## 1 Introduction
DWT applies DWT to the RGB contributions of the cover image, wherein watermark coefficients are embedded in DWT bands for robust resilience [7]. DWT uses approximation coefficients of one block as a watermark by embedding them into the detail coefficients of another block, which improves recovery and enhancements during tampering detection [10; 11]. DFT uses magnitudes that result in high imperceptibility of the watermarking [8] and other frequency domains to design algorithms to overcome the high-frequency information lost by lossy compression. Symmetry is essential in this approach, especially when using DCT in the embedding process. The symmetrical nature of DCT ensures that watermark information is evenly dispersed across the image, hence boosting resistance to lossy compression. Symmetry also enhances alignment between the embedder and decoder, which is necessary to keep the embedded watermark intact. The suggested watermarking technique is more imperceptible and robust to image distortion since it preserves symmetrical characteristics during embedding and decoding. In recent years, there have been many watermarking techniques using neural networks [12; 13]. These methods use neural networks to replace traditional embedding and extraction algorithms to improve the imperceptibility of watermarks, but most of them are non-blind watermarks, which are not suitable for practical applications. Therefore, since 2017, many scholars have proposed blind watermarking technology based on deep neural networks [14; 15; 16; 17; 18], which is more in line with reality, and uses various methods to train the resist image processing and steganalysis. The watermarking method is based on a deep neural network designed to replace the traditional embedding and extracting algorithm to improve the imperceptibility of the watermark and improve the watermark's robustness by simulating the image-processing attacks. Although these methods show good performance, particularly in terms of robustness against attacks and imperceptibility of watermarks, their effectiveness is measured using deep learning-based evaluation metrics such as the Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Bit Error Rate (BER). These metrics allow for a quantitative assessment of the robustness and accuracy of watermark recovery. On the other hand, these practices will make the overall network architecture larger and require a huge amount of training time. The purpose of this research is to develop a digital watermarking mechanism based on deep learning technology to protect the copyright of JPEG-compressed images. This paper proposes a watermarking method based on deep convolutional generative adversarial networks (DCGANs) [19], which decodes the JPEG-compressed image and restores (retrieves) the watermark using the generator of DCGANs. This study is aimed at defeating all the present watermarking techniques by designing a strong semi-blind watermarking method applicable for JPEG lossy compression. Specific objectives include:  
1. Designing embedding and decoding watermarking through a deep convolutional generative adversarial network (DCGAN). 2. Introducing a generator into the framework for each image watermark pair to gain robustness and flexibility in the approach. 3. Embedding a secret key mechanism, so that authorization is provisioned for a model not to be exploited without permission. 4. Providing a framework that allows exclusive model training for specific applications and enhances manageability. 5. Providing excellent robustness based on various quality factors, as validated by PSNR, SSIM, and BER metrics. The rest of the paper is organized as follows: the proposed method is elaborated in Section 2. Section 3 demonstrates the evaluation results. The conclusions and future work are presented in Section 4.