Attacks on heartbeat-based security using remote photoplethysmographySeepers, R.M.; Wang, W.; de Haan, G.; Sourdis, I.; Strydis, C. • A submitted manuscript is the author's version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website.• The final author version and the galley proof are versions of the publication after peer review.• The final published version features the final layout of the paper including the volume, issue and page numbers. Link to publication General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.• Users may download and print one copy of any publication from the public portal for the purpose of private study or research.• You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal ? Take down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Abstract-The time interval between consecutive heartbeats (interpulse interval, IPI) has previously been suggested for securing mobile-health (mHealth) solutions. This time interval is known to contain a degree of randomness, permitting the generation of a time-and person-specific identifier. It is commonly assumed that only devices trusted by a person can make physical contact with him/her, and that this physical contact allows each device to generate a similar identifier based on its own cardiac recordings. Under these conditions, the identifiers generated by different trusted devices can facilitate secure authentication. Recently, a wide range of techniques have been proposed for measuring heartbeats remotely, a prominent example of which is remote photoplethysmography (rPPG). These techniques may pose a significant threat to heartbeat-based security, as an adversary may pretend being a trusted device by generating a similar identifier without physical contact, thus bypassing one of the core security conditions. In this paper, we assess the feasibility of such remote attacks using state-of-the-art rPPG methods. Our evaluation shows that rPPG has similar accuracy as contact PPG and, thus, forms a substantial threat to heartbeat-based-security systems that permit trusted devices to obtain their identifiers from contact PPG recordings. Conversely, rPPG ca...

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Objective: Detecting discomfort status of infants is particularly clinically relevant. Late treatment of discomfort infants can lead to adverse problems such as abnormal brain development, central nervous system damage and changes in responsiveness of the neuroendocrine and immune systems to stress at maturity. In this study, we exploit deep convolutional neural network (CNN) algorithms to address the problem of discomfort detection for infants by analyzing their facial expressions. Approach: A dataset of 55 videos about facial expressions, recorded from 24 infants, is used in our study. Given the limited available data for training, we employ a pre-trained CNN model, which is followed by fine-tuning the networks using a public dataset with labeled facial expressions (the shoulder-pain dataset). The CNNs are further refined with our data of infants. Main results: Using a two-fold cross-validation, we achieve an area under the curve (AUC) value of 0.96, which is substantially higher than the results without any pre-training steps (AUC = 0.77). Our method also achieves better results than the existing method based on handcrafted features. By fusing individual frame results, the AUC is further improved from 0.96 to 0.98. Significance: The proposed system has great potential for continuous discomfort and pain monitoring in clinical practice.

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This work is funded by the scientific and technological research council of Turkey (TÜBITAK) under grand 119E392.