Robust Watermarking in Digital Image Using Neural Network and Glowworm Optimization

Abstract

Digital watermarking has been recognized as a very vast research field to handle the challenges that occur during the distribution of digital content over a network or the internet. The digital content may be misused by an unauthorized person, or its copyright may be illegally claimed by someone who is not actually involved in the creation of this digital content. So, it is highly required to overcome these issues to protect the digital contents from unauthorized persons. Digital watermarking techniques are very useful in this regard. In this technique, a secret message is embedded imperceptibly into the actual digital content. The embedded secret information is called a "watermark". The watermark may be a logo of an organization or company, some hidden text, the author’s secret serial number, or images of some special importance or a label. It could be further used for different applications, such as copyright protection, authentication, and temper detection. In the paper, the discrete wavelet transform (DWT) has been applied for image segmentation. Optimization of the segmented image is achieved through the Glowworm Swarm Optimization algorithm, and lastly, the Advanced Encryption System is used to encrypt the watermarked image. The method proposed in this paper will improve security, performance, and efficiency as compared with already existing techniques.