PRZEWODNIK Społeczny

Chińskie zasady głębokiej syntezy i etykietowanie treści AI

China regulates deepfakes and other synthetic media through two layers of rules.

Posłuchaj tego przewodnikaOpowiadane przez sztuczną inteligencję · ~3 min

Na tej stronie4 minuty czytania
  1. Przegląd
  2. Głębokie nurkowanie
  3. Implementacja w świecie rzeczywistym
  4. The Future of China's Deep Synthesis Rules and AI Content Labeling
  5. Wdrażanie tego w praktykę
  6. Odkrywaj dalej
  7. Często zadawane pytania

Przegląd

The Deep Synthesis Provisions took effect in January 2023. The Measures for Labeling AI-Generated Synthetic Content, in force since September 1, 2025, require both visible labels and hidden metadata labels on AI-generated text, images, audio, video and virtual scenes. Together they make China one of the first jurisdictions to require AI content labeling across the whole distribution chain, from the tools that generate content to the platforms that spread it.

Głębokie nurkowanie

The Provisions on the Administration of Deep Synthesis Internet Information Services were issued in late 2022 by the Cyberspace Administration of China (CAC), the Ministry of Industry and Information Technology and the Ministry of Public Security, and took effect on January 10, 2023. They define deep synthesis broadly as technology that uses deep learning or virtual reality to generate or edit text, images, audio, video or virtual scenes. Providers must verify users' real identities, set management rules, review content, and keep mechanisms for dispelling rumors. When they offer tools for editing biometric information such as a face or voice, they must prompt users to obtain the separate consent of the person being edited. They must also file their algorithms with the CAC. Content that could confuse or mislead the public, such as synthetic faces, cloned voices or realistic scenes, must carry a conspicuous label, and no one may remove or conceal these labels. The 2025 Labeling Measures, issued by the CAC with three other agencies and accompanied by a mandatory national standard, GB 45438-2025, make labeling more specific. Explicit labels are visible or audible cues such as text notices, audio prompts or graphic marks. Implicit labels are embedded in file metadata and include information such as the provider's name and a content ID. Distribution platforms must check uploads for metadata. They display a notice when AI generation is confirmed, and they apply possible or suspected labels when the user declares it or detection suggests it. App stores must check that apps comply. Western approaches are narrower or newer. The EU AI Act's Article 50 imposes transparency and marking duties on AI-generated content and deepfakes, but they apply later than China's. In the United States, deepfake rules are mostly state laws on election ads and intimate imagery, while standards such as C2PA content credentials are voluntary. Labels do not solve detection: metadata can be stripped by screenshots or re-encoding, which is why China layers platform detection and user declarations on top.

Implementacja w świecie rzeczywistym

A face-swap app must add a conspicuous label to its output videos and require users to obtain consent from the person whose face is being edited. It must also verify its users' real identities.

An AI image generator must embed hidden metadata in each file, such as the provider's name and a content identifier. Text generation tools must show visible cues, such as notices at the start or end of generated text or in the interface.

A short-video platform that detects AI-generation metadata in an upload must show a label telling viewers the content is AI-generated. If there is no metadata but its systems detect signs of synthesis, it adds a suspected label.

A creator uploading an AI-made voiceover to a Chinese platform is expected to declare it as AI-generated using the platform's labeling tool, and must not strip existing labels.

The Future of China's Deep Synthesis Rules and AI Content Labeling

Enforcement of the 2025 Labeling Measures is still new, and it is not yet clear how consistently platforms will detect unlabeled content or how regulators will treat false positives. Watermarking technology keeps improving, but no method yet survives every edit, so labeling will probably continue to rely on several signals together. As the EU's transparency obligations take effect and more countries consider labeling rules, technical interoperability between China's metadata standard and schemes like C2PA may become a practical question for global platforms.

Wdrażanie tego w praktykę

Dlaczego to ma znaczenie

  • Ryzyko i bezpieczeństwo. Zarówno katastrofalne, jak i codzienne szkody spowodowane sztuczną inteligencją zależą od tego, kto rozumie ryzyko i kto może podjąć działania.
  • Jaśniejsze decyzje. Umiejętność korzystania z usług publicznych i zawodowych wpływa na to, czy silna polityka bezpieczeństwa jest politycznie możliwa.
  • Przebijanie się przez szum. Jasne wyjaśnienia ograniczają wpływ szumu, PR laboratoryjnego i niejasnego teatru etycznego.

Uważaj na

  • Traktowanie ryzyka egzystencjalnego jako science-fiction, choć łączy w sobie możliwości.
  • Mylenie bezpieczeństwa produktów powierzchniowych z wyrównaniem przy dużej autonomii.
  • Pozostawienie odbiorcom nieanglojęzycznym i nieeksperckim jedynie źródeł o niskiej jakości.

Kolejne kroki

  1. Oddziel ryzyko szkód, niewłaściwego użycia i utraty kontroli/niewspółosiowości produktu.
  2. Zapytaj, jakie dowody zmieniłyby Twój pogląd na temat terminów i dotkliwości.
  3. Przedkładaj źródła pierwotne i konkretne oceny nad twierdzenia marketingowe.
  4. Zidentyfikuj jedną ścieżkę działania: karierę, politykę, finansowanie lub umiejętności – nie tylko świadomość.

Odkrywaj dalej

Darmowy newsletter

Otrzymuj codzienne podsumowanie AI

Trzy zweryfikowane historie AI każdego poranka w dni robocze, napisane prostym angielskim. Bezpłatne na zawsze, bez reklam.

Jedno e‑mail każdego dnia roboczego. Wypisz się jednym kliknięciem. Nigdy nie sprzedajemy ani nie udostępniamy Twojego adresu.

Sprawdź się

Take the China's Deep Synthesis Rules and AI Content Labeling quiz

Natychmiastowa informacja zwrotna na każde pytanie oraz udostępnialny certyfikat z weryfikowalnym ID po zaliczeniu kursu.

Rozpocznij quiz

Wspieraj darmową edukację AI. AI Understanding jest organizacją non-profit 501(c)(3) — bez reklam, bez płatnej ściany, zawsze. Przekaż darowiznę

Często zadawane pytania

What is China's Deep Synthesis Rules and AI Content Labeling?

China regulates deepfakes and other synthetic media through two layers of rules. The Deep Synthesis Provisions took effect in January 2023. The Measures for Labeling AI-Generated Synthetic Content, in force since September 1, 2025, require both visible labels and hidden metadata labels on AI-generated text, images, audio, video and virtual scenes. Together they make China one of the first jurisdictions to require AI content labeling across the whole distribution chain, from the tools that generate content to the platforms that spread it.

When did China's Deep Synthesis Provisions take effect?

The Deep Synthesis Provisions were issued in late 2022 and took effect on January 10, 2023, before the generative AI Interim Measures.

When did the Measures for Labeling AI-Generated Synthetic Content take effect?

The Labeling Measures, together with the mandatory national standard GB 45438-2025, took effect on September 1, 2025.

What is an implicit label under China's labeling rules?

Implicit labels are embedded in metadata and are not directly visible. Explicit labels are the visible or audible cues shown to users.

What should a platform do when an upload has no AI metadata but its detection suggests synthesis?

Platforms use tiers. Confirmed content with metadata gets a clear AI-generated notice, and content flagged by detection gets a possible or suspected label.

Under the Deep Synthesis Provisions, what must be obtained before a user edits another person's face or voice?

Providers offering biometric editing tools must prompt users to obtain the separate consent of the person whose face or voice is being edited.