This article explores a powerful ChatGPT prompt designed to identify real-world locations from images using pixel analysis, metadata exclusion, and systematic verification.
You Should Know:
1. Understanding the Prompt Structure
The prompt enforces strict ethical guidelines and a structured approach:
– No EXIF or metadata analysis (pure pixel-based deduction).
– Raw observation notes (colors, shapes, shadows, structures).
– Clue categorization (vegetation, terrain, cultural cues).
– Shortlisting regions (5 potential locations).
– Hypothesis testing (leader vs. runner-up comparison).
– Verification plan (public photo comparisons).
– Final lock-in (uncertainty radius and residual doubts).
2. Practical Implementation with AI & OSINT Tools
To replicate this process, use the following commands and tools:
Linux/CLI Tools for Image Analysis
Extract basic image info (without EXIF) file image.jpg Check image dimensions identify -format "%wx%h" image.jpg Analyze color distribution (ImageMagick) convert image.jpg -define histogram:unique-colors=true -format %c histogram:info: Detect edges (for structure analysis) convert image.jpg -canny 0x1+10%+30% edge_output.png
Python Script for Pixel Analysis
from PIL import Image
img = Image.open("image.jpg")
width, height = img.size
pixel_data = list(img.getdata())
Extract dominant colors
from collections import defaultdict
color_count = defaultdict(int)
for pixel in pixel_data:
color_count[bash] += 1
sorted_colors = sorted(color_count.items(), key=lambda x: -x[bash])
print("Top 5 colors:", sorted_colors[:5])
Windows Command for Image Forensics
Check file properties (without EXIF) Get-ItemProperty -Path "C:\path\to\image.jpg" | Select-Object Name, Length, LastWriteTime
3. Verification Using Public Data
- Reverse Image Search (No Metadata):
curl -X POST -F "[email protected]" "https://api.tineye.com/rest/search/"
- Compare with OpenStreetMap (OSM):
Query OSM for landmarks osmfilter data.osm --keep="amenity=restaurant or building=church"
What Undercode Say
This method forces AI to rely solely on visual cues, reducing bias from metadata. However, real-world accuracy depends on:
– Shadow analysis (suncalc.org for solar positioning).
– Cultural cue databases (license plates, road signs).
– Botany APIs (identifying regional vegetation).
For best results:
- Combine with YOLOv8 object detection (
yolo detect predict source=image.jpg). - Use CLIP (OpenAI) for semantic image understanding.
- Cross-reference with Wikidata geographic queries.
Prediction
AI-powered geolocation will evolve into real-time augmented reality (AR) navigation, replacing traditional GPS in 3-5 years. Expect:
– AI drones autonomously mapping disaster zones.
– Privacy-focused OSINT tools bypassing metadata restrictions.
– Blockchain-verified image timestamps to combat deepfake locations.
Expected Output:
A structured, metadata-free geolocation report with ranked hypotheses and verification steps.
Relevant URL: Astral Codex Ten – Testing AI’s GeoGuessr Genius (if available).
This guide merges AI prompting with cybersecurity-grade forensics for real-world applications.
References:
Reported By: Ruben Hassid – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
