# AI-based Identity Fraud Detection: A Systematic Review

arXiv: https://arxiv.org/abs/2501.09239 · HTML: https://mlsanity.com/paper/2501.09239/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 88 references that are in the map.
- First posted on arXiv 16 Jan 2025.

## Builds on (most cited first, 7 of 7)

- [Deepfakes Generation and Detection: State-of-the-art, open challenges, countermeasures, and way forward](https://mlsanity.com/paper/2103.00484/) (2021)
- [Detecting Deep-Fake Videos from Appearance and Behavior](https://mlsanity.com/paper/2004.14491/) (2020)
- [Deep Models and Shortwave Infrared Information to Detect Face Presentation Attacks](https://mlsanity.com/paper/2007.11469/) (2020)
- [Restricted Black-box Adversarial Attack Against DeepFake Face Swapping](https://mlsanity.com/paper/2204.12347/) (2022)
- [Synthetic ID Card Image Generation for Improving Presentation Attack Detection](https://mlsanity.com/paper/2211.00098/) (2022)
- [Detection and Localization of Facial Expression Manipulations](https://mlsanity.com/paper/2103.08134/) (2021)
- [FIDO2 the Rescue? Platform vs. Roaming Authentication on Smartphones](https://mlsanity.com/paper/2302.07777/) (2023)

## Built on by (most cited first, 2 of 2)

- [Agentic AI Microservice Framework for Deepfake and Document Fraud Detection in KYC Pipelines](https://mlsanity.com/paper/2601.06241/) (2026)
- [From Measurement to Mitigation: Quantifying and Reducing Identity Leakage in Image Representation Encoders with Linear Subspace Removal](https://mlsanity.com/paper/2604.05296/) (2026)

## Abstract (arXiv)

With the rapid development of digital services, a large volume of personally identifiable information (PII) is stored online and is subject to cyberattacks such as Identity fraud. Most recently, the use of Artificial Intelligence (AI) enabled deep fake technologies has significantly increased the complexity of identity fraud. Fraudsters may use these technologies to create highly sophisticated counterfeit personal identification documents, photos and videos. These advancements in the identity fraud landscape pose challenges for identity fraud detection and society at large. There is a pressing need to review and understand identity fraud detection methods, their limitations and potential solutions. This research aims to address this important need by using the well-known systematic literature review method. This paper reviewed a selected set of 43 papers across 4 major academic literature databases. In particular, the review results highlight the two types of identity fraud prevention and detection methods, in-depth and open challenges. The results were also consolidated into a taxonomy of AI-based identity fraud detection and prevention methods including key insights and trends. Overall, this paper provides a foundational knowledge base to researchers and practitioners for further research and development in this important area of digital identity fraud.