# Robust JND-Guided Video Watermarking via Adaptive Block Selection and Temporal Redundancy
## 1 Introduction  
The digitalization of multimedia content and the prevalence of high-bandwidth communication networks has considerably changed the production, distribution, and consumption of information. Video content has emerged as a predominant medium across multiple sectors, including entertainment, education, social media, and surveillance. The simplicity of duplicating, modifying, and redistributing digital video has generated significant apprehensions about intellectual property safeguarding, content verification, and data integrity [1, 2]. These issues have been increased by the widespread use of compressionstandards, transcoding across platforms, and malicious interference, which may distort or eliminate identifying information. Digital watermarking has emerged as a suitable solution for these issues. It allows embedding copyright or authentication data directly into the host media in a way that is imperceptible to human observers while remaining robust to signal processing and malicious attacks. A wide range of watermarking schemes have been proposed for still images [3,4], audio signals [5], and digital videos [6,7]. While image watermarking has matured considerably, the domain of video watermarking still presents unique challenges due to the temporal dimension, larger data volumes, and frequent exposure to aggressive processing such as compression, frame rate conversion, and cropping [8]. Two dominant paradigms exist in video watermarking: transform-domain and spatial-domain approaches. Transform-domain techniques, such as those based on the Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT), or Singular Value Decomposition (SVD), typically offer superior robustness but at the cost of high computational complexity [9, 10, 11]. Spatial-domain methods, in contrast, offer lower computational overhead and are easier to implement in resource-constrained environments, but they often lack sufficient robustness under aggressive signal modifications [12,13]. Recent advances have attempted to bridge this trade-off. One promising direction is the use of Just Noticeable Difference (JND) models to guide watermark embedding in the spatial domain, leveraging the human visual system (HVS) to determine perceptually insensitive regions [14,15]. By incorporating saliency information or texture masking, such methods can adaptively modulate the watermark energy, balancing imperceptibility and robustness. For instance, the proposal in [16] presented a saliency-modulated JND model for video frames that enhances the watermark strength in low-attention regions. Similarly, [PERSON] et al. [14] demonstrated effective image watermarking by combining color sensitivity with JND thresholds. Despite these improvements, key limitations persist. First, the majority of spatial-domain schemes rely on static, frame-level decisions, neglecting the temporal dynamics and redundancies present across frames. Second, the embedding locations are often selected uniformly or randomly, without regard to perceptual importance, spatial stability, or semantic content. This can lead to suboptimal robustness, especially under cropping, compression, or transcoding attacks [17]. Third, many schemes utilize single-frame insertion without redundancy, resulting in high Bit Error Rates (BER) during extraction in realistic conditions [18,19]. To overcome these limitations, researchers have increasingly explored the integration of perceptual modeling, adaptive modulation, and channel coding techniques. Among modulation strategies, Quantization Index Modulation (QIM) has gained attention due to its theoretical robustness and simplicity. QIM embeds watermark bits by quantizing selected host features to different intervals based on the message bit [20]. While originally designed for still images, it has been successfully adapted for videos when combined with temporal synchronization strategies [21,22]. Redundancy is another critical aspect of robust watermarking.