Forgery Aware Adaptive

Forgery-aware adaptive methods aim to improve the detection of manipulated images and videos, focusing on robustness and generalizability across diverse forgery techniques and datasets. Current research emphasizes transformer-based architectures, often incorporating adaptive modules that leverage pre-trained models while learning forgery-specific features through techniques like contrastive learning and continual learning to enhance performance. This field is crucial for combating the spread of misinformation and protecting the integrity of digital media, with applications ranging from security and law enforcement to social media verification.