Text Tampering

Text tampering, the fraudulent alteration of text within images, is a growing concern fueled by advancements in generative AI. Current research focuses on developing robust detection methods, employing techniques like hybrid approaches combining traditional feature extraction with deep learning models (e.g., Bayesian classifiers, gradient boosting, and transformer-based models), and dual-path networks that analyze both image and text features. These efforts aim to improve the generalization capabilities of detection systems, addressing the challenge of identifying unseen forgery types and enhancing accuracy across diverse tampering techniques. The development of high-quality, real-world datasets is crucial for training and evaluating these models, ultimately contributing to improved information security and combating the spread of misinformation.