# AI Image Validation X2Earn Apps that send captured photos for image analysis to AI should consider adding specific tasks in their AI prompts to detect: * Image quality * Doctored or Unrealistic modifications * Photo of a computer screen * Watermarks * Painted or hand-drawn text replacing real data ... or other unrealistic items that a "fake" photo could capture. ### Example: In this example we are assuming that we are taking a photo from a dApp runs inside VeWorld and so the photo will be captured by the users mobile camera. The backend of the app would like the AI to return a json structure giving: ``` { "evaluation_feasible": true, "doctored_unrealistic_score": 0.0, "doctored_unrealistic_reasons": [], "screen_capture_score": 0.0, "screen_capture_reasons": [], "watermark_score": 0.0, "watermark_reasons": [], "watermark_text": "", "painted_text_score": 0.0, "painted_text_reasons": [], "final_label": "clean", "final_confidence": 0.0 } ``` Where: | Item | Description | | --------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | evaluation\_feasible | True if the quality of the image is sufficient to perform other checks, False if the quality is poor and other checks could be unreliable | |

doctored\_unrealistic\_score
doctored\_unrealistic\_reasons

| A score between 0-1 if the AI has detected doctored or unrealistic items in the image. The reasons array will be populated with a summary | |

screen\_capture\_score
screen\_capture\_reasons

| A score between 0-1 if the AI has detected that the image was taken from a computer screen. The reasons array will be populated with a summary | |

watermark\_score
watermark\_reasons

|

A score between 0-1 if the AI has detected that the image contains watermarks.

The reasons array will be populated with a summary

| |

painted\_text\_score
painted\_text\_reasons

|

A score between 0-1 if the AI has detected user drawn text in the image.
The reasons array will be populated with a summary

| |

final\_label
final\_score

|

A final classification label of:

A final score between 0-1 as the level of confidence in the classification

| We can use a multi-stage prompt to guide the AI through these tasks: {% code overflow="wrap" %} ``` Mobile Photo Authenticity Check — Multi-Stage Prompt Objective: Given a photo provided by a mobile device, evaluate it through multiple analytical stages to determine: 1. If the photo has been doctored or altered in an unrealistic way. 2. If the photo has been taken from a computer screen rather than being an original capture of a real-world scene. 3. If the photo contains visible or partially obscured watermarks. Instructions: You must progress through the stages in sequence. At each stage, clearly indicate whether the photo passes or fails, and explain the reasoning. Output: Return only the final JSON object in the specified schema—no extra text. --- STAGE 1 — Quick Triage (visibility & quality): 1. Is the content visible and in focus enough to evaluate? 2. Are there heavy obstructions, extreme blur, or tiny resolution? 3. If evaluation is not feasible, mark `evaluation_feasible=false` and explain briefly. --- STAGE 2 — “Doctored / Unrealistic” Screening: Check for visual signs of synthetic or manipulated content. Consider: - Physics & geometry: inconsistent shadows, impossible reflections, mismatched perspective/vanishing points, warped straight lines near edits. - Material cues: plastic-like skin, repeated textures, smeared hair/eyelashes, “melting” edges, duplicated fingers/ears. - Edge artifacts: halos, cut-out borders, fringing, mismatched depth of field. - Compression anomalies: localized blockiness/quality shifts suggesting pasted regions. - Lighting: inconsistent color temperature or specular highlights vs. environment. - Text & patterns: deformed text/logos, repeated tiling. - Context coherence: scale mismatches, impossible combinations. Output: - `doctored_unrealistic_score` (0–1) - `doctored_unrealistic_reasons` (bullet list) --- STAGE 3 — “Photo of a Screen” Screening: Evidence the subject was displayed on a digital screen and re‑photographed: - Screen structure: visible pixel grid/subpixels, scanlines, PWM/refresh bands, moiré. - Device clues: bezels, notch, status bar, window chrome, cursor, taskbar, scroll bars. - Optical clues: rectangular glare, Newton rings, rainbowing consistent with glass. - Focus/parallax: focus on flat screen surface; keystone perspective of a monitor. - White point/gamut: uniform backlight glow, overly blue/green whites. Output: - `screen_capture_score` (0–1) - `screen_capture_reasons` (bullet list) --- STAGE 4 — Watermark / Overlay Detection: Detect watermarks or ownership/stock overlays that may indicate non-original content or re-use: - Typical forms: semi-transparent text/logos (“Getty Images”, “Shutterstock”, “Adobe Stock”, creator handles), diagonal repeating patterns, corner logos, date/time stamps that appear composited. - Visual traits: consistent alpha translucency, uniform repetition across the frame, crisp overlay unaffected by scene lighting/perspective, different resolution/sharpness vs. underlying image. - Placement: along edges/corners/center diagonals; multiple repeats; patterned tiling. - Edge cases: legitimate **camera UI overlays** (e.g., timestamp) vs. stock watermarks—distinguish when possible. - Context: if a watermark is present, note its content (if legible) but **do not identify a person**. Output: - `watermark_score` (0–1) - `watermark_reasons` (bullet list) - If confidently recognized, add a short `watermark_text` string (e.g., `"Shutterstock"`, `"creator handle @name"`); otherwise empty. --- STAGE 5 - Detect Hand-Drawn or Painted-On Text: Detect if the image has text that was manually added using a paint or drawing program, rather than being part of the original scene: - Look for uneven, non font based handwriting or shapes inconsistent with printed text - Identify brush strokes, smudging, or digital pen artifacts in the text - Detect text blending poorly with the image background or overlapping objects unnaturally - Check for consistent resolution between the text and the rest of the image Output: - `painted_text_score` (0-1) - `painted_text_reasons` (bullet list) --- STAGE 6 — Final Decision & Confidence: - `evaluation_feasible`: boolean. - `final_label`: one of `"clean"`, `"doctored_unrealistic"`, `"screen_capture"`, `"watermarked"`, `handdrawn`, `"multiple_flags"`, `"inconclusive"`. - `final_confidence` (0–1): overall confidence in `final_label`. - Keep reasoning concise; cite visible cues only. --- OUTPUT — JSON Schema (return only this): { "evaluation_feasible": true, "doctored_unrealistic_score": 0.0, "doctored_unrealistic_reasons": [], "screen_capture_score": 0.0, "screen_capture_reasons": [], "watermark_score": 0.0, "watermark_reasons": [], "watermark_text": "", "painted_text_score": 0.0, "painted_text_reasons": [], "final_label": "clean", "final_confidence": 0.0 } ``` {% endcode %} --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.vebetterdao.org/developer-guides/ai-image-validation.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.