Manipulation Detection

Manipulation detection research aims to identify alterations in various data types, including images, videos, and text, focusing on both the detection of manipulation and the localization of altered regions. Current efforts leverage deep learning models, often employing transformer architectures and contrastive learning techniques, to analyze features and anomalies indicative of tampering, with a growing emphasis on multi-modal approaches and the development of robust datasets. This field is crucial for combating the spread of misinformation and deepfakes, with applications ranging from digital forensics and cybersecurity to social media fact-checking and the integrity of scientific research.