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For a Go app that accepts captions and images, use separate automated checks for the caption text and image pixels, then route uncertain or policy-sensitive uploads to a human. Keep each upload unavailable to other users until those checks and your own policy rules are complete. A moderation label is a signal for a decision—not proof that content is safe, lawful, or consistent with its caption.
Why captions and images need separate checks
A caption is text; an image is visual content. Send each to a moderation capability that supports that input type. OpenAI documents text and image classification in its Moderation guide. Google Cloud Vision SafeSearch classifies images into five likelihood categories, while Amazon Rekognition returns image moderation labels in a hierarchy.
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These outputs are not interchangeable taxonomies. A provider’s “adult” or “racy” label does not dictate what your platform must allow, and separate text and image classifications do not establish that the image depicts what its caption claims. The cited documentation does not establish reliable joint reasoning about caption-image consistency; if that matters to your product, treat it as a separate policy check rather than assuming two moderation calls provide it.
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| Service | Documented input and output | Go and workflow evidence |
|---|---|---|
| Google Cloud Vision SafeSearch | Image likelihood values for adult, spoof, medical, violence, and racy content. These are categories, not a universal allow/block policy. | The official SafeSearch documentation includes a Go client example and local-image and remote-URI examples. Google warns that fetching external image URLs can fail and recommends not relying on externally hosted images in production. |
| Amazon Rekognition | Image moderation labels using a hierarchical taxonomy. The service returns a moderation model version. | The moderation API guide documents synchronous image moderation and asynchronous media-analysis operations. The AWS SDK for Go reference documents the Go API. |
| OpenAI Moderation | Text and image classification, with results to review before displaying output or taking downstream action. | The guide documents the modality support and handling guidance. It does not provide a specific Go code sample in the cited material. |
The documentation does not provide a comparable independent benchmark for accuracy, latency, or cost on representative uploads and a shared policy. Choosing among providers on those dimensions requires testing against your own content and requirements.
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Build a staged moderation workflow in Go
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Accept and validate the upload
Check that the request is well-formed and the uploaded file is of an expected type before moderation. Store the submission in a pending or quarantined state so it is not displayed while checks are incomplete. This is an application design recommendation; the cited provider documentation does not prescribe a storage architecture.
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Classify the caption as text
Submit the caption to a text-capable moderation endpoint. Keep the returned categories and scores with the submission when the service provides them. Do not treat a text result as an assessment of the image.
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Classify the image bytes
Send the uploaded image through an image-capable moderation API. Prefer the controlled upload path over a third-party image URL where practical: Google documents remote-URI examples but warns that external image fetching can fail and advises against depending on externally hosted images in production.
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Apply your policy to the results
Define application-specific actions for clear allow, block, and uncertain or review outcomes. Choose thresholds based on your product’s rules and validate them with representative uploads; the provider labels alone do not determine those thresholds. Preserve category and score outputs and, where returned, the model version so decisions can be interpreted later.
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Release, hold, or remove the submission
Make the content available only after the automated checks and policy decision permit it. Hold ambiguous cases for review; block cases your policy disallows. Define explicit behavior for timeouts, provider errors, missing results, and malformed responses—for example, keep the item pending rather than silently treating a failed check as an approval.
When human review belongs in the process
Automated labels can identify content for closer attention, but policy interpretation and appeals may require judgment. Create a human path for uncertain classifications, user reports, and appeals, and give reviewers the relevant caption, image, model outputs, and policy context. Record the resulting decision so that reports and appeals can be handled consistently.
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Amazon describes machine-flagged review as a way to reduce moderator workload and says human moderators may review a smaller set, “typically 1-5% of the total volume.” That is AWS’s generalized description of its workflow, not an independently verified rate or a prediction for your application. Your review share depends on your uploads, policies, and thresholds. AWS also cautions: “Amazon Rekognition isn’t an authority on, and doesn’t in any way claim to be an exhaustive filter of, inappropriate or offensive content.” See the Amazon Rekognition moderation guide.
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These moderation services do not establish that an image is legal or that it contains no illegal material. AWS states that Rekognition moderation does not detect illegal content such as CSAM. OpenAI says its Moderation endpoint is not designed for known or suspected CSAM and must not receive it. Follow dedicated child-safety safeguards and applicable response procedures; these provider moderation guides are not a substitute for them.
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