting identities, and ensuring the trustworthiness of digital information, with ongoing efforts to address challenges like bias, robustness to unseen forgeries, and efficient adaptation to new data.