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There is no universal detector that can prove a generative-AI system copied a particular game asset. A credible investigation is an evidence chain: preserve your original work and its history, capture the suspected output exactly as found, compare the right kinds of similarities for art, code, or writing, inspect provenance metadata, and document access and dates. A copyright lawyer should evaluate that packet before you send a DMCA notice, file a platform complaint, or accuse anyone publicly.
A visual resemblance, a matching code fragment, or a phrase that looks familiar can justify investigation. None, by itself, proves what data a model used, who owns the material, whether a license was breached, or whether an exception such as fair use applies.
What you can—and cannot—establish
Start by separating three questions that are often mixed together:
- Did the output reproduce protected expression? This is a comparison between your work and the generated result.
- How did the system obtain the material? A match may support a copying theory, but it does not by itself reveal whether the material was in training data, supplied in a prompt, retrieved from a service, or produced independently.
- What legal remedy is available? Ownership, contracts, open-source licenses, registration, jurisdiction, access, the particular similarities, and possible defenses all matter.
A style that resembles your game, a common genre layout, or a familiar trope is usually a weak signal. Focus on unusual expressive choices that are unlikely to arise from the same broad idea. Even then, treat the result as a lead until the surrounding history and legal analysis support it.
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Build an evidence chain before investigating
1. Freeze your originals
Make read-only copies of every relevant source before opening or editing files. For art, include layered working files, source textures, animation files, exports, and the build in which the asset appeared. For code, preserve the repository, branches, tags, build scripts, dependencies, and release artifacts. For writing, keep dialogue files, lore bibles, drafts, editorial notes, and approved scripts.
For each item, record a cryptographic hash, repository commit or version identifier, contributor names, applicable licenses, publication or release dates, and the original URL if it was published online. Take screenshots that show the surrounding context, not just the disputed fragment. Keep a simple log of who collected each file and when. Store a second copy in a location where it cannot be silently overwritten.
2. Capture the suspected output exactly as found
Save the generated image, code, or text in its original file format. Do not rely on a copy-and-paste excerpt if a downloadable file, repository snapshot, transcript, or export exists. Record the service or application, account or workspace context when visible, prompt or input, model label if shown, settings, date and time, and the page or file location. Preserve the surrounding interface with screenshots and save the URL. If the output later changes or disappears, your contemporaneous copy is the version that can be examined.
3. Keep an immutable timeline
Make a dated chronology covering creation, revisions, publication, access by contractors or collaborators, release of the suspected output, and every comparison you performed. Repository history, source-control logs, ticket systems, cloud version history, and dated editorial files can establish when a distinctive element existed. A timeline does not prove infringement, but it can clarify authorship, access, and whether an alleged copier could have seen the work.
Use different tests for art, code, and writing
Do not apply an image “AI score” to source code or a text detector to a sprite. Each medium has different similarity signals and different ownership questions.
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| Medium | High-value comparison signals | Evidence to preserve | What a match still does not prove |
|---|---|---|---|
| Game art | Rare composition, character silhouette, distinctive texture defects, UI layout, color script, and other expressive details | Original layers and exports, pixel dimensions, hashes, side-by-side images, reverse-image or similarity-search results | That the model trained on your file, or that a genre convention is protected expression |
| Source code | Exact or near-duplicate snippets, unusual identifiers, comments, control-flow structure, distinctive error messages, and bugs | Repository history, commits, authors, dates, dependency and license records, copied output and its context | How the model obtained the code, or whether a license, fair-use theory, or other defense applies |
| Writing | Rare phrases, invented names, unusual metaphors, sentence sequences, and nonfunctional creative choices | Dated drafts, editorial notes, prompt or model logs, complete generated text, and source history | That a probabilistic text score identifies your work as the source |
Check provenance, but understand its limits
C2PA Content Credentials use signed manifests and tamper-evident data to record provenance such as creation and editing actions, including declared AI use. When a credential is present and validates, it can show what the signer stated about the file’s history. It does not independently establish ownership, the truthfulness of every declaration, or infringement.
Missing credentials are inconclusive. Metadata may be stripped by an export, screenshot, content-management system, or ordinary file conversion. Treat absence as “no credential available,” not as proof that no AI was involved. Preserve the original file before opening it in software that may rewrite metadata, and save a validation report with the file hash.
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Investigating suspected copying of game art
Compare expressive details, not a general style
Place your original and the suspected image at the same scale, then make a second comparison that normalizes size and crop. Look for a combination of details: an unusual character pose, the exact arrangement of background shapes, a distinctive highlight or texture flaw, a nonstandard interface panel, and a particular color progression. One coincidental feature is weak; a cluster of rare, independent choices is more informative.
Separate protectable expression from an idea or genre convention. A fantasy “inventory grid,” a space-shooter palette, or a generic cartoon outline may be expected in the genre. The precise arrangement, selection, and visual treatment can carry more weight than the concept itself. Keep notes that identify each matching element and why it is unusual without claiming that similarity alone proves infringement.
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Use image searches as leads
Reverse-image search and image-similarity software can reveal earlier postings, near duplicates, or other versions. Run the search on the full image and on distinctive crops. Preserve every result page, query image, date, and URL, including “no result” outcomes. Search results are investigative leads, not a court finding; services can miss transformed, low-resolution, private, or newly posted material.
Preserve technical context
Record pixel dimensions, color space when available, file format, layer names, export settings, and hashes for both files. Note whether the suspected image has been resized, denoised, upscaled, translated, or composited. Keep the unmodified download and a working copy for annotations. If the output is embedded in a video or game build, capture the frame, timestamp, surrounding interface, and the build identifier.
Investigating suspected copying of source code
Find exact and near-duplicate material
Begin with distinctive strings rather than broad architecture. Search for unusual identifiers, comments, error messages, literal constants, uncommon function names, and the same sequence of statements. Then inspect control-flow structure, data layout, edge-case handling, and distinctive bugs. A generated answer that contains a rare typo or the same nonessential comment is more probative than one that merely implements a common algorithm.
Keep a comparison that shows the complete surrounding functions, not only highlighted lines. Record whitespace and formatting separately from substantive structure, and note any translation between programming languages. If the suspected output was generated in several steps, preserve each response so the context of the matching fragment is clear.
Use repository history to establish authorship and access
Export commit history showing when the code and distinctive elements were present, who contributed them, and when they were released. Preserve branches and tags relevant to the disputed version. Check contributor agreements, employment terms, third-party components, and the source project’s license. If the output includes required notices or attribution, record whether they appear, are altered, or are missing.
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A code match can support a copying theory, but it does not identify the model’s data source. The same fragment might have been supplied by a user, retrieved from a public repository, generated from a common example, or produced through another route. License compliance and fair-use questions remain separate from the technical comparison.
Investigating suspected copying of dialogue or lore
Compare rare language and sequence
Search for invented names, unusual metaphors, idiosyncratic terminology, and sequences of sentences that would be unlikely to occur independently. Compare complete passages so context, punctuation, and character relationships are visible. Nonfunctional creative choices—such as a peculiar cadence or a deliberately repeated image—can be more informative than plot facts or short functional phrases.
Preserve drafts and model context
Keep dated drafts, editorial notes, writers’ room records, and approved versions. If a prompt, conversation log, or API record exists, save it in a format that preserves ordering, timestamps, and system instructions. Capture the full generated response, including refusals or alternative completions that show how the matching passage appeared.
Text-detector percentages are probabilistic. They cannot identify a particular source work on their own, and substantial rewriting, translation, or a very short passage can reduce the reliability of provenance signals. Use such scores, if at all, as one contextual observation rather than a conclusion.
Distinguish output similarity from training-data claims
Seeing your character, function, or line in an output does not establish that the model trained on the corresponding file. Training data may be unavailable, filtered, licensed, user-supplied, or changed over time. A model can also reproduce material after a prompt, retrieval step, fine-tune, or direct upload that has nothing to do with pretraining.
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To make a training-related allegation responsibly, document the output and its provenance, the dates and public availability of your work, plausible access routes, and any repeated or unusually specific generations. Avoid presenting a stylistic resemblance as proof that a particular file was in a training set. The underlying training, licensing, and liability questions are fact-dependent and may require discovery or other evidence unavailable to an individual developer.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Choose investigation tools by evidence quality
Compare tools by what they can document, not by a headline “AI percentage.” Before uploading confidential assets, read the service’s privacy and retention terms and determine whether it permits reuse for training or product improvement.
| Capability to compare | Questions to ask | Why it matters |
|---|---|---|
| Medium coverage | Does it handle images, repositories/code, text, or only one medium? | A tool outside the relevant medium cannot answer the question you have. |
| Matching method | Does it find exact duplicates, near duplicates, semantic similarity, or visual features? | Exact and semantic matches support different inferences and have different false-positive patterns. |
| Auditable report | Can you export the inputs, settings, result, timestamp, and tool version? | An exportable record lets counsel or another examiner reproduce the comparison. |
| Provenance support | Can it validate signed credentials or preserve metadata without rewriting it? | History and integrity evidence are distinct from similarity scoring. |
| Privacy and upload terms | What happens to uploaded art, code, or writing, and how long is it retained? | Uploading unpublished game material can create a separate confidentiality or license risk. |
| Hashes and timestamps | Does the tool expose input hashes or a verifiable time record? | These details connect a report to the exact files you examined. |
| Independent reproduction | Can another person repeat the test with the same inputs and obtain the same result? | Reproducibility is stronger evidence than an unexplained score. |
Use multiple independent observations when practical: repository history plus a code comparison, or a validated credential plus an image comparison. Do not combine incompatible scores into a made-up certainty.
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The U.S. Copyright Office’s AI initiative addresses digital replicas, copyrightability of AI outputs, and training-related legal issues. Its January 29, 2025 Part 2 announcement states: “It concludes that the outputs of generative AI can be protected by copyright only where a human author has determined sufficient expressive elements.” The announcement also 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.”
That guidance concerns copyrightability of an output; it does not decide whether another party infringed your game. A human’s creative contribution, the source work, the degree of similarity, access, and the use made of the material must still be analyzed. The 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 the purposes considered in fair-use analysis, along with the other statutory factors. Fair use is fact-specific and is not a blanket answer for model training or reuse of generated output. Jurisdiction, contracts, open-source licenses, registration, access, and the particular similarities can change the assessment.
Assemble an escalation packet
Organize the material so a lawyer or platform reviewer can follow the chain without relying on your memory. Include:
- Read-only originals, hashes, version history, contributor records, and publication dates.
- License files, contributor agreements, and notices relevant to the art, code, or writing.
- The suspected output in its original format, plus screenshots, URLs, account or workspace context, prompts, settings, and collection timestamps.
- Provenance or credential reports that identify the exact input hash and validation time.
- Side-by-side comparisons with annotations that distinguish rare expressive details from generic ideas or conventions.
- Reverse-image, similarity, code, or text comparison results, including negative results and the tool settings used.
- A dated timeline of creation, publication, access, discovery, preservation, and analysis.
- Any correspondence in which the other party describes the source, license, prompt, or generation process.
Keep the packet factual. Label an inference as an inference, preserve contradictory evidence, and avoid editing the suspected output to make the match look stronger. Qualified copyright counsel should review the packet before a DMCA notice, demand letter, platform complaint, or public allegation. A notice that overstates what your evidence proves can undermine credibility and create avoidable legal exposure.
Quick Recap
A compact decision checklist
- Preserve: hash and lock the original work, its history, licenses, and the suspected output.
- Classify: decide whether the issue is art, code, writing, provenance, or a combination.
- Compare: document rare expressive similarities using the medium-appropriate method.
- Trace: record dates, publication, plausible access, prompts, logs, and credential data.
- Qualify: separate evidence of similarity from claims about training, ownership, infringement, or fair use.
- Escalate: obtain legal review before sending a notice or making a public accusation.
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