Image Moderation 2.0 detects risky image content across multiple scenarios and supports pay-as-you-go and resource plan billing.
1. Introduction to Image Moderation 2.0
Features
The Image Moderation 2.0 API detects image content that violates online regulations, disrupts platform governance, or degrades user experience. It supports 40+ content risk tags and 40+ risk control items. Based on the risk tags and confidence scores returned by the API, you can define custom review or governance actions tailored to your industry standards or platform content policies.
Version comparison
Compared with Image Moderation 1.0, Image Moderation 2.0 offers broader risk coverage, more granular risk labels, and more flexible console configuration options.
Comparison item | Image Moderation2.0 | Image Moderation 1.0 |
Default QPS | Non-large-model version: 100 Large-model version: 50 | 50 |
Risk detection scope |
| Pornography, politically sensitive and terrorist content, text-image violations, inappropriate scenes, special logos, QR codes |
Risk detection labels | 100+ Note For a full list of labels, see the Risk label interpretation table. | 40+ Note For a full list of labels, see label. |
Console |
Note For more information, see the Console operation guide. |
|
Supported image formats | PNG, JPG, JPEG, BMP, WEBP, TIFF, SVG, GIF, ICO, HEIC | PNG, JPG, JPEG, BMP, GIF, WEBP |
Billing | Billed per service. Each service detects multiple risks simultaneously. Cost = Number of images × Number of services Billed separately for the following services:
| Billing is based on threat scenarios (scene). Billed per risk scenario (scene). Cost = Number of images × Number of risk scenarios Billed separately for the following risk scenarios:
|
Services
Image Moderation V2.0 supports the following services:
Scenario | Service | Detected content | Applicable scenarios |
LLM moderation | Image Moderation for Large and Small Model Integration (postImageCheckByVL_global) | Combines large-model and expert-model capabilities to detect pornography, sexually explicit content, politically sensitive content, terrorist content, prohibited content, religious content, flags, advertising traffic diversion, inappropriate content, abuse, and other violations. Returns detailed labels. For a full list of detectable items, see the AI Guardrails Console. | Use this service when high accuracy is required. Recommended as the preferred choice. For more information, see LLM-based image moderation enhanced service. |
LLM moderation (entering the Chinese mainland) | Image Moderation for Large and Small Model Integration_China Version (postImageCheckByVL_ec) | Designed for entering-the-Chinese-mainland scenarios. Combines large-model and expert-model capabilities to detect pornography, sexually explicit content, politically sensitive content, terrorist content, prohibited content, religious content, advertising traffic diversion, and inappropriate content in images. Returns detailed labels. | Use this service when high accuracy is required for content entering the Chinese mainland. Recommended as the preferred choice. |
General-purpose scenarios | Baseline Check (baselineCheck_global) | Detects pornography, sexually explicit content, terrorist content, prohibited content, flags, inappropriate content, abuse, and special elements in images. Includes both visual content and embedded text in 18 languages: Chinese, English, French, German, Indonesian, Malay, Portuguese, Spanish, Thai, Vietnamese, Japanese, Arabic, Filipino, Hindi, Türkiye, Russian, Italian, and Dutch. For a full list of detectable items, see the AI Guardrails Console. | Use this service to detect whether images contain noncompliant or unsuitable content. Apply it to all images exposed to the public internet. |
AIGC scenarios | AIGC image detection (aigcDetector_global) | Detection target: Images. Detection content: Whether images are suspected to be AI-generated or synthesized. | Use this service to determine whether images are AI-generated. Apply it to tag image sources. For more information, see Image Moderation Enhanced 2.0 AIGC scenario detection service. |
AIGC scenarios | AI-Generated Image Detection_Ultimate (aigcDetector_ultra_global) | Detection target: Images. Detection content: Whether images are suspected to be AI-generated or synthesized, whether they are suspected to be composites (including face synthesis), and whether they show signs of AI local editing. | Use this service to determine whether images are forged. Recommended for image forensics, especially when determining whether images have been locally edited by AI. For more information, see Image Moderation Enhanced 2.0 AIGC scenario detection service. |
Use postImageCheckByVL_global for businesses operating outside mainland China. Use postImageCheckByVL_ec for businesses serving users within mainland China.
2. Billing
Pay-as-you-go
Image Moderation 2.0 uses pay-as-you-go billing by default. You are charged only when you call a service. The following table lists the API pricing.
Moderation type | Supported business scenarios (services) | Unit price |
Standard image moderation (image_standard) |
| 0.6 USD per 1,000 calls Note One call to Baseline Check counts as one billing unit. For example, 100 calls cost 0.06 USD. |
Advanced image moderation (image_advanced) |
| 1.2 USD per 1,000 calls Note You are charged for each invocation. For example, 100 invocations cost 0.12 USD. |
Large-model image moderation (China) (image_vl_standard_cn) |
| 0.7 USD per 1,000 calls Note One call to the China mainland LLM image moderation service counts as one billing unit. For example, 100 calls cost 0.07 USD. |
Content Moderation 2.0 pay-as-you-go usage is billed every 24 hours. In the billing detail report, the moderationType field corresponds to the moderation type field described earlier. You can view Billing Details.
Resource plan deduction
If your moderation volume is large or predictable, purchase a resource plan. Larger plans offer lower per-unit costs than pay-as-you-go pricing. For more information, see Purchase Content Moderation 2.0 resource plans.
Content Moderation 2.0 resource plans offset usage of Content Moderation 2.0 and cannot be shared with the Content Moderation 1.0 data transfer plan. The specific offset factors are as follows:
Moderation type | Offset factor |
Standard image moderation (image_standard) | 2: Each successful API call deducts 2 units from the resource plan quota. Note For example, if your resource plan quota is 10 units and you make 1 successful API call, 2 units are deducted, leaving 8 units. |
Advanced image moderation (image_advanced) | 4: Each successful API call deducts 4 units from the resource plan quota. Note For example, if your resource plan quota is 10 units and you make 1 successful API call, 4 units are deducted, leaving 6 units. |
Large-model image moderation for entering the Chinese mainland (image_vl_standard_cn) | 2.34: Each successful API call deducts 2.34 units from the resource plan quota. Note For example, if your resource plan quota is 100 units and you make 1 successful API call, 2.34 units are deducted, leaving 97.66 units. |
After purchase, Image Moderation 2.0 API usage is deducted from your resource plan quota first. When the quota runs out, pay-as-you-go billing applies automatically. Monitor your remaining quota and charges. You can set low-balance alerts in the Resource Plan System in the Alibaba Cloud User Center.
3. Usage
Integration
Image Moderation 2.0 supports SDK integration and native HTTPS integration:
For SDK integration, see Image Moderation 2.0 SDK and integration guide.
For native API integration:
For large-model image moderation, see LLM Image Moderation.
For general image detection, see Image moderation V2.0 API.
For AIGC and image authentication scenarios, see AIGC and Forgery Detection Service of Image Moderation 2.0.
Console operations
Before your first API call, configure your image moderation policy in the AI Guardrails Console.
In the console, you can adjust the detection scope, configure per-business policies, manage custom image libraries and dictionaries, and view call results and usage.
After adjusting your moderation policy, run effect testing in the console to verify the results.
For step-by-step instructions, see the Console