The Interplay of AI and Biometrics: Challenges and Opportunities

Berghoff, Christian; Neu, Matthias; Twickel, Arndt von

doi:10.1109/mc.2021.3084656

Cited by 6 publications

(4 citation statements)

References 20 publications

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“…Datasets here make use of geographical information. For data collected in Los Angeles and Denver, they achieved an accuracy of 54% and 51%, respectively [9,39].…”

Section: Literature Reviewmentioning

confidence: 99%

“…As an introduction to common techniques for locating and identifying action at sporting events, researchers provide an overview of the field. The authors proposed segmenting the activity recognition pipeline into three distinct stages: feature extraction, deep learning depiction of clips, and sport classification [5][6][7]. Using the UCF-supplied Sports dataset as a benchmark, they evaluated the contentious issue.…”

Section: Literature Reviewmentioning

confidence: 99%

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Deep Learning Algorithm for Detecting and Analyzing Criminal Activity

Mehsen¹

2023

IJC

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When applied to an entire field, automation and autonomous systems are among the rare creative superpowers capable of catapulting progress at an exponential rate. The arrival of machine intelligence will give such automated machines the intelligence to perform their tasks with power of outcome, drastically reducing the need for human intervention in redundant processes. Large-scale technological progress can be traced back to responsibilities that are simplified and, as a result, more easily distinguished by means of automation. In accordance with these guidelines, we propose creating a product that eliminates or significantly reduces the need for human intervention in primary issue statements that can be automated and processed. The public safety infrastructure of today relies on surveillance cameras, but these devices are merely video recorders; they have no intelligence of their own. Automated video streams are now required for automatic event detection thanks to the massive amount of data produced by surveillance cameras. The project's main objective is to increase public safety through the mechanization of crime measurement and review using actual Closed-Circuit Television footage (CCTV). This is achieved by assigning the task of recognizing criminal behavior to a system that can do so automatically, allowing for more precise tracking. In this study, we present a model with a precision of 0.95 for assault and 0.97 for abuse.

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“…Datasets here make use of geographical information. For data collected in Los Angeles and Denver, they achieved an accuracy of 54% and 51%, respectively [9,39].…”

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Deep Learning Algorithm for Detecting and Analyzing Criminal Activity

Mehsen¹

2023

IJC

View full text Add to dashboard Cite

show abstract

“…Biometrics are measurements of a person's unique physical or behavioral characteristics. These measurements, stored in a form of encrypted code, are used as a personal identifier [18]. Biometricbased systems generally compare the existing representatives of biometrics with the provided one, to determine if there is a match or not.…”

Section: Biometric-based Applications 21 Overviewmentioning

confidence: 99%

“…'Voice ID' [54] technology, for instance, is a speaker authentication technology that analyzes incoming audio signals and compares them to previously stored 'voice representations' and produces a confidence score of how closely the speaker's voice sample matches the voice representations. Despite biometric authentication/access controls enhancement to many applications, there are also associated privacy and security concerns [14,15,18,95]. There are very few existing ways to provide privacy to users when the risk is that providers of services may be 'honest-but-curious' [65] or simply untrustworthy (considering their business practices and/or usage intent for user data).…”

Section: Introductionmentioning

confidence: 99%

Locally Authenticated Privacy-preserving Voice Input

Aloufi¹,

Nautsch²,

Haddadi³

et al. 2022

Preprint

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Increasing use of our biometrics (e.g., fingerprints, faces, or voices) to unlock access to and interact with online services raises concerns about the trade-offs between convenience, privacy, and security. Service providers must authenticate their users, although individuals may wish to maintain privacy and limit the disclosure of sensitive attributes beyond the authentication step, e.g., when interacting with Voice User Interfaces (VUIs). Preserving privacy while performing authentication is challenging, particularly where adversaries can use biometric data to train transformation tools (e.g., 'deepfaked' speech) and use the faked output to defeat existing authentication systems. In this paper, we take a step towards understanding security and privacy requirements to establish the threat and defense boundaries. We introduce a secure, flexible privacypreserving system to capture and store an on-device fingerprint of the users' raw signals (i.e., voice) for authentication instead of sending/sharing the raw biometric signals. We then analyze this fingerprint using different predictors, each evaluating its legitimacy from a different perspective (e.g., target identity claim, spoofing attempt, and liveness). We fuse multiple predictors' decisions to make a final decision on whether the user input is legitimate or not. Validating legitimate users yields an accuracy rate of 98.68% after cross-validation using our verification technique. The pipeline runs in tens of milliseconds when tested on a CPU and a single-core ARM processor, without specialized hardware.

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Biometrics

Ding,

Nunes,

Bettendorff

et al. 2023

Trends in Data Protection and Encryption Technologies

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In biometrics, physiological characteristics (e.g., fingerprints, facial features, voices) are measured to identify individuals. Since biometry does not share the same characteristics as other authentication techniques, such as passwords or PIN codes, it is widely used as a second-factor authentication. A fully integrated solution offers significant advantages as it aligns biometric hardware, key generation, validation, and storage with the cryptographic provider, providing a coherent control environment. As a result, designing and implementing secure biometric authentication methods requires highly specialized technical expertise and experience. In addition, as biometrics become prevalent and their integration with artificial intelligence systems increases, ethical and regulatory questions become more pressing.

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The Interplay of AI and Biometrics: Challenges and Opportunities

Cited by 6 publications

References 20 publications

Deep Learning Algorithm for Detecting and Analyzing Criminal Activity

Deep Learning Algorithm for Detecting and Analyzing Criminal Activity

Locally Authenticated Privacy-preserving Voice Input

Biometrics

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