AI generated?
Start with evidence.
A practical approach to the images, videos, and writing that make you look twice.
A convincing image can be synthetic. A strange-looking image can be real. Fluent writing can come from a person or a model. The useful question is not just “does this look like AI?” but “what evidence do I have about how this was made?”
This guide explains a careful review process. It cannot establish the origin of a particular file. The domain aigenerated.ai is available for acquisition.
Start with the source, not a score.
Before uploading a file to any service, find out where it came from. Keep the original link and, when you are entitled to access it, the original file. A screenshot or forwarded copy may remove information that could help establish its history.
- Find the earliest available version. A reverse-image search or a search for a distinctive sentence can help trace prior appearances. An earlier result is a lead to investigate, not automatic proof of origin.
- Separate the content from the claim. A real photograph can be paired with a false caption. A generated illustration can be accurately labeled. “AI-generated” and “misleading” are not synonyms.
- Ask for context. The creator, publisher, original posting, and editing history may offer evidence that is not present in the final file.
- Protect private material. Before using an external detector, check its retention and training policies. For student work, follow the institution's approved process.
Images: inspect origin as well as appearance.
Visual oddities can justify a closer look, but a checklist of fingers, text, or shadows is not a reliable verdict. Instead, compare the image with its original publication and inspect any available creation or editing information.
Some tools expose content credentials alongside model-based detection results. These are different types of evidence: provenance describes recorded history; a detector estimates whether patterns resemble generated content. Hive's documentation describes both confidence-based classification and available C2PA metadata, and recommends considering its response holistically. Read Hive's documentation.
If credentials are absent, leave that question open. Missing information does not establish that an image is real or synthetic. Prefer the original file where possible; resizing, compression, and screenshots can make analysis harder. Copyleaks explains image-analysis limitations.
Video: consider picture, sound, and context separately.
A clip can contain authentic footage, a synthetic voice, generated frames, or a mixture. A single still image does not describe every part of a video. Look for the original recording and longer context, and treat the audio and visual tracks as separate questions.
When considering a tool, check what it actually analyzes and which formats it supports. A system designed for facial manipulation may answer a different question from one designed for fully generated scenes. For example, Hive documents separate generation and deepfake classifications; Reality Defender describes audio, video, and image analysis with results that need to be considered alongside other evidence. Hive's model documentation · Reality Defender's technology overview.
Writing: review the process, especially in education.
For an essay, the strongest next step may be a conversation about the student's work. Ask how they developed the argument, which sources they used, and whether drafts or version history are available. These can support a fair review, but none should be treated as infallible in isolation.
Turnitin says its AI-writing model can misidentify human and AI-written text and should not be the sole basis for adverse action against a student. GPTZero similarly describes its classifier as a starting point for further inquiry. A score alone does not establish misconduct. Turnitin's interpretation guidance · GPTZero's FAQ.
Use the school's policy, give the student a chance to explain, and distinguish permitted assistance from prohibited generation. Detection, plagiarism, factual accuracy, and the quality of learning are separate issues.
Read a result as evidence, not certainty.
Before acting on a detector result, ask:
- What was analyzed? The entire file, selected frames, prose passages, or only faces?
- What does the number mean? A model confidence score is not necessarily the percentage of a file that was generated. Consult the tool's documentation.
- Was this input suitable? Language, length, file quality, editing, and the model's supported formats can affect whether a result is useful.
- What else supports the conclusion? Compare the result with original sources, available history, and independent context.
- What remains unknown? “Inconclusive” is a useful outcome when the available evidence does not support a firm conclusion.
A second detector can offer another signal, but two agreeing scores are not automatically independent confirmation. Keep a record of the tool, date, input, and limitations so someone else can understand how you reached your assessment.
Useful tools, different jobs.
Hive, Copyleaks, GPTZero, Reality Defender, and Turnitin each describe different capabilities and audiences. The links in this guide are documentation references, not endorsements, affiliate links, or a comparative accuracy ranking. This website is independent of those providers.
Reviewed November 6, 2026. This guide draws on the linked providers' documentation about their own capabilities and limitations. It is introductory guidance, not independent testing of those products. Product capabilities can change.
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aigenerated.ai is available for acquisition at an asking price of $100,000 USD. The offering is for the domain registration, not a detection service or operating business.
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