Damageless Digital Watermarking by Machine Learning: A Method of Key Generation for Information Extraction Using Artificial Neural Networks
Kensuke Naoe, Hideyasu Sasaki, Yoshiyasu Takefuji · 2009
Soft computing in the area of information security is a promising field for the creation of intelligent solutions. This paper discusses a method for digital watermarking using artificial neural networks to realize secure copyright protection of visual information without any damage. The discussed watermark extraction keys and feature extraction keys identify the secure and unique hidden patterns for proper digital watermarks. In the experiments, we have shown that the proposed method is robust to high pass filtering and JPEG compression of visual information, only for those watermark extraction keys which were able to identify the proper hidden bit patterns from original visual information using corresponding feature extraction keys. The proposed method is to contribute to secure visual digital watermarking without damaging or losing any detailed data of visual information.