What Is Adobe Stock’s Acceptance Rate? Review Criteria and Rejection Reasons

Adobe Stock’s public moderation guidance does not provide an overall average acceptance rate. Rather than assuming a typical percentage, calculate your own results consistently and use refusal reasons to improve the work you submit.

What is Adobe Stock’s acceptance rate?

An acceptance rate is the share of completed submission decisions that resulted in approval. Adobe reviews files individually for technical quality, rights, commercial value, metadata and distinctiveness. Metadata means descriptive information such as titles and keywords, rather than the image itself. The official moderation overview explains these criteria but does not state an average for all contributors, beginners or AI-generated images.

Personal reports online are individual examples. One contributor may submit only carefully selected photographs, while another experiments with many AI concepts. Sample size, subject, submission period and treatment of resubmissions can make two reported rates of 60% mean different things. A high percentage alone does not establish skill or earnings.

Separate photographs, illustrations, vectors and video, and identify AI-generated work in your records. Fixing missed focus in a photograph is different from correcting an impossible hand in a generated image. Records that connect changes in your process to later results are more useful than an unsupported platform-wide estimate.

How to calculate your own acceptance rate

Acceptance rate = accepted submissions ÷ (accepted + refused submissions) × 100. Exclude files still awaiting review and files uploaded but not submitted. Treating an unfinished decision as a refusal understates the rate.

Consider a hypothetical batch of 50 files: 24 accepted, 16 refused and 10 pending. There are 40 completed decisions, giving an acceptance rate of 60%. Dividing 24 by all 50 gives 48%, which measures approvals as a share of the entire batch, not the acceptance rate of completed reviews.

Decide how to count resubmissions. If one work is refused, corrected and then accepted, its submission-attempt rate is 50%: one approval and one refusal. Its final acceptance rate per unique work is 100%: one work eventually approved. Neither metric is useful if silently mixed with the other. Keep an original work identifier, submission date, decision date, attempt number, outcome and changes made.

Track recent batches alongside lifetime results. If an older record of 90 approvals and 10 refusals gains a new batch of 10 approvals and 10 refusals, the cumulative rate is about 83.3%, while the recent batch is 50%. Mark partially reviewed batches as provisional. Preserve historical outcomes in your own log even if you later delete a file, so comparisons remain consistent.

What does Adobe Stock consider during review?

The central question is whether customers can actually use the asset. Adobe’s quality and technical guidance addresses sharpness, noise, editing artifacts and balanced exposure. An attractive thumbnail can still conceal halos along edges or marks in the background when viewed at full size.

Commercial usefulness is not simply visual spectacle. A cup photograph with a clear subject and room for advertising copy may serve a particular customer better than a frame packed with objects. That is a way to think about use, not an approval rule promising that empty space makes an image acceptable.

Titles and keywords must describe what is actually present. Do not add unrelated popular terms to a plant photograph. Follow the official title and keyword guidance: describe the main subject and setting clearly and use the metadata language selected in your account. Review automatically suggested terms instead of accepting them indiscriminately.

Good technical quality does not remove rights or similarity concerns. A model release is permission for relevant use of a person’s likeness; a property release covers relevant rights relating to property or artwork. Check when these permissions are required and whether distinctive product designs or another person’s work dominate the image. Requirements also differ by file type, so do not apply photograph specifications unchanged to video or vectors.

Common refusal reasons and what to change

• Quality: inspect subject focus, highlight and shadow detail, noise, excessive sharpening and compression artifacts. Compare corrections with the original. Enlarging a file alone does not restore detail that was never captured.
• Similar content: avoid bulk submissions differing only in color, slight zoom or tiny angle changes. Ask whether each version actually conveys different information or serves a different customer need.
• Rights: removing a logo may not solve every issue. Recognizable product shapes, packaging, artworks and restricted locations also need attention. Hold back work if necessary permissions cannot be obtained.
• Metadata and compliance: check the title, keywords, content type and AI labels against the actual file. Remove irrelevant terms and descriptions that misleadingly suggest an actual news event.

Use Adobe’s common refusal reasons to categorize problems. A general reason does not identify the precise pixel or object responsible. Start with the stated issue, then inspect the entire file for additional problems.

The distinct-content guidelines specifically caution against minor variations on the same concept. Changing a title and submitting the same picture again does not address visual redundancy.

What extra checks do AI-generated images need?

Start with structure. Look beyond finger counts to how a hand holds an object, how glasses connect, and whether mug handles, chair legs and lettering make sense. Check that reflections and shadows correspond to objects and that perspective and background elements remain coherent. After repairing an error, zoom in again to inspect the joining edges and texture.

The generative AI guidelines require appropriate rights from the tool provider for stock licensing. Generation does not erase third-party rights. Check prompts, titles and keywords for prohibited references to people, artists, fictional characters, copyrighted work and other protected material.

Select the file in the Contributor Portal and review Created using generative AI tools in the Metadata panel. Where applicable, select People and Property are fictional for fictional subjects not based on real ones. An identifiable real person depicted or used as a basis triggers the relevant model-release requirements; recognizable real property also requires the relevant rights checks. Declaring a subject fictional does not replace permission for a real subject.

Using an AI-assisted editing tool does not automatically mean every edit has identical labeling requirements. Adobe distinguishes creating or materially changing the main subject from certain edits such as background extension or removing distractions. Compare your actual edits with the official examples. The AI submission procedure explains labels, releases and the final submission step.

A practical checklist before submitting

Keep the editable original and make a separate final export. Work through the following checks to catch preventable errors; the process cannot guarantee approval.

1. State the intended use in one sentence. For example, “a plant image for a guide to environmentally conscious living.” Check whether the subject and composition actually support that purpose.
2. Compare variations side by side. Keep the strongest, meaningfully different files. Set aside versions changed only by color or zoom and decide whether they add enough value to submit.
3. Inspect the final file at 100%. Move from the center to every corner, checking focus, noise, edges, lettering and AI structural errors. Then zoom out to judge the whole composition.
4. Check file-specific requirements and rights. Match size and format to current documentation, confirm required releases and review the generation tool’s terms. Pause submission where a requirement remains unclear.
5. Review each file’s portal metadata. Confirm title, keywords, type, AI status, fictional-subject status and linked releases. Recheck individual files after automatic entry or bulk changes.
6. Record submission and outcome. Log the filename, work identifier, submission date and attempt number. Confirm the file actually entered review, then add the result, stated reason and planned changes.

Uploading is not the same as submitting for moderation. If pressing Submit does not move the file into review, you may be facing a limit or an incomplete field rather than a low acceptance rate. The guide to Adobe Stock upload and submission limits explains how to distinguish a waiting queue from permission to submit more files.

Can you resubmit refused images?

Consider resubmission when you can address a specific issue. Correct identified noise or resolve a missing release, then inspect the exported file and metadata from the beginning. A correction does not guarantee approval, and another issue may still remain.

Avoid repeatedly sending an unchanged file or altering only its title or filename. A similarity refusal may call for a genuinely different purpose or concept rather than a slight color adjustment. If the work cannot offer that difference, deciding against further resubmission is reasonable.

If you believe there is a clear mistake, prepare the file identifier, refusal text, original file or production evidence, and details of actual corrections before using Contributor support. An inquiry is not a promise of reversal. Group recurring reasons in your own log to determine what needs attention before the next production session.

How does acceptance differ from sales performance?

Approval means an asset has entered the licensable collection, not that someone bought it. Acceptance rate measures submission preparation and review outcomes; downloads and earnings measure demand and sales. A high approval rate can coexist with low earnings when few customers search for the subject or the asset does not fit their intended use.

Track approvals, downloads, earnings and production or correction time by subject. Compare revenue per accepted image over the same observation window. Comparing lifetime downloads for a month-old asset with a file published yesterday gives the older file an unfair advantage.

If two subjects both pass review but only one attracts repeat downloads, investigate the actual use cases before merely increasing output. If refusals cluster around structural errors, improve creation and inspection first. The aim is content that satisfies review requirements and gives a customer a reason to license it.