## 4 Technical Review and Discussion
### Evaluation Metrics
#### 4.1.1 Imperceptibility Evaluation for Watermarking in Latent Diffusion Models  
Imperceptibility refers to how well the embedded watermark is hidden within the generated content without affecting its visual quality. The primary goal of an imperceptible watermark is to remain invisible to the human eye while maintaining the aesthetics and quality of the generated image. Traditionally, metrics such as Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), and Mean Squared Error (MSE) are employed to quantify visual quality differences between original and watermarked images. However, watermarking in generative model domains like LDMs does not involve modifying an original cover image directly, making traditional comparison metrics less applicable (See Figure 6).  
Unlike traditional metrics, which provide specific numerical results to quantify image quality, embedding transparency is about maintaining consistency without direct measurement. Instead, it serves as a qualitative assessment strategy, ensuring that watermark embedding causes minimal alteration. To properly evaluate imperceptibility within LDMs, quantitative approaches like Learned Perceptual Image Patch Similarity (LPIPS) [43], Frechet Inception Distance (FID) [44], or human perceptual studies can be used alongside embedding transparency to report specific results or scores [23].  
These evaluation strategies and metrics are crucial for assessing how well watermarking techniques preserve the original quality of generated images. Unlike conventional watermarking, where an original cover image is directly modified, LDM watermarking must ensure that the watermark is seamlessly integrated during the latent generation process, resulting in high-quality outputs that do not raise suspicion.  
Given these strategies, recent research has developed alternative evaluation methods specific to the generative context:  
* **Perceptual consistency metrics:** In studies such as [37], the focus is on maintaining consistency in high-level semantics rather than individual pixel values. Metrics like LPIPS are often utilized. LPIPS leverages deep neural network features to determine perceptual differences, providing a more reliable measure of how similar two images appear to a human observer when traditional pixel-based differences are not available [45].  
Figure 6: Watermarking technique through adjustment of the generative semantic space and the inapplicability of PSNR and SSIM metrics. (**a**) Original image, (**b**) traditional watermarking, and (**c**) watermarked image from generative model. Images are taken from [23].  
* **FID:** The FID score has also been adopted as a proxy for evaluating imperceptibility in generative contexts. It measures the distance between feature representations of watermarked and non-watermarked images using an Inception network, thus quantifying how closely the distribution of watermarked images matches that of non-watermarked ones. This helps in ensuring that the introduction of a watermark does not degrade the quality of generated images perceptually [29].
* **Human evaluation studies:** Some works also incorporate user studies as a part of their imperceptibility evaluation. Participants are asked to differentiate between watermarked and non-watermarked images in a controlled setting to determine if watermarks are noticeable. For example, in the DiffuseTrace [21], this evaluation approach was used to empirically validate the transparency of embedded watermarks across different types of generative content.