Hybrid deep learning based digital image watermarking using GAN-LSTM and adaptive gannet optimization techniques

Nowadays, multimedia technology is progressing everyday. It is very easy to duplicate, distribute and modify digital images with online editing software. Image security and privacy are critical aspects of the multimedia revolution. Therefore, digital image watermarking offers an alternative way out for image authentication. Currently, watermarking methods are crucial for safeguarding digital images. Several traditional watermarking approaches have been developed to protect images using spatial domains and transformations. Watermarking techniques that are more traditional are less resistant to repeated attacks. Deep learning-based watermarking has recently gained traction, greatly improving the safety of visual images in a variety of common applications. This study presents a robust and secure digital watermarking method for multimedia content protection and authentication. The watermark image is first transformed using the hybrid wavelet transform, and then it is encrypted using a chaos encryption algorithm. The cover image is simultaneously subjected to neighborhood-based feature extraction. Leveraging these extracted features, a novel Adaptive Gannet Optimization algorithm (AGOA) is employed to determine the optimal embedding location. Subsequently, the watermarked image is seamlessly integrated and extracted using the hybrid Generative adversarial network-based long short-term memory (GAN-LSTM) approach within the identified optimal region. Decryption and Inverse transformation are then used to get the original watermark image. Several previous methods, such as DNN, Deep-ANN, and Deep-CNN, are used to evaluate the performance of the proposed method. This technique improves multimedia content protection and authentication by guaranteeing strong and secure watermarking. The proposed method for digital image watermarking produced a peak signal-to-noise ratio of 46.412 and a mean square error of 24.512. Therefore, the proposed method performs well in digital image watermarking with a low error rate.