How Does AI Image Detection Work? From Provenance Credentials to Pixel Review

What actually happens after you upload an image to an AI detector?

Many people assume that the service sends the picture to a single model and returns a result such as “82% likely to be AI-generated.” That describes only one possible stage. A more defensible approach looks for direct provenance evidence first and uses model inference only when that evidence is unavailable.

ShanHaiYin currently follows this workflow: original file and SHA-256 fingerprint → trusted C2PA Content Credentials → GB 45438-2025 AIGC metadata → primary model analysis → pixel-level review when needed → combined report.

The principle is straightforward: verify direct source evidence when it exists; use probabilistic inference when it does not.

The process begins by calculating a SHA-256 fingerprint for the file that the user actually uploaded.

SHA-256 converts a file into a fixed-length digest. If even one byte changes, the recalculated fingerprint will normally be different. In a detection report, it answers a narrow but important question: “Does this report correspond to the same file I submitted?”

It does not determine whether the image was generated by AI. It cannot establish when the image was created, who created it, whether the depicted event is real, or whether its use is lawful. Its purpose is to bind the report to a particular set of file bytes.

For a large upload, the system may create a copy that fits a detection service’s input requirements. The SHA-256 value shown in the report still refers to the original uploaded file, not to that temporary analysis copy.

The system next checks whether the image contains Content Credentials based on the C2PA standard.

C2PA data is not merely an editable note saying where an image came from. A valid Content Credential can be cryptographically bound to an asset and may contain information about the signer, the tool used, and actions performed during creation or editing.

ShanHaiYin does not classify an image from the letters “C2PA” alone. The validation process asks:

• Is there a readable active credential associated with the current file?
• Does the digital signature validate?
• Does the signing certificate connect to a recognized trust chain?
• Does the credential describe camera capture, conventional editing, algorithmic enhancement, or AI generation?
• Is the credential still correctly bound to the current asset?

Only when the credential validates and its recorded actions explicitly indicate AI generation or algorithmic enhancement can it serve as direct evidence of that origin. An ordinary camera photograph may also contain a valid C2PA credential, so the presence of Content Credentials does not automatically mean AI generation.

C2PA also does not decide whether the event shown in an image really happened. It validates provenance information and integrity; it does not make a value judgment about whether the depicted content is true.

If C2PA does not provide decisive provenance evidence, the system checks for AIGC metadata defined by GB 45438-2025, China’s national standard for labeling AI-generated and synthetic content.

Supported metadata may include the content attribute, producer or service provider, content identifier, and related information:

• Label=1: the file explicitly declares itself to be AI-generated or synthetic content, so the declaration can be reported directly;
• Label=2 or Label=3: the file indicates possible or suspected generated content, so it remains a risk signal and the image continues to model analysis.

A file declaration and a verified digital signature are not the same thing. GB 45438-2025 provides standardized labeling information. C2PA can additionally involve cryptographic signatures, asset binding, and certificate-chain validation.

China’s Measures for Labeling AI-Generated and Synthetic Content distinguish explicit labels users can perceive from implicit labels stored in file data. Both the measures and GB 45438-2025 took effect on September 1, 2025. For international readers, this is jurisdiction-specific standardized metadata, not a universal label attached to every AI-generated image worldwide.

Many images online have no trusted Content Credentials. Metadata may disappear when an image is screenshotted, cropped, compressed by a social platform, or exported through another application. Failure to find provenance data does not prove that the image was made by a human; it only means that this layer produced no usable result.

At this point, ShanHaiYin uses its primary detection layer. The model examines statistical patterns associated with generation, synthesis, or algorithmic editing, including relationships among local textures, fine-detail distribution, edges, lighting, and broader pixel patterns.

The score represents how strongly the current input matches a category, or the model’s confidence in its current label. It is not the detector’s accuracy across all images, and it does not mean that a corresponding percentage of the picture was created by AI.

New generators, heavy compression, low resolution, screenshots, crops, filters, and later editing can all change observable features. Real photographs processed with denoising, sharpening, background blur, or computational photography may also display patterns that resemble generated imagery.

If the primary analysis produces a sufficiently clear result, the system does not automatically run another detector merely to collect more scores. A second pixel-level review is used when the primary result falls into an ambiguous range or when the primary service is temporarily unavailable.

The purpose is not to claim that “two models agree, therefore the result is 100% certain.” It reduces the risk of treating one model output as the whole answer. When the layers conflict, or when input quality is too poor, the better outcome is to preserve uncertainty and recommend checking the original file, source, and context.

The final conclusion is not a simple average of several numbers. ShanHaiYin organizes evidence by type and strength:

1. A credential that passes signature and trust-chain validation and records an AI-generation action takes priority as direct provenance evidence.
2. GB 45438-2025 metadata is interpreted according to its declaration level, with explicit and suspected labels handled differently.
3. When no direct evidence is available, the result relies on the primary model and, where needed, pixel-level review.
4. When evidence is insufficient or conflicting, the report retains room for additional verification.

The report then presents the main conclusion, supporting grounds, a request ID, and the SHA-256 file fingerprint. The request ID identifies the service call, while the fingerprint identifies the submitted file. Neither is a forensic case number or a judicial certification.

Human inspection may reveal malformed text, repeated textures, structural inconsistencies, or implausible lighting. But modern generated images do not necessarily contain obvious mistakes, and real images can look unusual after aggressive editing or compression.

A single model cannot cover every generator, editing workflow, and distribution path. NIST’s overview treats provenance records, labeling, watermarking, and detection as complementary technical approaches, not one universal solution.

The practical goal is to establish an evidence order: look for verifiable provenance first, read standardized declarations next, use model inference when needed, and document the file and the limits of the analysis.

A strong AI-generation signal does not automatically prove that a caption is false, that the publisher intended to deceive, or that a particular person created the image. Conversely, the absence of a strong signal cannot guarantee that the image was never processed with AI.

ShanHaiYin does not claim to detect every platform’s proprietary invisible watermark, nor does it present itself as an official verifier for private watermark systems. A provenance signal is included only when the system can actually read or validate it.

A report is useful for initial screening, risk assessment, and follow-up verification. It does not replace forensic examination, human review, or a final determination of fact. For consequential decisions, preserve the original file, source page, retrieval time, context, and available creation records.

The underlying logic is not simply “does this picture look artificial?” and one score should not decide authenticity. ShanHaiYin moves from direct evidence toward probabilistic inference: fingerprint the file, validate C2PA credentials, read applicable AIGC metadata, run primary model analysis when necessary, request pixel-level review for ambiguous cases, and record both the evidence and its limits.

For the strongest available analysis, upload the original file rather than a screenshot, crop, or re-export. Read a practical AI image verification checklistand learn why cropping makes AI images harder to detect.

After receiving a report, read the supporting grounds alongside the conclusion, request ID, and SHA-256 fingerprint. For important content, continue with source tracing and human verification.