Document Fraud: Will Your Identity Be Secure in the Twenty-first Century?

Baechler, Simon

doi:10.1007/s10610-020-09441-8

Cited by 11 publications

(5 citation statements)

References 31 publications

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“…However, this sparse uptake sits in very sharp contrast with AI literature, which boasts of a plethora of person identity verification and fraud detection mechanisms using techniques such as face recognition (Li et al 2020), fingerprint recognition, iris recognition (Mostofa et al 2021), retina scans, and counterfeit document detection (Berenguel Centeno et al 2019;Berenguel Centeno 2019), among others. Given observations of improvements in document forging and an increasing realization that the promise of tamper-proof official documents remains a holy grail (Baechler 2020), it may be reasonable to expect a shift of focus from voter authentication based on identity documents to biometricbased authentication.…”

Section: Extant or Potential Ai Usagementioning

confidence: 99%

“…2019; Berenguel Centeno 2019), among others. Given observations of improvements in document forging and an increasing realization that the promise of tamper‐proof official documents remains a holy grail (Baechler 2020), it may be reasonable to expect a shift of focus from voter authentication based on identity documents to biometric‐based authentication.…”

Section: Voter Authenticationmentioning

confidence: 99%

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AI and core electoral processes: Mapping the horizons

Simoes,

MacCarthaigh

2023

AI Magazine

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It is well documented that there has been significant enthusiasm across the globe in respect of using AI for all forms of social activity. However, the electoral process – the time, place, and manner of elections within democratic nations – is one of few sectors in which there has been limited penetration of AI. Electoral management bodies in many countries have recently started exploring and deliberating over the use of AI in the electoral process. In this paper, we consider five avenues within the core electoral process which have potential for AI usage, and map the challenges involved in using AI within them. These five avenues are: voter list maintenance, determining polling booth locations, polling booth protection processes, voter authentication, and video monitoring of elections. Within each avenue, we lay down the context, illustrate current or potential usage of AI, and discuss extant or potential ramifications of AI usage, as well as potential directions for mitigating risks when considering AI usage. We believe that the scant current usage of AI within electoral processes provides a very rare opportunity to deliberate on the risks and mitigation possibilities prior to actual and widespread AI deployment. This paper is an attempt to map the horizons of risks and opportunities in using AI within electoral processes and to help shape the debate around the topic.

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Section: Extant or Potential Ai Usagementioning

confidence: 99%

Section: Voter Authenticationmentioning

confidence: 99%

AI and core electoral processes: Mapping the horizons

Simoes,

MacCarthaigh

2023

AI Magazine

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“…This makes them highly attractive assets for individuals deprived of such benefits. Document fraud is thus a convenient solution, sometimes the only one, to get past identity checks and controls and access to the places or services sought [ 1 ]. But identity documents are not the only documents used for obtaining benefits and are thus not the only ones affected by forgeries.…”

Section: Introductionmentioning

confidence: 99%

Analysing the digital transformation of the market for fake documents using a computational linguistic approach

Degeneve

Longhi

Rossy

2022

Forensic Science International: Synergy

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“…Fraudulent identity and travel documents can be linked to many criminal activities, including financial fraud, human trafficking, and terrorism [1]. Most of the current detection for passport authenticity relies on manual [2], which involves the devices such as UV lamp [3], IR detector etc.…”

Section: Introductionmentioning

confidence: 99%

PSFNet: A Deep Learning Network for Fake Passport Detection

Jia

Lin

et al. 2022

IEEE Access

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Passport security feature classification has always been an important issue in border checks. Current manual methods struggle to achieve satisfactory results in terms of consistency and stability for security features with high similarity. For this reason, this study designs and develops a deep learningbased passport security feature classification model that can identify similar security features. The proposed model is based on the ShuffleNet v2, and its network structure is further optimized by two aspects for our task. Firstly, we embed pixel attention in the model to enable the network to better focus on important features with discriminatory power. Secondly, we introduce focal loss to relieve the overfitting problem caused by data imbalance. Finally, the superiority of the proposed classification algorithm is verified with the constructed passport security features data. The classification accuracy of the proposed algorithm is enhanced by 0.8%. The experimental results show that the classification accuracy is as high as 95.5%.INDEX TERMS Passport, security feature detection, deep learning.

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Document Fraud: Will Your Identity Be Secure in the Twenty-first Century?

Cited by 11 publications

References 31 publications

AI and core electoral processes: Mapping the horizons

AI and core electoral processes: Mapping the horizons

Analysing the digital transformation of the market for fake documents using a computational linguistic approach

PSFNet: A Deep Learning Network for Fake Passport Detection

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