# Robust JND-Guided Video Watermarking via Adaptive Block Selection and Temporal Redundancy
## 3 Proposed Method
### General Architecture
#### 3.1.1 Step 1: Perceptual Map Computation  
The perceptual score map used in this method is computed by combining multiscale texture information, saliency estimation, and semantic region masking. This combination was chosen to ensure that the watermark is embedded in regions that are structurally rich, perceptually tolerant, and semantically non-critical. Specifically, multiscale texture analysis using Laplacian of Gaussian filters captures local detail stability across resolutions and has been shown to improve watermark persistence in textured areas [35; 36]. Contrast-based saliency models, such as spectral residual methods, identify low-attention regions where modifications are less likely to be noticed by the human visual system [37]. Finally, semantic masking ensures that perceptually sensitive or meaning-bearing regions (e.g., faces, objects) are excluded from embedding, aligning with findings from object-aware watermarking and attention-preserving methods [38].  
This combination creates a perceptual score map that balances imperceptibility with structural robustness, guiding block selection accordingly. Each frame is divided into nonoverlapped \(8\times 8\) blocks \(B_{ij}\). For each block we compute the following:* A multiscale detailed map using Laplacian of Gaussian filters applied at \(M\) different scales (\(D_{i,j}\)) [36]. This map captures texture richness and edge strength across multiple spatial scales. The Laplacian of Gaussian (\(LoG\)) filter enhances local variations, such as corners and edges, which are less perceptually sensitive. The equation is as follows: \[D_{i,j}=\frac{1}{M}\sum_{k=1}^{M}|LoG(I,\sigma_{k})|(i,j)\;\;,\] (1) computes the average response across \(M\) different Gaussian scales \(\sigma_{k}\), typically chosen in the range between 1 and 3. High values of \(D_{i,j}\) indicate structurally complex areas where watermark energy can be safely embedded.
* The saliency map \(S(i,j)\) is computed using spectral residual contrast [37]. This map identifies low-attention regions in the frequency domain based on spectral irregularities. This approach has been widely used due to its simplicity and effectiveness in modeling human visual attention. Since this method is used without modification, full mathematical details are omitted here for brevity.
* In this work, a semantic mask \(M(i,j)\) is constructed using the Haar cascade frontal face detector proposed by [PERSON] and [PERSON] [38], applied to exclude sensitive regions such as faces or foreground objects from the embedding process. This ensures that watermark insertion is avoided in semantically important areas, preserving visual fidelity. No changes were made to the underlying detector beyond default parameters. While more accurate methods based on deep learning (e.g., YOLO, RetinaFace) are available, Haar cascades provide a good balance between performance and computational cost, making them suitable for real-time and embedded watermarking applications.  
The final perceptual score map is computed by combining the three components with empirically determined weights \(\alpha\), \(\beta\), and \(\gamma\):  
\[P_{i,j}=\alpha\cdot D(i,j)-\;\beta\cdot S(i,j)-\gamma\cdot M(i,j)\;, \tag{2}\]  
where the weight values are \(\alpha=0.4\), \(\beta=0.3\), and \(\gamma=0.3\). Blocks with higher \(P_{i,j}\) scores are prioritized for embedding. Figure 1 visually illustrates the four key components used to construct the perceptual score map applied to the video sequence \"Akiyo\". The subfigures show the following: (a) a multiscale detail map capturing high-frequency edges via Laplacian filtering; (b) a saliency map derived from spectral residuals, highlighting perceptually sensitive regions to be avoided; (c) a semantic mask that detects and excludes face regions; and (d) the resulting perceptual score map that combines these cues. This visualization helps clarify how the method selects blocks that are structurally rich yet perceptually tolerant, supporting the adaptive block selection described in Section 3.1.2. Step 2.