Identification Document Liveness Detection
ID Liveness Detection: General Overview
Document liveness detection refers to the verification that a document presented remotely is authentic and real. This is necessary to verify people, detect forgeries, as well as prevent potential digital crime. There are various types of fraud that involve fake document usage online. These include identity theft, money laundering, illegal purchasing of restricted goods or prescribed medicine, accessing private data, and so on. Terrorism assistance and sponsoring is another critical threat, as by using a physical fake ID, an attacker can purchase tickets online and obtain access to airplanes, public events and critical areas while keeping their real identity hidden from the authorities.
ID-based attacks also vary in ways and means. Presentation Attacks (PA) imply that a malicious actor can show a printed or digital photo of someone else’s likeness to a system's sensors if the remote identity proofing (RIDP) requires a selfie to complete authorization. Another method of attack is Face morphing. Although it is not an attack type on its own, it is gaining popularity among malicious actors. By combining facial features of two or more people, they can produce an ID photo or fake selfie that has a possibility of being authorized as both/multiple applicants.
Another attack directly involves fake, stolen or expired documents. To detect them, a document liveness check is required. It analyzes various properties of a presented ID, including its 3D properties, expiration/issuance date, coat of arms, holograms, stamps and other security details.
A noteworthy case of online ID fraud took place in 2020 when a perpetrator used multiple accounts, fake driver licenses and wigs to spoof ID.me — the digital identity verification platform employed by US government agencies. According to their report, $900,000 were claimed on unemployment grounds before the scam was finally detected and stopped. The eventual goal of the heist was to illegally claim $2.5 million.
Document liveness detection also plays a major role in Know Your Customer (KYC), Customer Due Diligence (CDD), and Anti-Money Laundering (AML) security standards accepted by payment providers, banks, e-commerce, and various institutions worldwide.
Document Authenticity vs Originality
The word ‘authentic’ means reliable or genuine which can be equivalent to original, but not original. While, the word ‘original’ means the first or the earliest of all, which exists from its birth, the real true form from which replicas can be made, but those won’t be the original ones.
In KYC the remote checking of the Authenticity of the document means checking the correctness of the form, structure, bar-codes, and other security features presented on the photo of the document while checking the Originality of the document means that the original document (not its digital or printed copy) is presented in front of the camera.
To address the issue of rising threats against online identity documentation, multiple solutions and countermeasures are proposed.
IDLive Doc, a solution developed by ID R&D, focuses on preventing screen replay attacks. These attacks stem from a common criminal tactic: perpetrators obtain an image of a real ID or produce its copy in a software like Photoshop or GIMP. Then, the fabricated image is presented to the sensors of a RIDP system using a high-resolution screen.
Typically, a human eye cannot spot a forgery if the "high resolution retina displays" are used for the attack. Therefore, an accurate and quick liveness check is required. IDLive Doc is based on a passive liveness detection method, hence the procedure requires a few seconds without imposing any additional verification challenges. ID R&D has not undisclosed which technical solutions are used in IDLive Doc, except that a "unique Deep Neural Network-based approach" serves as its basis. It allows detecting liveness from just a single image. It is claimed to be capable of performing "artifact detection", which possibly refers to editing artifacts, double compression, and other similar clues.
Smart Engines introduced two solutions for document liveness detection.
Computation Document Forensic AI
Computation Document Forensic AI (CDF AI) is a GDPR-compliant multipurpose AI system. It specializes in full-scale document analysis, providing a group of AI models for analyzing images, document liveness holograms, templates, stamps and seals, text and font etc. These AI models search for potential anomalies left by image editing, artificial synthesis, digital or physical copying, and other procedures. The detection process is based on optical and infrared range analysis, which can be compared to a standard UV lamp check widely used in many countries. Another interesting feature of CDF AI is providing complete autonomy to the user. All data and analysis tools are deployed and stored on the user’s gadget —smartphone or tablet etc. As a result, this prevents data leakages and provides a convenient offline workflow.
Smart ID Engine
Smart ID Engine is an ID liveness detector that analyzes various properties of a document. These properties include document geometry, holograms and monograms, machine-readable zones (MRZ), and other security components. Besides, Smart ID Engine can attest the document state by capturing a video stream or creating separate frames. The solution can process documents from 210 jurisdictions worldwide, while supporting 99 languages, including Chinese and Urdu. Working in a passive mode, it also shows high performance speed: taking 250 ms per frame to recognize a US driver’s license.
CheckScan is a novelty ID verification approach introduced in March 2022. The idea suggests the possibility of distinguishing a legitimate document from a fake by analyzing its quality. It follows two stages.
Feature extraction. This stage is based on Fast Fourier Transform (FFT). Basically, an image in question is separated into a group of blocks that do not overlap. Then, the FFT magnitude spectrum will be estimated for every single block. As a result, FFT magnitude peaks are extracted as discriminative features.
Hash construction. Previously obtained magnitude peaks are quantized into binary codes based on the peak coordinates. In this way good discriminates can be achieved. They are capable of distinguishing bona fide and fabricated identification documents.
Dataset for Document Recognition
To assist development of document liveness detection tools, a number of datasets have been developed. However, if compared to deepfake datasets, their number is quite limited. The known examples are LRDE Identity Document Image Database (LRDE IDID), Brazilian Identity Document Dataset (BID Dataset), SmartDoc (partly), DLC-2021, and Mobile Identity Document Video Dataset (MIDV) presented in a few generations.
Common challenges in all existing datasets are insufficient sample materials or feature IDs with blurred faces, precise cropping, nonexistent background, and so on. The MIDV dataset seeks to fix that, while also providing a large number of IDs and a variety of document types and templates, fonts, ethnicities, ornaments and other security elements. The dataset also applies different capture methods such as office scanners and smartphones etc.
The ID photos were created with Generated Photos based on a StyleGAN2 approach. While the datasets preserve original templates that can be found within the European Union, their names and additional elements, like signatures, are purposely falsified.
Notably, ID photos are presented both in color and grayscale, which is a common case with authentic documents. Furthermore, a select number of samples were photographed in "real life" ambiance — on the ground, desk, keyboard — to increase the difficulty for the recognition system.
Document Liveness Challenge
Document liveness challenges are an important part of increasing awareness about ID fraud and finding potential solutions. The first competition dubbed ICMV Document Liveness Challenge 2021 was held in 2021 by Smart Engines in collaboration with RAS and Laboratoire Informatique. The challenge was divided into three parts:
- Laminated documents & unlaminated gray copies
- Documents photographed with a smart gadget
- Unlaminated copies in color.
The DLC datasets included 1424 videos, each one captured vertically with an average length of 5 seconds. The samples were presented in the form of videos, separate frames and markups. The competition is held regularly with the upcoming challenge to take place in November 2022, welcoming researchers in the area to showcase their solutions.
- Inside the Brussels flat where terrorists scored fake IDs
- Face Morphing, a Modern Threat to Border Security: Recent Advances and Open Challenges
- A guide to getting remote identity verification right
- ID.me gathers lots of data besides face scans, including locations. Scammers still have found a way around it
- Who is ID.me?
- What is Customer Due Diligence (CDD)?
- IDLive Doc
- Standalone universal ID document liveness detector launched by ID R&D
- General Data Protection Regulation
- Understanding UV Security: How and Why It’s Used on Payment Documents
- Smart Engines has launched document liveness detection for ID documents scanning
- Smart Engines has launched a new generation of recognition systems with document authentication and biometric verification
- CheckScan: a reference hashing for identity document quality detection
- LRDE Identity Document Image Database
- Brazilian Identity Document Dataset
- Mobile Identity Document Video Dataset
- A synthesized face created with Generated Photos
- MIDV-2020: A Comprehensive Benchmark Dataset for Identity Document Analysis
- Fall digital identity events announced by Goode Intelligence, Future Identity, EAB, Smart Engines
- Russian Academy of Sciences by Wikipedia
- The 15th International Conference on Machine Vision