Tamper Detection

Tamper detection research focuses on identifying manipulations in various media types, including images, videos, and text, aiming to verify authenticity and provenance. Current efforts leverage deep learning models, such as convolutional neural networks, vision transformers, and large language models (LLMs), often incorporating techniques like test-time training and multi-modal approaches for improved accuracy and explainability. This field is crucial for combating the spread of misinformation and protecting intellectual property, with applications ranging from art authentication to securing digital documents and combating the malicious use of AI-generated content. The development of robust and generalizable tamper detection methods is a significant challenge driving ongoing research.