Pipelines
A pipeline is a named detection configuration you pass in the pipeline field of every verification request. Each pipeline chains one or more models into a fixed sequence, runs your image through them, and returns a single consolidated verdict plus per-model details. You choose the pipeline; it orchestrates the models, routing, and post-processing for you.
All pipelines answer the same core question — was this media generated or altered by AI? — but they differ in the media they accept (images or video), the depth of analysis they perform, and the artifacts they return. See the Verification API Deep Dive for how a pipeline fits into the request lifecycle.
Available pipelines
| Pipeline | Status | Media | Description |
|---|---|---|---|
| cantaloupe | Public | Images | Deepfake image detector combining the Cantaloupe and Azalea models |
| honeydew | Closed beta | Images | Source attribution and tampering localization combining the Azalea and Honeydew models, with downloadable localization artifacts |
| pitaya | Closed beta | Videos | Detects whether a video is fully AI-generated or authentic via spatio-temporal analysis (Pitaya model) |
Additional pipelines are in development and available to select customers. Contact Truebees for early access.
How each pipeline works
cantaloupe — the standard authenticity check
The cantaloupe pipeline is the everyday image verification path. It answers the most common question: is this a real photograph, or was it AI-generated? It returns a binary verdict (AI_GENERATED / NOT_AI_GENERATED) and a confidence_score, so you get both the result and how certain the system is.
It runs two models in sequence:
- Azalea first classifies the image's social-media origin — whether it has been shared or processed through a platform such as Facebook or Instagram. This matters because platforms re-encode images on upload, and that re-compression introduces artifacts that can otherwise be mistaken for signs of manipulation. Azalea detects these traces and routes the image to the appropriate inference weights.
- Cantaloupe then performs the actual deepfake detection on the correctly-routed image, producing the final AI-generation verdict.
This social-media awareness is what keeps the verdict reliable on real-world images that have passed through messaging apps and social feeds.
Choose cantaloupe when you need a fast, dependable real-or-fake answer at scale. It is publicly available to any account with an active subscription.
Read the full cantaloupe page →
honeydew — source attribution and tampering localization
The honeydew pipeline performs a deeper analysis. Rather than a binary verdict, it attributes the image's source across several categories — authentic, GAN-generated, diffusion-generated, AI-partially-generated, or human-partially-edited — and it localizes which regions of the image appear manipulated.
It also runs two models in sequence:
- Azalea again determines social-media origin and routes between social and non-social inference weights, for the same reasons as in cantaloupe.
- Honeydew then classifies the image origin across the categories above and produces a per-category probability breakdown alongside the headline verdict.
On top of the verdict, Honeydew emits two downloadable artifacts:
| Artifact | What it is |
|---|---|
localization_map | A heatmap highlighting manipulated regions (red) versus authentic regions (green) |
localization_overlay | That heatmap blended over the original image for easier reading |
Artifacts are retained for 24 hours after the verification completes and are fetched by id from the artifact endpoint. They give you visual, defensible evidence — useful when you need to explain or justify a decision rather than record a bare yes/no.
Choose honeydew when you need to understand and demonstrate how an image was produced and where it was altered — for investigations, content-moderation review, or any case where a per-category breakdown and visual evidence add value. It is currently in closed beta; contact support@truebees.eu to request access.
pitaya — video deepfake detection
The pitaya pipeline is the first Truebees pipeline for video. It answers the same core question as cantaloupe — is this AI-generated, or authentic? — but for footage rather than still images, returning a binary verdict (AI_GENERATED / NOT_AI_GENERATED) and a confidence_score.
Unlike the image pipelines, it runs a single model:
- Pitaya is built on a foundational vision transformer backbone and analyzes spatio-temporal artifacts — the inconsistencies a generator leaves both within a frame and across consecutive frames. It samples temporally distributed clips across the whole video and extracts contiguous patches from consecutive frames, then aggregates the clip-level signals into one verdict.
Choose pitaya when you need to verify whether a video is AI-generated. It targets fully generated or fully authentic footage, not partially edited clips. It is currently in closed beta; contact support@truebees.eu to request access.
Which pipeline should I choose?
| If you need… | Use |
|---|---|
| A fast binary real-or-fake verdict with a confidence score, at scale | cantaloupe |
| Source attribution across categories plus downloadable tampering-localization artifacts | honeydew |
| A real-or-fake verdict on a video | pitaya |
Not sure which fits your use case? Contact Truebees and we'll help you choose.