Visible Marking: Techniques and Applications

• Visible marking is a technique that applies explicit overlays and annotations to digital and physical media to guarantee high visibility and easy human interpretation.
• It is used in diverse applications such as compliance labeling in AI-generated content, forensic visualization, educational assessments, and scientific image annotation.
• Techniques incorporate controlled design choices in opacity, placement, and modality-specific implementation, balancing visibility with minimal content distortion.

Visible marking refers to practices and technologies where markings (overlays, annotations, signals, or features) are intentionally made conspicuous to human or automated observers, in contrast to hidden or invisible marking schemes. In diverse research domains, visible marking serves critical roles—compliance labeling, human-in-the-loop annotation, interpretability in AI, forensic visualization, and more. Visible marks can range from imprinted logos on images and regulatory banners in generative content, to overlays in scientific imagery and visually highlighted feedback in educational assessment. Central to visible marking is the guarantee of perceptibility under standard viewing conditions and, in many applications, unambiguous human interpretability.

1. Core Principles and Definitions

Visible marking encompasses techniques in which the mark is designed to be detectable or legible by unaided human inspection or straightforward algorithmic matching. Contrary to hidden watermarks, which prioritize imperceptibility and robustness under adversarial transformations, visible marks achieve compliance and transparency by trading off for maximum clarity, sometimes at the cost of content occlusion or distortion. The operational definition of visibility is domain dependent: for images or video, it denotes overlays that are visually salient; for audio, audible prompts; for text, banners or explicit markup; for neural models, the reconstructability of a human-readable pattern from model parameters or outputs (Li et al., 13 Dec 2025, Krauß et al., 2023).

2. Methods and Implementations Across Modalities

Digital Content and AI-Generated Media

Visible marking for compliance in synthetic media is typically implemented as a post-processing overlay. In systems like UniMark, this involves a modular API that supports marking in image, video, audio, and text modalities by embedding a conspicuous layer—logo, text banner, or audio prompt—onto the underlying signal. The embedding step is parameterized by placement, size, and opacity (for vision), or by timing and amplitude (for audio). For example, an image with a semi-transparent PNG in the lower-right corner (α=0.6) or an audio file prepended with a "This content was generated by an AI model" prompt meets regulatory requirements such as those in the EU AI Act. Formatting, sizing, placement, and signal-processing steps (e.g., color conversion for image overlays) are controlled but the essential goal is maximal visibility (Li et al., 13 Dec 2025).

The overlay mechanism for images can be formally described as: $$f_{\mathrm{embed}}(x,w;p,\alpha)(u) = \begin{cases} (1-\alpha)\,x(u) + \alpha\,w(u-p) & \text{if }u\in p+\Omega_m,\ x(u) & \text{otherwise} \end{cases}$$ where $x$ is the base image, $w$ is the overlay mark, $p$ specifies placement, and $\alpha$ is the opacity.

DNN Ownership and Intellectual Property

In DNN watermarking, traditional invisible schemes encode bits in model weights or outputs, requiring algorithmic extraction and thresholding. ClearMark advances a paradigm shift by embedding visible (“human readable”) marks into model parameters themselves. The scheme involves a dual-branch architecture: the main task is solved in the forward direction, and a transposed branch reconstructs a watermark image from a key. By sharing all parameters and introducing dropout, the mark becomes entangled throughout the network. Verification is achieved through visual inspection of the reconstructed watermark, eliminating the “brittle threshold” problem of prior methods. This approach provides a capacity of up to 8544 bits with <2% drop in task accuracy and demonstrates robustness to fine-tuning, pruning, and even attacker-injection attempts (Krauß et al., 2023).

Forensic and Scientific Imaging

For detecting physical or chemical marks (e.g., fingerprints), visible marking can refer to the process of enhancing otherwise faint residues for human examination. Novel techniques such as the use of columnar thin films (CTFs) exploit nano-scale optical effects to render latent fingermarks highly visible on difficult substrates (CDs, DVDs). Thin CTFs (100–1000 nm) of nickel or chalcogenide glass are grown by oblique-angle vapor deposition, conformally covering ridge–valley patterns. Reflection or interference at the film amplifies perceptual contrast, enabling full marks to be graded at the highest forensic quality levels (UK Home Office grade 4), even for depleted marks aged up to 72 hours (Faryad et al., 2023).

Annotation Tools for Human Inspection

Software frameworks such as Image Marker operationalize visible marking for large-scale, expert-driven image annotation tasks. Using a customizable, GUI-driven interface supporting up to nine mark classes, analysts place marks directly on displayed images, with each placement being logged alongside positional and, where applicable, world coordinate system (WCS) metadata. CSV or JSON logs capture all session activity, while overlays from external mark catalogs can be rendered in contrasting colors for cross-validation or algorithm-human comparison. The system is optimized for high-throughput (thousands of images per session) and provides direct mapping from mark placement to tabular summary (Walker et al., 2 Jul 2025).

3. Evaluation, Trade-offs, and Detection

The primary evaluation criterion for visible marking is legibility—whether humans (or specified detectors) can reliably confirm the presence and interpretation of the mark.

For regulatory overlays (UniMark mode), detection reduces to template matching or keyword/voice prompt search, with 100% identification reported in template-matching evaluations on their benchmark subsets (Li et al., 13 Dec 2025). Content distortion is a secondary concern, rarely penalized so long as legibility is achieved.

In DNN visible watermarking, empirical assessments cover both machine (e.g., SSIM between reconstructed and reference watermark) and human evaluation. ClearMark demonstrates SSIM > 0.4 after severe parameter pruning (with marks still visually apparent to human reviewers), and high robustness under multiple attack vectors (Krauß et al., 2023).

For annotation and forensic applications, self-consistency and granularity (correct localization/class tagging) are emphasized, often in combination with visualization or grading scales (e.g., Home Office 0–4 for fingerprints (Faryad et al., 2023)). Annotation tools facilitate both the placement and downstream analysis of mark distributions.

A summary table of domain-specific evaluation aspects:

Domain	Primary Metric	Human-in-Loop?
Synthetic Content Compliance	Legibility	Yes
DNN Watermark Verification	SSIM, Visual check	Yes/Optional
Forensic Imaging	Grade, Contrast	Yes
Scientific Image Annotation	Mark completion	Yes

4. Application Domains and Use Cases

Visible marking has diverse deployment scenarios:

• Content identification and compliance: Regulatory labelling of AI-generated content for end-user transparency (image, video, audio, text overlays) (Li et al., 13 Dec 2025).
• Model ownership and provenance: Proof of DNN model authorship with human-auditable, visually reconstructible marks resistant to partial erasure (Krauß et al., 2023).
• Automated and manual image annotation: Feature localization and classification in astronomy, bioimaging, and materials science, using tools with efficient mark placement and catalog export (Walker et al., 2 Jul 2025).
• Physical evidence enhancement: Forensic science applications where substrate-foreground contrast must be optically enhanced for latent mark visibility (Faryad et al., 2023).
• Educational assessment: Segment-level, color-coded marking in AI-assisted grading, providing granular visual feedback to students on correctness, error, and missing content (Sonkar et al., 22 Apr 2024).

Further, benchmark datasets such as CeyMo for road marking detection provide multi-format visible annotation and reference metrics, enabling robust method comparison and domain adaptation (Jayasinghe et al., 2021).

5. Methodological Limitations and Challenges

Visible marking methods generally excel in transparency and interpretability but face several inherent tradeoffs and operational constraints:

• Susceptibility to removal: Overlaid visible marks (e.g., banners, logos) can be trivially cropped or blurred, and are not robust against adversarial tampering; enforcement often relies on policy and audit, rather than technical resilience (Li et al., 13 Dec 2025).
• Influence on content fidelity: Increasing overlay opacity or area enhances visibility at the expense of content obstruction or utility (Li et al., 13 Dec 2025).
• Verification: Methods such as ClearMark reduce dependency on unreliable thresholds but may benefit from automated similarity classifiers for large-scale deployments (Krauß et al., 2023).
• Physical implementation: For forensic visualization (CTF), resource requirements (vacuum deposition, substrate handling), batch throughput, and substrate quality may limit scalability and universal applicability (Faryad et al., 2023).
• Annotation standards: Scientific annotation tools depend on well-defined schema and consistent usage for reproducibility and cross-comparison (Walker et al., 2 Jul 2025).
• Semantic limitations: For model-driven visible grading in education, current approaches can misclassify paraphrases or fail in high-context tasks; improvements in paraphrase-robust representations are a forward path (Sonkar et al., 22 Apr 2024).

6. Representative Datasets and Benchmarking

Several public datasets and toolkits anchor research in visible marking:

• CeyMo: 2,887 high-resolution images, 4,706 annotated instances over 11 road marking classes, supporting polygon, bounding box, and pixel-level segmentation annotation. Accompanied by an evaluation script calculating precision, recall, F1, and macro-F1, as well as baselines using both detection and segmentation architectures (Jayasinghe et al., 2021).
• Marking (BioMarking): SME-annotated dataset of 318 student responses over 11 biology questions, labeled at span-level for correct, incorrect, irrelevant, and omitted content, enabling evaluation and development of segment-wise visible feedback systems (Sonkar et al., 22 Apr 2024).
• Image Marker Tool: Software allowing annotation of FITS, TIFF, PNG, JPEG, with mark logs exportable for validation, cataloging, and further analysis (Walker et al., 2 Jul 2025).

These resources establish norms for empirical assessment and facilitate reproducible research in visible marking across domains.

7. Emerging Directions and Future Challenges

Visible marking continues to evolve along technical, regulatory, and methodological axes:

• Integration of combined visible and invisible marks for multilevel ownership or compliance attestation (Krauß et al., 2023).
• Extension to non-image modalities (audio, text, multimodal content), with format-appropriate “locks” and overlay mechanisms (Li et al., 13 Dec 2025).
• Automation of verification and quality assessment at scale, including the use of learned similarity classifiers for DNN model marks (Krauß et al., 2023).
• Expansion of educational marking frameworks to address broader disciplines and longer-form responses, enabled by more semantically robust models and larger annotated corpora (Sonkar et al., 22 Apr 2024).
• Scaling of forensic physical marking techniques to diverse substrates, donors, and environmental factors to meet accreditation and operational field standards (Faryad et al., 2023).
• Ongoing refinement of annotation tools and benchmarks, including WCS-aware placement and high-efficiency logging, to keep pace with the expanding volume and diversity of scientific imaging data (Walker et al., 2 Jul 2025).

Visible marking thus constitutes an essential and growing toolkit for interpretable, transparent, and human-centric annotation and labeling in the contemporary research and application landscape.