Image watermarking based on spiking neural networks

Abstract Image watermarking serves as a crucial technique for protecting copyrights and verifying the ownership of digital images. The watermarking process involves embedding data into images to prevent unauthorized usage of digital content. In this study, an innovative image watermarking method, using Spiking Neural Networks to embed robust and imperceptible watermarks, is proposed. The proposed method benefits from the edge detection capabilities of spiking neural networks to identify optimal regions for watermark locations. By targeting edge detection, this watermarking approach ensures significant resistance to common image processing attacks such as compression, noise addition, and cropping, while maintaining minimal perceptual distortion. The edge image obtained with the spiking neural network approach is divided into 16 × 16 non-overlapping blocks, and edge pixel definitions are made. Moreover, to increase the security level, the watermark image is scrambled with the help of a chaotic substitution box. The scrambled image is placed on the pixels marked as edges in the edge image. Afterward, it is divided into sub-bands by applying a discrete wavelet transform. The watermark image is inserted into the HH (High-High) band with the help of the discrete wavelet transform and the singular value decomposition approach. In the extraction stage, the HH band of the original image is used together with the watermarked image. Comprehensive experiments are conducted to evaluate the proposed technique, revealing its superiority in preserving both image quality and watermark integrity compared to conventional approaches.