markup-detection

Markup Detection

When to Use

• Assess whether images or video show signs of tampering, compositing, or inconsistent capture metadata
• Review documents (Office, PDF) for hidden markup, revision layers, comments, or undisclosed edits
• Run metadata and provenance checks—EXIF/XMP, file hashes, embedded timestamps, export vs capture mismatches
• Triage synthetic media and deepfake concerns using observable workflow indicators (not model training)
• Interpret watermarks, C2PA / content credentials, and publisher attestation claims at a high level
• Produce investigation memos with confidence tiers, evidence tables, and stated limitations
• Compare source chain (original upload, reposts, screenshots) when authenticity is disputed

When NOT to Use

• Train, fine-tune, or benchmark detection ML models → ml-research-engineer-safeguards
• Design cryptographic watermarks, steganography, or signing schemes → cryptographer-specialist
• Perform full digital forensics with chain-of-custody, disk imaging, or courtroom expert testimony → specialized forensics vendor / digital-forensics-analyst (when installed)
• Conduct authorized offensive security or adversarial AI red teaming → ai-redteam, penetration-tester
• Map findings to audit attestations, SOC 2 opinions, or control effectiveness → auditor, compliance-engineer
• Blockchain-only address tracing, sanctions, or on-chain attribution → blockint / investigation skills unless user adds document/media context
• Issue legal conclusions on fraud, defamation, or admissibility → counsel; state facts and uncertainty only

Related skills

Need	Skill
IAM, logging, DLP, and control implementation	information-security-engineer
Adversarial testing of LLM apps and copilots	ai-redteam
Crypto signing, PKI, and watermark protocol design	cryptographer-specialist
Internal audit, evidence standards, deficiency write-ups	auditor
Technical control evidence and CCM pipelines	compliance-engineer
Production LLM features and RAG (not media forensics)	ai-engineer
AI governance and risk tiers for synthetic content policy	ai-risk-governance
Disk/memory/log forensics and chain of custody	digital-forensics-analyst (when installed)

Core Workflows

1. Intake and scope

1. Identify asset type (image, video, audio, PDF, Office, email archive, web capture)
2. Record claimed provenance (author, date, device, platform, original URL)
3. List decision needed (internal triage, incident response, press review, litigation support prep—not legal advice)
4. Note constraints (only copies available, re-encoded social video, missing originals)
5. Select reference files from the table below

Topic	Reference
Scope and boundaries	references/markup_detection_scope.md
Visual tampering heuristics	references/visual_media_tampering_indicators.md
Documents, EXIF, hashes	references/document_integrity_and_metadata.md
Synthetic media / deepfakes	references/synthetic_media_and_deepfakes.md
Watermarks, C2PA, credentials	references/watermarks_provenance_c2pa.md
Reporting and limits	references/investigation_reporting_and_limits.md

2. Preserve and catalog evidence

1. Obtain best available originals; avoid unnecessary re-saving that strips metadata
2. Compute cryptographic hashes (SHA-256) per file; record filename, size, received time
3. Capture context screenshots (platform UI, URLs, conversation thread) separately from the asset
4. Document tool versions used for extraction or enhancement
5. If chain of custody matters, route to formal forensics—do not improvise custody from this skill alone

3. Layered analysis

Run checks in parallel where possible; corroborate across layers before strong conclusions.

1. Structural / visual — lighting, shadows, edges, noise, perspective, duplicate regions (see visual reference)
2. Metadata — EXIF/XMP, container timestamps, software strings, GPS/device fields (see document integrity reference)
3. Document markup — tracked changes, comments, hidden text, embedded objects (see document integrity reference)
4. Synthetic-media signals — face boundary, blink cadence, audio-visual sync at workflow level (see synthetic reference)
5. Attestation — C2PA manifest, publisher credentials, visible watermarks (see watermarks reference)

4. Score and report

1. Classify each finding: observed fact vs inference vs hypothesis
2. Assign confidence (High / Medium / Low) per finding with rationale
3. State alternative explanations (heavy compression, beauty filters, legitimate edits)
4. Deliver memo using templates in references/investigation_reporting_and_limits.md
5. Recommend next steps (obtain original, specialist lab, legal review) when confidence is insufficient

Quality bar

• Never present heuristic suspicion as definitive proof of manipulation
• Separate undisclosed editorial markup from malicious tampering when intent is unknown
• Call out re-encoding, screenshots, and platform transcoding as common false-positive drivers
• Cite which checks were not run when tooling or access was missing