Navigating the Waters of Image Watermarking: A Neural Network-Centric Review

Image watermarking is a procedure to incorporate a watermark, be it any hidden data into an image. To later confirm ownership or validity in the matter of copyright protection, or to trace the image’s dissemination, the watermark can be removed from the image. Traditional image watermarking techniques often insert the watermark using handcrafted elements. These qualities can, however, be fragile and challenging to develop for many sorts of images. Image watermarking can be done more flexibly and effectively using neural networks on account of their automated feature extraction techniques. Neural networks can be trained to learn the statistical characteristics of images in order to implant the watermark in a way that is both undetectable and resistant to attacks. The current state of the art as regards image watermarking using neural networks is assessed in this paper. A discussion of the various types of neural network architectures that are generally used to watermark images is provided. This review also reviews the performance of neural network image watermarking schemes on a wide range of data sets and under different attack conditions. The results of this review show that neural networks offer a promising approach to image watermarking but it’s not without it’s own set of limitations. Neural network-based watermarking schemes have shown superior performance to traditional schemes in terms of imperceptibility, robustness, and capacity and have sometimes lagged behind on account of their scalability and generalization. In developing future image watermarking systems, we assume that neural networks will continue to be crucial, and with that perspective comes the addition of numerous lines of futuristic thought about advancements on these projects. This is included in the review paper to inspire thoughts on new projects and draw a fitting conclusion to the paper.