1.0 Introduction, Regulatory Authority, and Scope

This brief provides an analysis of the key provisions within China’s “Measures for Identification of AI-Generated Synthetic Content” (人工智能生成合成内容标识办法). Issued jointly by the Cyberspace Administration of China, the Ministry of Industry and Information Technology, the Ministry of Public Security, and the National Radio and Television Administration, these Measures establish a comprehensive framework for labeling content created or synthesized by artificial intelligence. For any service provider operating within the regulation’s jurisdiction, understanding these obligations is of critical strategic importance, as they define a new compliance landscape for the rapidly evolving field of AI-generated content.

The stated purpose of the Measures, as outlined in Article 1, is to achieve several key objectives:

• Promote the healthy development of artificial intelligence.
• Standardize the labeling of AI-generated synthetic content.
• Protect the legal rights and interests of citizens, legal persons, and other organizations.
• Safeguard the public interest.

The Measures are set to take effect on September 1, 2025.

According to Article 2, the Measures apply to “service providers” whose activities are governed by a set of pre-existing foundational regulations. This scope explicitly includes providers subject to the ‘Internet Information Service Algorithm Recommendation Management Regulations’, the ‘Internet Information Service Deep Synthesis Management Regulations’, and the ‘Generative Artificial Intelligence Service Management Tentative Measures’.

This document will now proceed to break down the foundational definitions established by the regulation, which are essential for understanding the subsequent compliance obligations.

2.0 Core Definitions: The Labeling Framework

A clear understanding of the regulation’s core definitions is essential for effective compliance. The Measures establish a dual framework of explicit and implicit labeling to ensure transparency and traceability of AI-generated synthetic content. This section dissects the official definitions that underpin the entire regulatory structure.

With these core concepts defined, we can now examine the specific operational obligations for service providers who offer AI content generation services.

3.0 Obligations for AI Content Generation Services

This section analyzes the core operational mandates for providers at the point of content creation, establishing the first link in the regulatory chain of custody. These duties apply directly to entities offering AI generation capabilities, such as text-to-image platforms or video synthesis tools.

3.1 Explicit Labeling Mandates

Article 4 of the Measures requires service providers to add a “significant” (显著) explicit label to generated content. The specific placement and nature of this label vary by content modality:

• Text: A text prompt or general symbol must be added at the beginning, end, or another appropriate middle position. Alternatively, a significant prompt can be added to the interactive interface or near the text itself.
• Audio: A voice prompt or audio rhythm cue must be added at the beginning, end, or an appropriate middle position. A significant prompt can also be added within the interactive interface.
• Images: A significant prompt must be added in an appropriate position on the image.
  • Video: A significant prompt must be added to the initial frame and in an appropriate position around the video playback area. It can also be added at the end or in the middle of the video.
• Virtual Scenes: When presenting a virtual scene, a significant prompt must be added in an appropriate position on the initial screen and can also be added at appropriate times during the continuous service.
• Other Scenarios: Other generated synthetic service scenarios must add significant prompt labels according to their own application characteristics.

Crucially, Article 4 mandates that these explicit labels must be maintained and included within the file when content is downloaded, copied, or exported by a user.

3.2 Implicit Labeling and Metadata Requirements

In addition to public-facing labels, Article 5 mandates that service providers must add an implicit label within the content’s file metadata. This behind-the-scenes label acts as a digital fingerprint for traceability.

The implicit label must contain the following essential information:

• Synthetic content attribute information
• The service provider’s name or a designated code
• A unique content number

The regulation also encourages providers to use additional forms of implicit identification, such as digital watermarks, to enhance traceability.

3.3 Exemption for Explicit Labeling and Data Retention

Article 9 provides a conditional exemption that permits a service provider to deliver content without an explicit, public-facing label upon a user’s request. This exemption, however, is contingent upon the provider fulfilling two key responsibilities:

• The provider must clearly define the user’s own legal responsibilities for labeling and usage within the user service agreement.
• The provider is legally required to retain relevant logs, including “providing object information,” for a minimum period of six months.

These provisions shift the focus from content generation to the responsibilities of platforms and services that distribute and disseminate that content to the public.

4.0 Obligations for Content Dissemination and Distribution Platforms

The Measures create a discrete set of obligations for service providers that facilitate the spread and distribution of content. This section establishes a “tiered liability and verification model,” where a platform’s responsibility increases as its awareness of the content’s nature grows. This model applies to social media platforms, forums, and application stores that are critical to the information ecosystem.

4.1 Responsibilities for Content Propagation Services

Article 6 outlines a tiered set of responsibilities for platforms when they handle content that may be AI-generated. The platform must take specific actions based on the available information:

• Implicit Label Detected: If the platform verifies an implicit label within the file’s metadata that identifies it as AI-generated, it must add its own significant prompt near the published content to clearly inform the public of its nature.
• User Declaration: If the metadata contains no label but the user publishing the content declares that it is AI-generated, the platform must add a significant prompt indicating that the content may be AI-generated.
• Platform Detection: If there is no metadata label and no user declaration, but the platform’s own systems detect an explicit label or other “synthetic traces,” it must identify the content as suspected to be AI-generated and add a corresponding significant prompt.

Article 6 also imposes two overarching duties on these propagation services. First, they must provide users with a function that allows them to declare their content as AI-generated. Second, whenever they apply a public-facing notice under the scenarios above, they must also add their own metadata to the file, including the platform’s name or code and a new content ID.

4.2 Requirements for Application Distribution Platforms

Internet application distribution platforms, such as app stores, have specific due diligence obligations under Article 7. They are required to implement a two-step verification process for new and updated applications:

• During the review process, they must require the application service provider to declare whether their service includes AI-generated synthetic content features.
• If the app provider confirms it offers such services, the distribution platform must verify the provider’s supporting materials related to their content identification measures.
  These obligations on platforms and distributors are complemented by rules governing the relationship with, and responsibilities of, the end-user.

5.0 User-Facing Requirements and Prohibitions

Effective compliance requires careful management of the user relationship. The Measures place specific obligations on service providers regarding their user agreements and simultaneously outline the responsibilities and restrictions imposed upon the end-users who create and publish AI-generated synthetic content.

5.1 User Agreement Mandates

According to Article 8, service providers must clearly specify their content identification methods, styles, and other related norms within the user service agreement. Furthermore, they are required to actively prompt users to read and understand these terms, ensuring that users are aware of the labeling framework before using the service.

5.2 User Obligations and Prohibitions on Label Tampering

Article 10 places a primary obligation on the end-user. When publishing AI-generated synthetic content through a network information service, users must proactively declare it and use the labeling functions provided by the platform.

The same article explicitly prohibits any organization or individual from engaging in label tampering. The following actions are forbidden:

• Maliciously deleting, tampering with, forging, or hiding any required content labels.
• Providing tools or services to other parties to enable them to perform these malicious acts.
• Harming the legitimate rights and interests of others through improper identification methods.

These user-centric rules are backed by a broader compliance and enforcement structure that ensures the Measures are implemented effectively.

6.0 General Compliance and Enforcement Framework

The Measures are underpinned by an overarching compliance and enforcement framework that integrates them with existing laws and regulatory processes. This section outlines the general duties and enforcement mechanisms that service providers must be aware of.

The key compliance and enforcement points from Articles 11, 12, and 13 are as follows:

• Adherence to Other Laws: All labeling activities must comply not only with these Measures but also with all other relevant laws, administrative regulations, departmental rules, and mandatory national standards.
• Regulatory Reporting and Assistance: When undergoing official regulatory processes, such as algorithm filing and security assessments, providers must submit materials on their content identification measures. Critically, they must also strengthen the sharing of identification information to provide support and assistance for preventing and combating related illegal and criminal activities, creating an affirmative obligation to assist state authorities.
• Enforcement Authorities: Violations of the Measures will be adjudicated by the relevant government departments according to their designated duties. The responsible authorities include the Cyberspace Administration, Telecommunications, Public Security, and Broadcast and Television departments.

7.0 Strategic Implications and Key Takeaways

The “Measures for Identification of AI-Generated Synthetic Content” represent a significant step in formalizing China’s approach to AI governance. For businesses operating in this space, the regulation introduces a new layer of technical and legal complexity that requires immediate strategic attention.

The key takeaways for compliance are:

• Primary Compliance Burden: The core of the regulation is the dual-labeling system (explicit and implicit) and the tiered liability model for platforms. This creates a chain of responsibility from the point of content generation through to its final distribution, requiring compliance at every step.
• Significant Operational Challenge: The most substantial operational hurdle will be the implementation of robust, automated systems for detecting, verifying, and applying metadata-based implicit labels. Content dissemination platforms, in particular, must develop sophisticated content moderation capabilities to manage their tiered verification duties effectively.
• Key Legal Risk: The prohibitions on label tampering (for both users and tool providers) and the mandatory six-month log retention requirement for unlabeled content present significant legal risks. Failure to comply with these provisions, or with the affirmative duty to share label data with authorities, could result in severe penalties under China’s existing legal framework.
  Ultimately, these Measures should be viewed not as a standalone regulation but as a foundational component of China’s broader AI and data governance architecture. They signal a clear regulatory intent to ensure traceability and accountability for all AI-generated synthetic content, a principle that will likely inform future legislation in this domain.