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How to Check Whether Generative AI Copied Your Game’s Art, Code, or Writing

By Freedom251 Team · · 11 min read

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There is no universal detector that can prove a generative-AI system copied a particular game asset. A defensible investigation is an evidence chain: preserve your original files and their history, capture the suspected output exactly as found, compare distinctive expression separately for art, code, and writing, inspect provenance metadata, document access and dates, and obtain legal advice before alleging infringement or sending a DMCA notice.

What you can and cannot prove

An AI-generated image that resembles your game, a code completion that contains one of your functions, or dialogue that echoes a rare line can justify investigation. None of those observations alone proves that a model trained on your work, that a provider had access to it, or that the output infringes your rights. The same visual style may arise from genre conventions; common code patterns may be independently written; and a short phrase may not be protectable expression.

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Keep three questions separate:

  • Similarity: Does the output share distinctive expression, structure, or wording with your work?
  • Provenance: Is there reliable evidence about where the output came from, how it was generated, or what files a system accessed?
  • Legal consequence: Does the similarity involve protected expression, violate a license or contract, or satisfy the elements of an infringement claim in the relevant jurisdiction?

A strong case connects all three with dated, reproducible records. A detector score, a screenshot, or a subjective impression is only one lead in that chain.

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Freeze the evidence before you investigate

Investigation can change files, web pages, repository state, and metadata. Make preservation your first operation, not something you do after comparing results.

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  1. Create read-only copies. Preserve layered art files, source repositories, build artifacts, scripts, dialogue databases, design documents, and dated drafts. Keep the working originals untouched and store the copies in a location where later edits are controlled.
  2. Record cryptographic hashes. Generate a hash for each preserved file and record the algorithm, value, filename, and time captured. Re-hashing later lets another person verify that the file presented as evidence is the same file you preserved.
  3. Export history. Save repository commits, branch information, file history, contributor names, changelogs, build numbers, editorial notes, and publication dates. A version-control record can establish when a work existed and who contributed to it.
  4. Capture the suspected output in its original form. Download the original image, archive the exact text response, or save the complete code output. Do not rely only on a compressed screenshot or a copied excerpt. Record the service or application, account or session context when available, prompt or input, model label, settings, and the date and time.
  5. Document location and access. Save the page URL, repository URL, post or listing identifier, screenshots showing surrounding context, and any access logs available to you. Note whether the output was public, behind a login, supplied by a contractor, or obtained from a test account.
  6. Preserve related licenses and agreements. Keep the license for every source file, asset pack, font, library, contractor agreement, and platform term that could affect ownership or permitted reuse.

Use a dated timeline to connect the records: creation of your work, publication or private access, discovery of the suspected output, every download or comparison, and any communication with the provider or uploader. A timeline prevents later confusion about which version was available at a particular date.

Keep art, code, and writing as separate investigations

The three media types produce different evidence and raise different ownership questions. A visual resemblance is not tested like a duplicated function, and a language-model response cannot be evaluated with an image detector. Make a separate evidence folder and comparison report for each medium.

Medium High-value similarity signals Records that strengthen the comparison Questions that remain separate
Art Rare composition, character silhouette, unusual texture defects, distinctive color script, or matching interface layout Original layered files, pixel dimensions, hashes, side-by-side images, reverse-search results, and dates Whether the feature is protectable expression rather than a genre convention or idea; who owns any underlying assets
Code Exact or near-duplicate snippets, unusual identifiers, comments, control-flow structure, error messages, or distinctive bugs Repository history, commits, authors, licenses, generated-output capture, and notice or attribution checks How a model obtained the code, whether a license was satisfied, and whether an exception such as fair use applies
Writing Rare phrases, invented names, unusual metaphors, sentence sequences, and nonfunctional creative choices Dated drafts, editorial notes, prompts or logs, complete output, and a passage-by-passage comparison Whether a short or common phrase is protectable; effects of translation, rewriting, or independent creation

Do not combine weak signals from different media into a single “AI percentage.” A model could produce a similar color palette while generating wholly different code and text, and each result would need its own analysis.

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Check provenance metadata, but understand its limits

C2PA Content Credentials are signed, tamper-evident manifests that can record creation and editing actions, including declared AI use. If a file carries a valid credential, preserve the manifest and the verification result with the file. It can show what the signer stated about the file’s history and whether the signed record was altered after signing.

A credential is not a verdict on ownership or infringement. It does not establish that every statement in the manifest is true, that the signer owned the source material, or that a particular training example produced the output. A missing credential is also inconclusive: metadata can be stripped by a platform, export process, screenshot, or ordinary file conversion.

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OpenAI describes its provenance checker as a provenance aid rather than a general AI detector. It does not identify every output from every provider. Treat any checker result as one record in your file, not as proof that a specific work was or was not used.

When examining provenance:

  • Save the original file before opening or re-exporting it in an editor.
  • Record the manifest, signer, timestamps, declared tools, and any verification errors.
  • Compare the manifest’s claims with independent records such as repository commits, invoices, or prompt logs.
  • Note whether a platform resized, recompressed, or stripped metadata before you obtained the file.

How to investigate suspected copying of game art

1. Identify distinctive expression

Start with features that are unlikely to be produced by the same broad genre brief. Compare the arrangement of foreground and background elements, a character’s silhouette and proportion, an unusual prop combination, a repeated interface layout, a specific color progression, and distinctive texture defects. A generic “dark fantasy” palette or a standard health-bar shape is weak evidence; an uncommon arrangement of many expressive details is stronger.

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2. Make a controlled visual comparison

Place the original and suspected output side by side at their native pixel dimensions. Mark matching contours, object positions, asymmetries, and repeated errors. Keep an unannotated pair as well as an annotated working copy. Record the files’ hashes and the date of each capture so another reviewer can reproduce the comparison.

3. Use searches as leads, not conclusions

Reverse-image search and image-similarity software can locate earlier uploads, derivatives, or visually close results. Save every result page, query image, ranking, and date. Search results change, and a high similarity score may reflect a shared stock asset, a common reference, or a broad style rather than copying. The output of a search is therefore investigative evidence that needs human interpretation.

4. Test alternative explanations

Check whether the matching feature is a genre convention, an idea, a standard software component, or an asset available under a permissive license. Compare dates and access: a creator who published the same image before your private asset was available may have an independent source. Conversely, an unusual combination that appears only after your work was publicly accessible deserves closer review.

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5. Preserve the complete context

Keep the full image, not just the cropped matching region; the prompt or brief if available; the page where it appeared; all metadata; and your comparison notes. If the output was edited after generation, preserve each version and identify which features came from the human editor.

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How to investigate suspected copying of source code

1. Compare exact and unusual material first

Search for verbatim or near-verbatim blocks, unusual variable and function names, comments with idiosyncratic wording, distinctive control-flow choices, custom error messages, and bugs that are unlikely to be independently repeated. A common loop or standard library call has little evidentiary value by itself. A sequence of uncommon identifiers, comments, and the same rare bug is more probative.

2. Reconstruct authorship and availability

Use repository history to show who wrote each relevant line and when it existed. Preserve commits, pull requests, issue discussions, and release archives. Establish whether the code was public, supplied to a vendor, present in a private repository accessible to the suspected user, or never disclosed outside your team.

3. Read the source license literally

Identify the license attached to the matching project and check its conditions for attribution, notices, redistribution, source disclosure, and modification. Inspect the suspected output or repository for required notices. A match can support a copying theory while still leaving a license-compliance dispute rather than a straightforward infringement claim.

4. Do not infer the training path from the match

Duplicated code does not reveal whether a model trained on your repository, retrieved it at generation time, received it in a prompt, or was copied by a human after generation. Preserve logs and access records that could distinguish those possibilities. Keep the technical similarity analysis separate from the question of how the code reached the model.

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5. Capture generated code reproducibly

Save the full response, not only the matching function. Record the prompt, model name or version displayed by the service, temperature or other settings when exposed, tool calls, and the exact time. If a completion changes between runs, preserve each run and label it; non-reproducibility does not erase a match, but it affects how confidently another reviewer can repeat the test.

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How to investigate suspected copying of dialogue or lore

1. Look for rare creative choices

Compare invented character and place names, unusual metaphors, distinctive world-building rules, rare phrases, and the order of several sentences or beats. Functional facts, clichés, and short everyday expressions are weaker indicators. A long sequence of uncommon choices that appears in the same order is more meaningful than a shared theme.

2. Preserve the writing’s development history

Keep dated drafts, planning notes, editorial comments, localization files, and approvals. These records can show independent human authorship and how a passage evolved. If you used a model during development, preserve the prompts and outputs so a reviewer can distinguish your instructions and edits from any alleged source text.

3. Treat text-detector scores as probabilistic

Text detectors cannot identify a source work on their own. Substantial rewriting, translation, short passages, and ordinary genre language can reduce the reliability of provenance signals. Use a detector, if at all, to flag passages for manual comparison; never present a percentage as proof that a model copied your script.

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4. Compare complete passages and context

Save the entire suspected response and the surrounding conversation. Compare sentence sequences, names, metaphors, and nonfunctional choices in context. Note edits made by a human after generation. A few matching words may be unavoidable, while a coherent sequence of rare phrases may warrant escalation.

Choose investigation tools by evidence quality, not an “AI percentage”

When evaluating a search, similarity, repository, or provenance tool, compare the capabilities that affect an auditable case:

Capability Questions to ask before using the result
Medium coverage Does it analyze images, repositories and code, text, or only one medium?
Matching method Does it find exact duplicates, near duplicates, semantic similarity, or stylistic resemblance? Are the thresholds documented?
Auditability Can you export a report containing the query, result, timestamp, settings, and file identifiers?
Provenance support Can it preserve or verify signed manifests and distinguish a missing credential from a negative finding?
Privacy and upload terms Will your unpublished art, source, or script be retained, used for training, or disclosed to another party?
Hashes and timestamps Does the report include exportable hashes or trusted timestamps so another reviewer can verify the examined files?
Independent reproduction Can a second investigator rerun the same query with the same inputs and understand why the result appeared?

Keep the raw input and exported report together. A vendor’s changing index, model, or scoring method can make a later result differ, so record the tool version or service label and the date of the search.

What U.S. copyright law changes about your conclusions

The U.S. Copyright Office’s January 29, 2025 Part 2 announcement says: “It concludes that the outputs of generative AI can be protected by copyright only where a human author has determined sufficient expressive elements.” The same announcement explains that protection can include “situations where a human-authored work is perceptible in an AI output, or a human makes creative arrangements or modifications of the output, but not the mere provision of prompts.”

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That guidance addresses copyrightability of an output, not a universal test for whether a model copied a training work. Human creative contribution, ownership of the underlying material, access, substantial similarity, contracts, licenses, registration, and jurisdiction can all affect a dispute. The Copyright Office’s training report is a pre-publication analysis of training, licensing, and liability questions, not a final rule.

Section 107 of Title 17 lists criticism, comment, news reporting, teaching, scholarship, and research among purposes considered in a fair-use analysis, along with the other statutory factors. Fair use is fact-specific; it is not a blanket answer for model training or for reusing an AI output. The commercial context, amount and substantiality, market effects, transformation, and the particular works and uses must be assessed together.

Other countries may apply different copyright, database, moral-rights, privacy, or notice rules. A contract with a publisher, platform, contractor, or model provider can also change the practical analysis. Do not treat a U.S. framework as a worldwide conclusion.

Build an escalation packet before contacting anyone

Organize the material so counsel or a platform reviewer can understand it without repeating your investigation:

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  • Read-only originals and their cryptographic hashes
  • Repository commits, dated drafts, contributor records, and publication history
  • Licenses, attribution files, contracts, and relevant platform terms
  • The suspected output in its original format, with its URL, account or post identifier, capture date, and surrounding context
  • Provenance manifests and verification reports, including any metadata-stripping observations
  • Side-by-side art comparisons or code and text diff files with annotations separated from the originals
  • Search results, tool reports, settings, versions, and timestamps
  • Access logs and a chronological timeline

Ask qualified copyright counsel to review this packet before sending a DMCA notice, platform complaint, demand letter, or public accusation. A notice that overstates what your evidence proves can create avoidable disputes, especially when the output may have an independent source, a license, or a substantial human modification.

Common mistakes that weaken an otherwise good case

  • Relying on one detector: No detector proves copying of a particular game asset.
  • Ignoring file history: A final export without drafts, commits, or hashes makes authorship and timing harder to establish.
  • Confusing style with expression: A genre look, trope, palette, or idea may not be protected expression.
  • Assuming missing metadata is exculpatory or incriminating: Credentials can be removed, and their absence proves neither human authorship nor AI use.
  • Publishing an accusation before verification: Preserve the evidence and obtain legal review first.
  • Uploading confidential material to an unknown comparison service: Check retention, training, privacy, and disclosure terms before submitting unpublished work.
  • Mixing media analyses: Similar art, code, and writing require separate tests and separate explanations.

A concise decision path

  1. Preserve: Freeze originals, hashes, history, licenses, and the exact suspected output.
  2. Classify: Decide whether the issue is art, code, writing, or several independent issues.
  3. Compare: Mark rare expressive details, exact code features, or unusual language; test ordinary and licensed explanations.
  4. Verify provenance: Check signed credentials and logs while recording what their presence or absence cannot establish.
  5. Document: Export reproducible tool reports and maintain a dated timeline.
  6. Escalate carefully: Have counsel assess ownership, access, license terms, jurisdiction, infringement, and fair use before any notice or public claim.

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