Localized Watermarking Techniques

• Localized watermarking is a technique that embeds watermarks in specific regions rather than across the entire content, enhancing targeted attribution and tamper localization.
• It employs methods such as semantic selection, mask-guided embedding, and feature-driven extraction to ensure high imperceptibility and robust resistance against local attacks.
• The approach balances watermark payload, perceptual quality, and precise error detection, proving vital for copyright protection, forensic analysis, and content provenance.

Localized watermarking refers to techniques that embed or detect watermarks in selected regions or objects within digital signals—rather than uniformly across the entire data instance. This spatial or semantic selectivity is motivated by applications in copyright attribution, tamper detection, provenance, and robustness against cropping or partial edits. Across modalities (images, video, audio, and text), localized methods balance imperceptibility, region-specific robustness, interpretability, and extraction capacity, often using sophisticated region selection mechanisms, adaptive embedding or masking strategies, and region-wise detection procedures. They enable fine-grained control over which elements of content carry the watermark and permit attribution even under compositional or partial modifications.

1. Principles and Motivations for Localized Watermarking

Localized watermarking emerged to address several core limitations of global watermarking:

• Partial Content Modification: In scenarios such as object replacement, inpainting, or region-specific edits (e.g., splicing, local inpainting, object removal), a global watermark becomes fully or partially irrecoverable. Localized watermarking ensures that surviving unedited regions retain the information necessary for attribution or detection (Sander et al., 2024, Hu et al., 17 Apr 2025).
• Selective Provenance and Attribution: In composite content (e.g., spliced images or mixed-authorship documents), localized watermarks permit provenance tracing at the region or object level, enabling extraction of individually embedded messages (Sander et al., 2024).
• Tamper Detection and Localization: Forensic requirements often demand pixel- or block-level identification of unauthorized modifications. Localized watermarking supplies fine-grained tamper maps via region-wise voting, error maps, or block-level detection (Hosseini et al., 2021, Zhang et al., 2024, Chen et al., 30 Jun 2025, Bulychev et al., 17 Dec 2025).
• Robustness against Local Attacks: By embedding in spatially diverse or feature-rich subregions, localized schemes can survive geometric, filtering, cropping, or semantic edits that target only part of the content (Jiang et al., 2023, Bulychev et al., 17 Dec 2025).
• Payload and Security: Localized adaptation to high-texture or high-entropy regions expands the permissible watermark payload while minimizing perceptibility (Banitalebi et al., 2018, Jiang et al., 2023).

2. Key Localization Mechanisms and Area Selection Strategies

Region selection—how and where to embed or extract the watermark—is central to localized watermarking. Methods include:

• Semantic/Token-driven Selection: In generative models, cross-attention mediates correspondence between text tokens and image/object regions, enabling direct object-level selection (e.g., fine-tuned text encoder token $W_*$ in LDMs for targeting user-selected objects) (Devulapally et al., 15 Mar 2025).
• User-driven or Mask-guided Embedding: Binary masks derived from user input, semantic segmentation, or object proposals direct the encoder to concentrate watermark energy in prescribed regions. Such mask-based systems permit both user and multi-object selection (Hu et al., 17 Apr 2025, Sander et al., 2024).
• Feature-driven Local Area Extraction: Unsupervised feature points (e.g., Daisy descriptors (Jiang et al., 2023)), high-entropy blocks, or content-aware adaptive strategies identify spatial squares or patches with high embedding capacity, yielding robust and imperceptible watermarking confined to selected local areas (Jiang et al., 2023, Banitalebi et al., 2018).
• Block-wise and Statistical Partitioning: Image partition into non-overlapping blocks (DWT/LL bands, LWT coefficients, or spatial grids) enables per-block embedding with pseudorandom block/region selection by secret keys or content-dependent seeds for fine localization and key-based security (Bulychev et al., 17 Dec 2025, Hosseini et al., 2021).
• Locality-Sensitive Hashing in Embedding Space: In text or embedding services, subspace projection and axis-aligned LSH partitioning of the semantic space randomly assign trigger regions for watermark injection, avoiding direct correlation with original feature dimensions (Yang et al., 17 Nov 2025).
• Coarse-to-Fine Patch Matching: For video, graphical watermarks are decomposed into patches routed hierarchically (coarse frame-level matching then fine spatial localization) to best-matching regions, minimizing perceptual cost per patch (Su et al., 19 May 2025).

3. Embedding and Extraction Methodologies

Localized watermarking methods span classical signal-processing approaches and recent deep learning pipelines:

• Adaptive LSB and Histogram Shifting: Region-adaptive LSB watermarking controls embedding depth via local structural similarity (SSIM) thresholds, optimizing for maximal imperceptibility and capacity per block (Banitalebi et al., 2018). Histogram-shifting schemes pair bins in locally denoised squares for robust, high-capacity embedding in feature-selected regions (Jiang et al., 2023).
• Transform-Domain Localized Embedding: Embedding in selected DWT/LWT coefficients within blocks/subblocks (guided by content or key) strengthens robustness to compression and allows block-wise error localization. Inter-block dependencies or scatterings (via secret keys) further enhance semi-fragility and localization (Hosseini et al., 2021, Bulychev et al., 17 Dec 2025).
• Neural Mask-Guided Encoders and Decoders: Modern encoder–decoder architectures (U-Net, ViT, DenseBlocks) concatenate mask-feature tensors and watermark messages, modulating watermark strength via JND maps. Masked regions receive concentrated watermark signals. Decoder branches simultaneously perform region segmentation and message recovery, with shared or distinct heads (Hu et al., 17 Apr 2025, Sander et al., 2024).
• Cross-Attention Overlay and Token Conditioning: In diffusion/text-to-image models, cross-attention maps enable embedding localized to the support of selected text tokens, leveraging overlays of watermark token attention maps on target object tokens at specified diffusion timesteps (Devulapally et al., 15 Mar 2025).
• Dual-mark or Multi-component Watermarks: Hybrid systems, such as TAG-WM or OmniGuard, inject both a payload (copyright) watermark and a localization or validation map, either as two separate bitstreams or via multi-variate latent representations, providing mutually reinforcing tamper detection (Zhang et al., 2024, Chen et al., 30 Jun 2025).
• Physical-World Localized Watermarking: For robust watermark recovery from photos of arbitrary-shaped, partially visible, curved, or folded physical carriers, a neural locator module segments and geometrically normalizes the watermarked region prior to bit extraction (Lei et al., 2023).

4. Detection, Localization, and Interpretability

Local extraction procedures are often more complex than their global counterparts:

• Region- or Mask-conditioned Extraction: Decoders segment watermarked areas (via mask branches or pixel classification) and then apply message extraction only in detected or user-supplied regions (Sander et al., 2024, Hu et al., 17 Apr 2025).
• Block-wise/Fine-grained Statistical Tests: Wavelet or LSB-based schemes implement per-block hypothesis testing or voting—e.g., count how many DWT LL coefficients of a given block lie in allowed intervals (statistically distinct for watermarked vs unwatermarked blocks) (Bulychev et al., 17 Dec 2025).
• Error/Disagreement Maps and Voting: Semi-fragile methods extract bits at multiple coefficients or blocks, aggregate per-block error via majority or multivariate voting, and construct pixel-level tamper or disagreement maps (Hosseini et al., 2021).
• Cross-modal Correspondence for Extraction: In cross-attention-based diffusion models, the same attention overlays used for targeted embedding inform region selection at extraction time (Devulapally et al., 15 Mar 2025).
• Proactive, Blind Tamper Localization: Extractor networks output pixel-level or block-level tamper/no-tamper masks, trained under differentiable degradation to maximize AUC/IoU even under adversarial or partial edits (Zhang et al., 2024, Chen et al., 30 Jun 2025).
• Localized Detection Maps for User Trust: Interpretable post-hoc methods emit block-wise heatmaps (green/red) denoting statistical watermark presence per region, enabling not only yes/no detection but forensic visualization of watermark distribution and potential tampering (Bulychev et al., 17 Dec 2025).

5. Robustness, Imperceptibility, and Capacity Trade-offs

Localized schemes report high robustness and imperceptibility metrics, with empirical trade-offs:

• Perceptual Quality: Deep learning and adaptive methods, leveraging JND maps, SSIM-local optimization, or GAN/LPIPS objectives, achieve PSNR in the 39–46 dB range and SSIM ≳ 0.98, even under full-resolution or region-specific watermarking (Sander et al., 2024, Hu et al., 17 Apr 2025, Jiang et al., 2023, Banitalebi et al., 2018).
• Robustness to Local and Global Attacks: Localized approaches retain bit accuracy >95% under geometric/valuemetric distortions, inpainting, in-region cropping, and JPEG compression; cropping/erasing up to 30–50% area often preserves region-wise TPR >90% (contingent on non-removal of all embedded regions) (Bulychev et al., 17 Dec 2025, Jiang et al., 2023, Devulapally et al., 15 Mar 2025, Sander et al., 2024).
• Localization Precision: Pixel- or block-level localization achieves IoU or F1 scores >0.85 under significant noise or tampering (Hu et al., 17 Apr 2025, Zhang et al., 2024). Interpretable block-wise detection maintains TPR >75% at FPR≈1% even at 50% image cropping (Bulychev et al., 17 Dec 2025).
• Capacity and Multi-watermarking: Local methods can embed up to 128 bits or more (e.g., 256 bits in TAG-WM (Chen et al., 30 Jun 2025)) by judicious region selection and encoding. Multiple non-overlapping regions can simultaneously harbor distinct messages, each decodable at region-level (Sander et al., 2024).
• Trade-offs: Larger capacity, denser embedding, or highly localized regions raise perceptual distortion or error rates. Small-area/low-texture regions are more vulnerable to erasure or detection errors (Hu et al., 17 Apr 2025, Sander et al., 2024).

6. Contemporary Directions and Modality-specific Extensions

• Generative Models and Diffusion: Embedding and extracting watermarks during diffusion (text-to-image/video) generation supports real-time, object-level authorship tracing with minimal parameter overhead and high cross-model compatibility (Devulapally et al., 15 Mar 2025, Su et al., 19 May 2025, Chen et al., 30 Jun 2025).
• Physical and Multi-modal Localization: Neural locator modules enable spatially robust extraction from photographs with arbitrary viewing conditions. The extractors are trained to recognize, crop, and geometrically normalize non-rectangular, curved, or occluded watermarked objects (Lei et al., 2023).
• Audio Localized Watermarking: Sample-level watermark/detector architectures allow frame-by-frame attribution for synthetic or cloned speech and music, exploiting perceptual masking and time-frequency sound structure (Roman et al., 2024).
• Text and Embedding Space Watermarking: Partitioning text embedding spaces allows for robust, region-triggered watermark injection—and resilient copyright protection against paraphrasing, fine-tuning, and model extraction (Yang et al., 17 Nov 2025).
• Post-hoc and Interpretable Schemes: Statistical block-level methods provide detection maps suitable for forensics and user trust, facilitating rapid, key-driven analysis after-the-fact without model retraining (Bulychev et al., 17 Dec 2025).

7. Current Limitations and Open Issues

While localized watermarking has demonstrated state-of-the-art performance in capacity, robustness, imperceptibility, and localization, several limitations remain:

• Region Removal and Content-specific Failure: If all embedded regions are lost (erased, overwritten, or undetectable), payload recovery and localization are impossible (Jiang et al., 2023, Bulychev et al., 17 Dec 2025).
• Parameter Sensitivity: Choice of block size, JND threshold, mask schedule, and region sampling significantly impacts the imperceptibility vs capacity vs robustness triad and requires careful tuning per application (Banitalebi et al., 2018, Hu et al., 17 Apr 2025).
• Computational Overhead: Complex region-adaptive extraction, block-wise statistical analysis, or repetitive DWT/LWT transform operations increase per-image computational cost, though inference-optimized deep models and block-parallel designs mitigate this (Bulychev et al., 17 Dec 2025, Hosseini et al., 2021).
• Security and Privacy: Key management for region selection is critical for resilience to forgery and removal. Some neural approaches are vulnerable to white-box attacks if the detection architecture or secret is compromised (Roman et al., 2024, Yang et al., 17 Nov 2025).
• Generality Across Modalities: While state-of-the-art for images, extension to high-fidelity video, long-form audio, and cross-modal (e.g., image–text) embedding remains an active area of research (Su et al., 19 May 2025, Roman et al., 2024).

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Localized watermarking constitutes a highly active research domain with applications in copyright, tamper localization, forensic analysis, and secure provenance across digital modalities. Techniques continue to rapidly evolve, adapting classical statistical signal processing and deep learning frameworks for improved robustness, imperceptibility, interpretability, and flexibility in region-wise embedding and detection (Devulapally et al., 15 Mar 2025, Jiang et al., 2023, Hu et al., 17 Apr 2025, Sander et al., 2024, Bulychev et al., 17 Dec 2025, Hosseini et al., 2021, Chen et al., 30 Jun 2025, Zhang et al., 2024, Yang et al., 17 Nov 2025, Lei et al., 2023, Banitalebi et al., 2018, Roman et al., 2024, Su et al., 19 May 2025).