The decision
Should you choose IsThisRealAI?
Publishers evaluating a secondary image-verification signal
Best for
Publishers evaluating a secondary image-verification signal.
Choose something else if
you have not defined the workflow and acceptance criteria yet.
Evidence
Verified research · rating withheld
Pricing checked
See current vendor pricing · Oct 28, 2026
No verified permanent free plan is recorded.
Confirm billing, usage limits, seats, and renewal terms with the vendor.
In this guide
Short answer
IsThisRealAI is a candidate for a secondary signal when checking a still image, provided you can accept its upload terms. A probability estimate cannot establish who created an image or whether it accurately depicts an event. For publication decisions, keep provenance and source contact central.
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Evidence and review scope
This review follows submission #376. Official product, pricing and privacy pages were retrieved on October 28, 2026. We did not upload an image, purchase credits or run an accuracy benchmark. All detection capabilities and privacy statements below are vendor claims. Ratings are withheld and no affiliate link is configured.
Workflow and documented limitations
The product page accepts JPEG, PNG and WebP still images up to 8 MB. Running a check requires login and a credit. Results include AI and deepfake estimates, with an uncertain outcome when the evidence is inconclusive. The vendor warns that screenshots, compression and edits reduce reliability, and explicitly reports missed InsightFace face swaps in its own tests. We have not independently reproduced those results.
Treat a result as a question to investigate. A low score does not authenticate an image, and a high score does not identify a person’s intent. Avoid converting a detector label into a public accusation.
Price: uncertainty is still a paid result
The pricing page lists one-time packs: 20 checks for $2.99, 100 for $9.99 and 300 for $24.99, tax included. Credits do not expire. There is no subscription in the checked offer. The homepage says uncertain verdicts consume a credit; service-side failures return it.
Budget for decisions supported, rather than counting confident-looking outputs. In a hypothetical ten-image assignment, repeat checks of altered copies could consume several credits for the same underlying question without supplying independent evidence. Decide beforehand when further checking stops being useful.
Privacy can determine suitability
The privacy policy says the site discards images after processing, but its analysis provider may retain them for up to 14 days, permit human review and use them to improve services. This is not a zero-retention workflow. Do not upload sensitive unpublished material without an appropriate basis and an understanding of those terms.
Proposed pilot with known-origin images
Build a small collection you are allowed to process: original camera images and generated images whose origin you can document. Keep the originals, record the generator when applicable, and label the answer key separately from the test filenames. Use varied subjects so one type of portrait does not dominate your conclusions.
Check each original, then a compressed or resized copy. Record the verdict, your known origin and whether the change affected the result. Count false accusations against real images separately from missed generated images. An uncertain outcome should remain uncertain in your notes.
This is a proposed exploratory pilot, not a statistically representative benchmark. Do not advertise an accuracy percentage from a handful of examples. Its purpose is to reveal whether the workflow is useful enough to investigate further.
Editorial handover
Keep the original source URL, permission record and unresolved questions together. Contact the image supplier when the origin matters. If a detector conflicts with a documented provenance trail, investigate the conflict rather than silently preferring the numerical answer.
Choose this tool only when a paid secondary signal improves an existing verification process and its data terms fit the material. See the tool profile, compare requirements in the buyer checklist, or browse research guides.
Transparency
How this guide was checked
Editorial accountability
Who checked this guide
- Evaluation type
- Research-based verification
- Last materially checked
- Evidence
- 3 listed sources
Hands-on testing is identified explicitly. Research-based coverage uses cited product documentation and other named sources; it does not imply every paid plan was used. Read the full methodology.
Editorial freshness
Pricing and material product claims were checked October 28, 2026.
Editorial basis
What this guidance is based on
- Editorial basis
- Source-led analysis
- Primary references
- 3
- Products covered
- 1
- Last checked
- 2026-10-28
Important limits
- • Features, availability, and pricing can change after publication; confirm consequential details with the provider.
Standardized benchmark coverage
How this review maps to the research benchmark
Citation accuracy
Not applicableThe reviewed workflow does not produce source-grounded research answers, so a citation score would be misleading.
Read protocol v2026.10-v1 →Thematic analysis
Not applicableThe reviewed workflow is not qualitative evidence analysis, so a thematic-analysis score would be misleading.
Read protocol v2026.10-v1 →Eligibility is not a product score. DiscoverAI publishes results only after output collection, blinded adjudication, reproducibility checks, and severe-error review.
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Confirm the tool meets every must-have workflow and stakeholder requirement.
Review starting point: Publishers evaluating a secondary image-verification signal
Run the same representative work you would use in production; do not score a polished demo.
Review starting point: Run a bounded set of representative tasks with known acceptable outcomes, then compare the result with your current workflow.
Calculate the effective cost per accepted result, including usage, review, corrections, and required add-ons.
Review starting point: One-time packs: 20 checks $2.99, 100 $9.99, 300 $24.99; tax included. Uncertain verdicts consume a credit.
Define an acceptance threshold, test known answers and edge cases, and record every correction.
Review starting point: Editorial quality signals: features 0.0/5. Validate these signals in your own work.
Verify what data enters the product, who can access it, how long it is retained, and whether it trains models.
Review starting point: Use approved low-risk data first. Check roles, consent, deletion, subprocessors, model-training settings, and the contract—not only the marketing page.
Test the real handoffs, permissions, failure states, and export path your team depends on.
Review starting point: IsThisRealAI integration requirements are not fully structured in this review; list every required system and test each connection.
Record training, governance, reliability, accessibility, ownership, and change-management risks before rollout.
Review starting point: Scores do not prove authenticity; Provider may retain images up to 14 days; No independent accuracy benchmark in this review
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Community evidence
How verified users put IsThisRealAI to work
Structured, editor-moderated experience—not star ratings. This complements our independent review and never changes its score.
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Community choice
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Sources and references
References cited by this article. Check the original source for scope, dates and current terms.
Frequently asked questions
Is there a free check?
The checked site says every check is paid; previewing an image does not run analysis.
Does a low score prove an image is authentic?
No. The vendor documents missed manipulations and weaker performance on altered copies.
Are uploaded images immediately deleted everywhere?
No. The policy says the analysis provider may retain them for up to 14 days.
Tools mentioned in this article
IsThisRealAI
Review IsThisRealAI’s paid image checks, uncertain verdicts and provider retention. Learn how to evaluate a detector without treating its score as proof.
Review IsThisRealAI’s paid image checks, uncertain verdicts and provider retention. Learn how to evaluate a detector without treating its score as proof.
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