FreqTransNet: A Frequency-Aware Transformer Network for Robust Image Watermarking

FreqTransNet is a new deep learning watermarking model that combines convolutional modules, Transformer structures (neural networks that use self-attention to understand relationships between distant parts of data), and frequency-domain transformations (mathematical techniques that analyze images by breaking them into component frequencies) to embed invisible marks into images more robustly. The model outperforms existing watermarking methods, achieving better visual quality and maintaining over 97% accuracy in extracting watermarks even when images are attacked or modified.