Specific Forgery

Specific forgery detection research aims to identify and localize manipulations within images and videos, combating the spread of misinformation and fraudulent content. Current efforts focus on developing robust algorithms, often employing convolutional neural networks (CNNs) and wavelet transforms, to detect various forgery types, including deepfakes, copy-move manipulations, and inpainting artifacts. Researchers are actively addressing biases in existing models and exploring techniques like data augmentation with synthetic forgeries to improve generalization and interpretability, ultimately striving for more reliable and accurate detection methods. This work has significant implications for combating the spread of disinformation, authenticating digital media, and advancing image processing techniques.