Toward Higher Levels of Assurance in Remote Identity Proofing

Nanda, Ashish; Shah, Syed Wajid Ali; Jeong, Jongkil Jay; Doss, Robin; Webb, Julian

doi:10.1109/mce.2023.3256640

Cited by 1 publication

(5 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recent significant advancements in computer vision and image generation using generative adversarial networks and diffusion models combined with their malicious use for manipulating faces in images, spreading fake news, and hacking remote identity-proofing systems that rely on a user's face for proofing have created an urgent need for methods that can reliably detect face manipulation [3]. To address this need, many efforts have been devoted to creating face forgery detection datasets to train deep learning models [7].…”

Section: Related Workmentioning

confidence: 99%

“…purposes. Attackers can use several methods to compromise eKYC systems by exploiting identity documents 3 .…”

Section: Spoofing Ekyc With Deepfakesmentioning

confidence: 99%

“…In the past, identity proofing was typically done in person, with individuals presenting physical documents such as a driver's license or passport to prove their identity. However, with the increasing prevalence of remote work and online interactions, there is a growing need for remote identity-proofing systems [2], [3].…”

Section: Introductionmentioning

confidence: 99%

“…It enables individuals to prove their identity from anywhere at any time, without needing to present documents or travel to a specific location physically. Various remote identity-proofing methods exist, including electronic document submission, biometric authentication, and identity verification [3] [4] [5]. Each method has advantages and disadvantages, and the most appropriate method depends on the specific needs and requirements of the organization or process in question.…”

Section: Introductionmentioning

confidence: 99%

“…The rapid advancement of enabling technologies has both positive and negative implications. While technological progress improves remote identity-proofing systems, it also provides fraudsters with tools and knowledge to manipulate these systems [3]. Identity fraud remains a widespread and concerning issue in today's digital era, encompassing the unauthorized acquisition and misuse of someone else's personal and financial data for fraudulent purposes.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

eKYC-DF: A Large-Scale Deepfake Dataset for Developing and Evaluating eKYC Systems

Felouat,

Nguyen,

et al. 2024

IEEE Access

View full text Add to dashboard Cite

The reliability of remote identity-proofing systems (i.e., electronic Know Your Customer, or eKYC, systems) is challenged by the development of deepfake generation tools, which can be used to create fake videos that are difficult to detect using existing deepfake detection models and are indistinguishable by facial recognition systems. This poses a serious threat to eKYC systems and a danger to individuals' personal information and property. Existing deepfake datasets are not particularly appropriate for developing and evaluating eKYC systems, which require specific motions, such as head movement, for liveness detection. Furthermore, they do not contain ID information or protocols for facial verification evaluation, which is vital for eKYC. We found that eKYC systems without the ability to detect deepfakes can be easily compromised. We have thus created a large-scale collection of high-quality fake videos (more than 228,000 videos) that are diverse in terms of age, gender, and ethnicity, plus a corresponding facial image subset. The videos include a variety of head movements and facial expressions. This large collection of high-quality diverse videos is well-suited for developing and evaluating various tasks related to eKYC systems. Furthermore, we provide protocols for traditional deepfake detection and facial verification, which are widely used in eKYC systems. It is worth mentioning that systematic evaluation of facial recognition systems on deepfake detection has not been reported. The entire eKYC-DF dataset, evaluation toolkit, and trained models will be open access for research purposes.

show abstract

Section: Related Workmentioning

confidence: 99%

“…purposes. Attackers can use several methods to compromise eKYC systems by exploiting identity documents 3 .…”

Section: Spoofing Ekyc With Deepfakesmentioning

confidence: 99%