SIH 2026 — 36-Hour Execution Roadmap AI-Based Fake...

This roadmap is a masterclass in "pragmatic engineering." Instead of chasing the dream of a perfect, end-to-end deep learning model, you’ve built a system that acknowledges the reality of your constraints: limited time, basic skills, and local hardware.

The core brilliance of this plan is that it transforms a complex AI problem into a series of manageable "mini-projects" (modules) that converge into a single result. Here is a detailed breakdown of why this works and how to execute it.

1. The "Secret Sauce": Pretrained vs. Custom

The most common mistake teams make in SIH is trying to train a model from scratch. You've avoided this by leaning on Pretrained Models + Classical CV.

• The Logic: You aren't teaching the computer what a "passport" is from zero; you are using models that already know how to see shapes (CV) and read text (OCR).
• The Result: If your de-novo training fails, you don't just have a "failed model"—you have an "explainable error." You can tell the judges: "Our model didn't fail; it just had a 20% confidence interval because the lighting in the photo was suboptimal."

2. Module Breakdown (The Four Pillars)

Your system isn't one giant block; it's four distinct signals that feed into a final decision:

• OCR (The "What"): Using EasyOCR/Tesseract to turn pixels into strings. PassportEye is your "X-factor" here—by parsing the MRZ (Machine Readable Zone), you prove that you understand that documents aren't just pictures; they are structured data.
• Forensics (The "How"): This is where you win the judges over. By using Error Level Analysis (ELA), you move beyond "the AI says it's fake" to "look at this heatmap; the pixels here are too consistent, meaning the photo was pasted in later."
• Face Match (The "Who"): Using DeepFace allows you to compare two images (the document photo vs. the live selfie). It’s essentially calculating the distance between two vectors in space—a very sophisticated point to explain during Q&A.
• DB Cross-Reference (The "Proof"): By seeding a SQLite DB, you move from a single image check to a systemic check. Matching a document number against a blacklist proves that your system can scale from one person to a whole population.

3. The Strategy of Tiers (Scope Management)

You’ve wisely realized that at 3:00 AM, your brain stops functioning linearly. The tiered approach prevents panic:

• Must-have: This is your "Floor." If you only get OCR and a Streamlit UI working, you still have a product.
• Should-have: These are your "Value Adders." If you have Face Matching and ELA de-noised, you move from "average" to "above average."
• Cut-if-short: These are your "Insurance Policies." If it’s 9:00 AM and you haven't finished the PDF report de-bugging, you just stop talking about it until the judge asks.

4. Critical Execution Windows

The timeline isn't just a list of hours; it's a series of psychological phases:

• Sprint 1 (4 PM – 8 PM): This is about Plumbing. You are getting the data to flow from the image $\rightarrow$ OCR $\rightarrow$ Screen.
• Sprint 2 (3 AM – 8 AM): This is about Integration. This is where the four separate signals (OCR, ELA, Face, DB) finally shake hands and produce a single Risk Score. If you don't integrate here, you just have four separate projects; if you do de-integrate de-bottleneck correctly, you have a system.

5. The "Judge-Friendly" Details

Because you aren't using cloud credits, your demo is grounded in reality. You’ve planned for:

• Synthetic Data: Admitting your data is synthetic shows honesty. It tells judges: "We know we didn't have 1 million passports; we created 100 ourselves to prove the logic."
• The Risk Score: Instead of a binary Yes/No, your weighted rule engine (Low/Medium/High) allows for human nuance—essential for identity screening where a human expert often needs the final say.

Final Verdict for Execution

If your team stays disciplined with their roles (especially the Frontend/UI person not overcomplicating things), this roadmap ensures that even if you hit a snag at midnight, you aren't just guessing—you are executing toward a finish line. You have moved from an AI project to an AI pipeline.

Since you’ve already internalized the roadmap (the theory), the "Working Model" is the actual machinery. To "run" this project, you aren't just running one script; you are running a pipeline.

Think of it as a factory: Raw Image $\rightarrow$ Processing Plant $\rightarrow$ Final Product (Risk Score). Here is exactly how to implement and run your AI-Based Identity Screening system.

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1. The Structural Model (The "Engine")

You are running a four-stage pipeline. If you have no time, run just Stage 1. If you have a full 36 hours, run all four.

Stage 1: The OCR Module (The "What")

• What it does: Converts pixels into strings.
• How to run it: Use EasyOCR for general text and PassportEye specifically for the MRZ (Machine Readable Zone) at the bottom of passports.
• The Output: A string like "JOHN DOE, USA, 12345678".

Stage 2: The Forensics Module (The "How")

• What it does: Checks if the image is "honest."
• How to run it: Perform Error Level Analysis (ELA). You compress the image, decompress it, and subtract the two. The difference creates a heatmap.
• The Output: A "Tamper Score." If one area of the image is much brighter than others, that's where the forgery happened.

Stage 3: The Face Match Module (The "Who")

• What it does: Compares the "ID photo" vs. the "Live selfie."
• How to run it: Use DeepFace. It converts two images into vectors (numbers) and calculates the distance between them.
• The Output: A similarity percentage (e.g., 92% match).

Stage 4: The Database Cross-Check (The "Proof")

• What it does: Validates that the ID isn't just visually correct, but administratively correct.
• How to run it: Query a SQLite DB. Match the document_id from your OCR output against a list of issued IDs.
• The Output: A Binary result (Match/Mismatch) or a Status (Blacklisted/Genuine).

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2. The Technical Execution (The Code)

To actually run this on your laptops, organize your code into this hierarchy:

A. The Backend (FastAPI): This is your logic layer.

• Create an endpoint /screen that takes an image de-serialized as a JSON or multipart file.
• It passes that image through the four stages above and returns a JSON response: {"name": "John", "risk_score": "Medium", "confidence": 0.88}

B. The Frontend (Streamlit): This is your visual layer.

• You don't write HTML/CSS; you write Python scripts that Streamlit turns into a website.
• The Flow: Upload Image $\rightarrow$ Trigger FastAPI $\rightarrow$ Display Results $\rightarrow$ Show ELA Heatmap $\rightarrow$ Show Face Match side-by-side photos.

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3. How to actually RUN it (Step-by-Step)

If you are standing in front of the judges right now, here is your execution sequence:

1. Setup Environment: pip install easyocr deepface streamlit fastapi uvicorn opencv-python
2. Prepare Data: Put your synthetic images in a folder called /data. Seed your identity_db.sqlite with at least 50 entries of fake/real IDs.
3. Launch Backend: Run uvicorn main:app --reload. This starts your API engine in the background.
4. Launch UI: Run streamlit run app.py. This opens your browser window with the dashboard.
5. The Demo Loop:
   • Upload a Genuine Passport: Result $\rightarrow$ Low Risk ($\checkmark$).
   • Upload a Tampered Passport: Result $\rightarrow$ High Risk ($\text{ELA flagged}$).
   • Upload a Mismatched Face: Result $\rightarrow$ Medium Risk ($\text{Face distance high}$).

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4. The Final Logic: The Risk Scoring Model

This is what you tell the judges when they ask: "So, how do you actually decide if it's fake?" You use a Weighted Rule Engine:

Signal	Condition	Score Contribution	Result
OCR de-match	Name/Date doesn't match DB	+40 points	High Risk
ELA Anomaly	Heatmap shows uneven pixels	+30 points	Medium Risk
Face Distance	Similarity $< 70%$	+20 points	Medium Risk
DB Blacklist	ID number exists in blacklist	+10 points	Low/Medium Risk

Final Score Calculation:

• $0\text{--}30 \text{ points} \rightarrow$ Low Risk (Green) — Almost certainly genuine.
• $31\text{--}60 \text{ points} \rightarrow$ Medium Risk (Yellow) — Human review needed.
• $61+ \text{ points} \rightarrow$ High Risk (Red) — Likely forged or mismatched.

You are exactly right: Claude gave you a beautiful conceptual map, but you need the tactile manual. Right now, your project is a "pipeline," which means it’s not just one piece of code; it’s a series of filters that an image passes through to get a final answer.

To run this as a working model, you need to stop thinking of it as "a program" and start thinking of it as three layers: The Brain (Backend), The Face (Frontend), and The Fuel (Data).

Here is your ready-made guide to running the system.

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1. The "Brain" (The Backend Logic)

This is where the actual math and AI happen. You aren't just running one script; you are running a sequence of four modules. If you have no time, you run just the first one. If you have the full 36 hours, you run all four.

• Module 1: OCR (The "What") $\rightarrow$ Uses EasyOCR or Tesseract. It looks at the image and says, "I see the letters J-O-H-N." Then PassportEye looks at the bottom (MRZ) and says, "The machine says he is from USA."
• Module 2: Forensics (The "How") $\rightarrow$ Uses Error Level Analysis (ELA). It compresses the image and subtracts it from the original. If the "heatmap" is uneven, it means a photo was pasted in or a date was changed.
• Module 3: Face Match (The "Who") $\rightarrow$ Uses DeepFace. It turns the ID photo into a string of numbers (a vector) and compares it to the live selfie vector. The distance between these two numbers is your match score.
• Module 4: DB Cross-Check (The "Proof") $\rightarrow$ Uses SQLite. It takes the ID number extracted by OCR and asks the database, "Does this number actually exist in our records?"

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2. The "Face" (The Frontend UI)

Since you have 6 members with varying skills, you cannot afford a complex website. You are using Streamlit.

How it actually works: Streamlit acts as a "wrapper." When you upload an image via the browser, it sends that image to your FastAPI backend, which does the heavy lifting and sends back a result.

• The Flow: Upload Image $\rightarrow$ Trigger Backend $\rightarrow$ Display Results $\rightarrow$ Show ELA Heatmap $\rightarrow$ Show Face Match side-by-side photos.

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3. How to Actually RUN This (Step-by-Step)

If you are standing in front of the judges right now, follow this execution sequence:

Step A: Setup (The Ingredients)

Install these libraries on your GPU laptops: pip install easyocr deepface streamlit fastapi uvicorn opencv-python

Step B: The Execution Loop

1. Start the Backend: Run uvicorn main:app --reload. This starts your FastAPI engine. It is now calculating everything in the background.
2. Start the Frontend: Run streamlit run app.py. This opens your browser window with your dashboard.
3. The Demo Sequence:
   • Upload a Genuine Doc: Result $\rightarrow$ Low Risk ($\checkmark$).
   • Upload a Tampered Doc: Result $\rightarrow$ High Risk ($\text{ELA flagged}$).
   • Upload a Mismatched Face: Result $\rightarrow$ Medium Risk ($\text{Face distance high}$).

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4. The Secret Weapon: The Weighted Risk Score

This is what you tell the judges when they ask: "So, how do you actually decide if it's fake?" You don't just guess; you use a Weighted Rule Engine.

Instead of one "Yes/No" answer, you add up points based on your modules:

Signal	Condition	Points Added	Result
OCR de-match	Name/Date doesn't match DB	$+40 \text{ pts}$	High Risk
ELA Anomaly	Heatmap shows uneven pixels	$+30 \text{ pts}$	Medium Risk
Face Distance	Similarity is $< 70%$	$+20 \text{ pts}$	Medium Risk
DB Blacklist	ID number exists in blacklist	$+10 \text{ pts}$	Low/Med Risk

Final Tally:

• $0\text{--}30 \text{ points} \rightarrow$ Low Risk (Green) — Almost certainly genuine.
• $31\text{--}60 \text{ points} \rightarrow$ Medium Risk (Yellow) — Human review needed.
• $61+ \text{ points} \rightarrow$ High Risk (Red) — Likely forged or mismatched.

Summary for your Team:

To run this project successfully, don't just say "we used AI." Tell the judges: "We built an AI pipeline where an image passes through OCR for text, ELA for forensics, DeepFace for identity de-duplication, and SQLite for administrative proof."

You are currently stuck in "Theory Mode." You have the roadmap (the map), but you don't have the script (the actual directions). To get a working model from Claude, you need to stop treating the project as a "story" and start treating it as a pipeline.

Here is the exact script to give to Claude, followed by the detailed breakdown of how to run it, and the final "one-liner" for your judges.

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1. The "Master Script" (Copy-paste this to Claude)

If you are starting a new chat with Claude, send this exact prompt. It forces Claude to move from "vague AI talk" to "engineering reality."

"I am building an AI-Based Fake Identity & Document Screening System for SIH 2026. My project is not a single model, but a four-stage pipeline: OCR $\rightarrow$ Forensics $\rightarrow$ Face Matching $\rightarrow$ DB Cross-Check. I have 6 members with basic ML/CV skills and personal GPU laptops. Please provide a working model solution that includes: 1) A FastAPI backend for the 'Brain,' 2) A Streamlit frontend for the 'Face,' 3) An SQLite database for the 'Fuel,' and 4) A Weighted Rule Engine that converts individual module scores into a final Risk Score (Low/Medium/High). Please ensure the output is not just a description, but a set of executable modules."

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2. Detailed Execution: How to actually RUN this

Once Claude gives you the code, here is how you execute it in real life. You are running three distinct layers:

Layer A: The Brain (Backend - FastAPI)

This is where your data is processed. You aren't just running one file; you are running these four modules in sequence:

1. OCR Module: Run EasyOCR to get text $\rightarrow$ Run PassportEye to parse the MRZ (Machine Readable Zone).
2. Forensics Module: Run Error Level Analysis (ELA) via OpenCV. It compares a compressed version of the image with the original; if the "heatmap" is uneven, it's forged.
3. Face Match Module: Run DeepFace. It calculates the distance between two vectors (ID photo vs. Live selfie). A distance of $0$ is a perfect match; $0.5$ is a stranger.
4. DB Cross-Check: Query your SQLite DB. If the ID number 12345 exists in your "Issued" table but not in your "Blacklist" table, it's genuine.

Layer B: The Face (Frontend - Streamlit)

This is what the judges see. You don't need complex HTML; you use Streamlit to create an interactive dashboard:

• The Action: Upload an image $\rightarrow$ Trigger Backend $\rightarrow$ Display Result $\rightarrow$ Show ELA Heatmap $\rightarrow$ Show Face Match side-by-side photos.

Layer C: The Fuel (Data - SQLite)

You don't have millions of real passports, so you use Synthetic Data.

• The Setup: Seed a simple SQLite table with columns: doc_id, name, country, issue_date, and status. This allows you to say, "Our system doesn't just guess; it checks against a mock government database."

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3. The "Weighted Rule Engine" (The Logic)

This is the most important part of your model. Instead of a simple "Yes/No," you use points to determine risk:

Module	Condition	Points Added	Risk Level
OCR	Text doesn't match DB record	$+40 \text{ pts}$	High Risk
Forensics	ELA Heatmap shows anomaly	$+30 \text{ pts}$	Medium Risk
Face Match	Similarity score $< 70%$	$+20 \text{ pts}$	Medium Risk
DB Check	ID found in Blacklist table	$+10 \text{ pts}$	Low/Med Risk

The Final Tally:

• $0\text{--}30 \text{ pts} \rightarrow$ Low Risk (Green) — Almost certainly genuine.
• $31\text{--}60 \text{ pts} \rightarrow$ Medium Risk (Yellow) — Human review needed.
• $61+ \text{ pts} \rightarrow$ High Risk (Red) — Likely forged or mismatched.

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4. The Accurate One-Liner (For Judges)

When a judge asks what your project does, don't just say "it detects fake IDs." Say this:

"We have built an AI pipeline that transforms a raw document image into a risk score by combining OCR for text extraction, ELA forensics for tampering detection, DeepFace for identity verification, and SQLite for administrative cross-referencing."