# A Brief, In-Depth Survey of Deep Learning-Based Image Watermarking
## 3 Comprehensive Survey of Deep Learning-Based Image Watermarking
### Embedder-Extractor Joint Training Methods
[39] introduced a scheme accentuated by a multi-class discriminator connected to the noise-infused marked image. This innovation not only targets robustness but also amplifies the watermark's security within the marked image. [PERSON] et al. [40], while aligning with the visual quality scrutiny embedded in HiDDeN [7], proposed the integration of a high-pass filter at the dis criminator's inception. This strategy nudges the watermark into the image's mid-frequency region, safeguarding visual quality given the amendable nature of high-frequency components. The loss computation accords amplified significance to the central region, resonating with the human visual system's focal inclination. For noise layer augmentation, additions encompass crop, crop-out, Gaussian blur, directional flips, and JPEG compression, painting a comprehensive spectrum of distortions. [PERSON] et al. [41] employed a reversible neural network functioning dually as the embedder and extractor, a strategy that is consistent with the traditional, reversible nature of watermarking transformations. In a deviation, [PERSON] et al. [42] advocated for the computation and integration of the difference between the marked and cover images into the extractor to augment extraction quality, a technique that transitions away from the blind scheme archetype. [PERSON] et al. [43] introduced a factor into the embedder, modulating the watermark's intensity on the cover image, and employed a trained spatial attention feature map to optimize watermark positioning. [PERSON] et al. [44] targeted an enhancement of embedding capacity, accommodating one to three watermark color images and employing a decoupling and revealing network tandem in the extraction phase. Their noise layer is fortified with cropping, scaling, Gaussian noise, JPEG, and Gaussian blur to simulate realistic distortions. In a novel structural approach, [PERSON] et al. [45] introduced an extractor-embedder-extractor training architecture to bolster extractor efficiency. Their extractor transforms an image into a binary watermark sequence while the embedder crafts an image residual from both the original and decoded watermark, enhancing marked image creation and extraction efficacy. This methodology underscores decoder training, prioritizing extraction quality. Incorporating reinforcement learning, [PERSON] et al. [46] enhanced the robustness of the embedder-extractor framework. They amalgamated convolutional neural networks (CNNs) [47] for embedding and extraction and a reinforcement learning actor for noise module operations. The actor, pivotal in selecting and integrating noise types and intensities into marked images, complements the embedder-extractor, which functions as an evaluative environment, assessing the actor's actions and training the extractor to counteract the induced noise efficaciously. Multimedia has also emerged as a significant focus in the field of deep learning-based image watermarking. [PERSON] and [PERSON] [48], for instance, developed a novel method to embed audio watermarks into cover images, accompanied by a network specifically engineered to determine the fidelity of the extracted audio watermark to its original form. In the domain of document images, [PERSON] et al. [49] proposed a technique enriched with multiple skip connections within the embedder, ensuring the preservation of intricate details in both watermark and cover images. The robustness of the watermarked document images is fortified through the integration of Dropout, crop-out, Gaussian blur, Gaussian noise, resizing, and JPEG techniques in the noise layer. [PERSON] et al. [50] expanded the embedder-extractor watermarking horizon to GIF animations. Their approach entails the employment of three-dimensional deep neural networks, transforming a single watermark into a three-dimensional feature for integration with GIFs. Discriminator networks are employed to evaluate the watermarking efficacy, focusing on maintaining the imperceptibility of the watermarked GIFs, and ensuring visual quality while securing embedded data.