Reversible Steganography

Reversible steganography focuses on embedding information within a cover medium (images, text, or neural networks) in a way that allows for perfect recovery of both the original medium and the hidden data. Current research emphasizes developing robust and secure methods using invertible neural networks, particularly focusing on improving the trade-off between embedding capacity and the imperceptibility of the hidden information. This field is significant for its applications in data security, intellectual property protection (e.g., watermarking deep learning models), and authentication, offering solutions that avoid the information loss inherent in traditional irreversible techniques.