Robust Image Watermarking Using LWT and Stochastic Gradient Firefly Algorithm

Watermarking of digital images is required in diversified applications ranging from medical imaging to commercial images used over the web. Usually, the copyright information is embossed over the image in the form of a logo at the corner or diagonal text in the background. However, this form of visible watermarking is not suitable for a large class of applications. In all such cases, a hidden watermark is embedded inside the original image as proof of ownership. A large number of techniques and algorithms are proposed by researchers for invisible watermarking. In this paper, we focus on issues that are critical for security aspects in the most common domains like digital photography copyrighting, online image stores, etc. The requirements of this class of application include robustness (resistance to attack), blindness (direct extraction without original image), high embedding capacity, high Peak Signal to Noise Ratio (PSNR), and high Structural Similarity Matrix (SSIM). Most of these requirements are conflicting, which means that an attempt to maximize one requirement harms the other. In this paper, a blind type of image watermarking scheme is proposed using Lifting Wavelet Transform (LWT) as the baseline. Using this technique, custom binary watermarks in the form of a binary string can be embedded. Hu’s Invariant moments’ coefficients are used as a key to extract the watermark. A Stochastic variant of the Firefly algorithm (FA) is used for the optimization of the technique. Under a prespecified size of embedding data, high PSNR and SSIM are obtained using the Stochastic Gradient variant of the Firefly technique. The simulation is done using Matrix Laboratory (MATLAB) tool and it is shown that the proposed technique outperforms the benchmark techniques of watermarking considering PSNR and SSIM as quality metrics.