Al-Based Fake Identity & Document Screening System
Ministry of Home Affairs · Miscellaneous · Software
Excellently specified with the tampering module as the clear differentiator, but you build your own tampered examples so the detector only catches what you made — invest in the forensic detection, lean on deterministic MRZ validation, and be honest that real forgeries are subtler than synthetic ones.
What it actually is
Border checkpoints process thousands of passports and visas by hand, and cannot reliably catch sophisticated forgeries, altered photos, tampered stamps or people using multiple identities. The ask is an AI platform that reads a travel document, checks its fields against rules and databases, detects tampering, verifies the face, and produces a risk score to help officers decide faster.
What to build
A document-screening platform with four modules: OCR extraction pulling the relevant fields from passports, visas, national IDs and permits; document validation checking those fields against official format and rule standards; a tampering-detection module — the core AI — flagging photo replacement, text manipulation and stamp forgery through image-forensics and metadata analysis; and face verification matching the document photo to the bearer, combining into a risk score that assists an officer rather than deciding automatically.
Smallest thing that wins the room
Scan a genuine specimen document and a tampered one, and show the platform extracting the fields, flagging the tampered photo and altered expiry date through image-forensic cues, verifying the face match, and producing a higher risk score for the tampered document.
How crowded this one gets
A guess, projected from the 2025 statements — the last year where both the submission counts and the winners were published.
Quieter than 33% of the 226 · #152 of 226 by expected field
A normal-sized field. Your idea has to be good, not miraculous.
Why: defence, intelligence and space bodies drew small fields.
This is a guess, not a fact
Nobody has published 2026’s numbers yet. This is an analysed estimate from last year’s pattern, so please do not take it as the truth — check the live counter on the SIH portal before you decide anything. The range covers the middle half of likely outcomes, so one statement in two lands outside it. Entry closes at 500 ideas per statement, so no range goes past that — a statement that reaches the cap fills and shuts rather than drawing an unlimited crowd. The model reads only three things a team can see before choosing — software or hardware, the theme, and what kind of body posted it — and those explain about a quarter of the variation in last year’s field sizes (R² 0.25 on held-out statements). Trust the band more than the number, and the ordering more than either. It cannot see how good your idea is, which is the part that actually decides it.
The scores
The number is the shorthand. The line under it is the reason.
Acceptance potential
3/5The specification is excellent and the tampering focus is the right differentiator, but there is no public tampered-document dataset so you construct your own manipulations and the detector only catches what you made, real forgeries are far subtler than synthetic ones, and an MHA judge will test whether it generalises beyond your examples.
Feasibility
3/5OCR, format validation and face verification are standard, and image-forensic tampering detection has established techniques, but the core tampering module is genuinely hard on real forgeries, and there is no public dataset of tampered travel documents, so you construct your own tampered examples and the detector only catches the manipulations you created.
Innovation scope
3/5OCR, validation and face matching are commodity, so the genuine room is the tampering-detection module the description itself names as the core innovation, which is where the real work and differentiation lie.
Clarity
5/5The description enumerates the four modules, the document types, the exact fields to extract, and the specific tampering use cases — photo replacement, text manipulation, stamp forgery — making the requirement precisely defined.
Effort
MassiveFour modules — OCR, validation, forensic tampering detection and face verification — plus the risk-scoring layer is a broad build, with the tampering detection the deep and demanding part.
Demo-ability
EasyA genuine document passing and a tampered one flagged with the specific manipulation highlighted and a higher risk score is a concrete, self-explanatory demo.
In its favour
- Green flag: OCR, MRZ validation and face verification are commodity components with mature libraries, so the surrounding modules are quick
- Green flag: The MRZ checksum on passports gives you deterministic validation that catches many alterations without any model
- Green flag: The description names tampering detection as the core innovation, so you know exactly where to invest
- Green flag: A genuine-versus-tampered side-by-side with the manipulation highlighted is a clean, convincing demo
Against it
- Red flag: No public tampered-travel-document dataset exists, so you construct your own manipulations and the detector only catches what you created
- Red flag: Real forgeries are far subtler than synthetic ones, so a detector trained on your tampering will miss sophisticated real fakes
- Red flag: Image-forensic cues like error-level analysis are unreliable and easily defeated, so overclaiming tampering detection is risky
- Red flag: Working with real identity documents raises privacy and data-handling concerns that constrain what you can use
What you will be writing
- OCR / MRZ extraction (Tesseract, PassportEye)
- Image-forensic tampering detection (ELA, noise, copy-move)
- Document format / MRZ checksum validation
- Face verification (ArcFace)
- Metadata analysis
- Risk-score fusion
- Document forensics
- Identity verification
- Border security
Prior art to read before you start
travel document tampering detection · identity document OCR and validation · face-to-document verification
Analysed by Claude Opus. Every score above is a judgment call with its reasoning attached — kindly cross-check this against the official statement on the SIH portal before your team commits to it.