Fusion, Sensor-Level

Noore, Afzel; Singh, Richa; Vasta, Mayank

doi:10.1007/978-0-387-73003-5_156

Cited by 2 publications

(1 citation statement)

References 11 publications

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“…Ensemble methods and multi-modal or uni-modal fusion are constantly being exploited in many fields of pattern recognition to improve the ability to generalize and deal with intra-class variations and inter-class similarity. Fusion can be performed at various levels of a classification system, including at the sensor level, feature level, score level and decision level [27][28][29].…”

Section: Ensemble Methods For Deepfake Detectionmentioning

confidence: 99%

Analysis of Score-Level Fusion Rules for Deepfake Detection

et al. 2022

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Deepfake detection is of fundamental importance to preserve the reliability of multimedia communications. Modern deepfake detection systems are often specialized on one or more types of manipulation but are not able to generalize. On the other hand, when properly designed, ensemble learning and fusion techniques can reduce this issue. In this paper, we exploit the complementarity of different individual classifiers and evaluate which fusion rules are best suited to increase the generalization capacity of modern deepfake detection systems. We also give some insights to designers for selecting the most appropriate approach.

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Section: Ensemble Methods For Deepfake Detectionmentioning

confidence: 99%

Analysis of Score-Level Fusion Rules for Deepfake Detection

et al. 2022

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Energy‐efficient multi‐biometric system for Internet of Things using trust management

et al. 2021

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The traditional password-based authentication systems are easy to breach because they rely on what the user knows and not on who is the user. This makes them prone to impersonation, as the culprit can gain access to the system once they knew the user's password, which led to the development of biometric-based authentication systems. Nonetheless, noisy data, intra-, and inter-class variations render these systems inaccurate. However, combining multiple biometric traits (multi-biometric systems) promises to increase the accuracy of biometric systems. But, this leads to an overhead in energy and latency in the system. These overheads are frowned at in embedded systems, especially those deployed in remote areas. Here, the authors propose a trust management-based multi-biometric system. The system switches between a uni-biometric and a multibiometric system to achieve high accuracy and reduced overhead (i.e. energy and latency). The system uses the uni-biometric technique for trusted users and the multibiometric method for untrustworthy users who are zero-effort impostors. It is found to reduce the false positive rate by 0.29 of Fingerprint uni-biometric system and the false negative rate by a factor of 0.5 of the multi-biometric system. Also, the energy consumption is reduced by a factor of 0.2 of the multi-biometric system.

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Fusion, Sensor-Level

Cited by 2 publications

References 11 publications

Analysis of Score-Level Fusion Rules for Deepfake Detection

Analysis of Score-Level Fusion Rules for Deepfake Detection

Energy‐efficient multi‐biometric system for Internet of Things using trust management

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