Securing high resolution grayscale facial captures using a blockwise coevolutionary GA

In biometric systems, reference facial images captured during enrollment are commonly secured using watermarking, where invisible watermark bits are embedded into these images. Evolutionary Computation (EC) is widely used to optimize embedding parameters in intelligent watermarking (IW) systems. Traditional IW methods represent all blocks of a cover image as candidate embedding solutions of EC algorithms, and suffer from premature convergence when dealing with high resolution grayscale facial images. For instance, the dimensionality of the optimization problem to process a 2048 Â 1536 pixel grayscale facial image that embeds 1 bit per 8 Â 8 pixel block involves 49k variables represented with 293k binary bits. Such Large-Scale Global Optimization problems cannot be decomposed into smaller independent ones because watermarking metrics are calculated for the entire image. In this paper, a Blockwise Coevolutionary Genetic Algorithm (BCGA) is proposed for high dimensional IW optimization of embedding parameters of high resolution images. BCGA is based on the cooperative coevolution between different candidate solutions at the block level, using a local Block Watermarking Metric (BWM). It is characterized by a novel elitism mechanism that is driven by local blockwise metrics, where the blocks with higher BWM values are selected to form higher global fitness candidate solutions. The crossover and mutation operators of BCGA are performed on block level. Experimental results on PUT face image database indicate a 17% improvement of fitness produced by BCGA compared to classical GA. Due to improved exploration capabilities, BCGA convergence is reached in fewer generations indicating an optimization speedup. In face recognition, high resolution facial capture images are commonly transferred and archived for various access control or surveillance applications. Since these images are vulnerable to unauthorized manipulations, watermarking is widely used to secure such images by embedding invisible watermark bits. The watermarking process should satisfy the trade-off between the distortion resulting from embedding the watermark (quality), and the resistance of the watermarked image to manipulations (robust-ness). The distortion resulting from the embedding is commonly measured using watermark quality metrics which is based on Human Vision System (HVS). The resistance of the watermarked image to manipulations is commonly measured using watermark robustness metrics, these metrics compare the original watermark with the extracted one from the manipulated watermarked image. Computational intelligence techniques are widely used to find the optimal watermark embedding parameters to satisfy the trade-off between watermark quality and robustness. Different Evolutionary Computation (EC) techniques have been proposed in intelligent watermarking …