101,944 research outputs found
Teaching Images in Headache: Thrombosis of Posterior Communicating Artery Aneurysm
No full textFrom the Nuffield Department of Clinical Neurosciences,
University of Oxford, Oxford, UK (M. Romoli); Neurology
Clinic, University of Perugia, S. Maria della Misericordia
Hospital, Perugia, Italy (M. Romoli, P. Sarchielli, G. Cardaioli,
and P. Calabresi); Neuroradiology Unit, University Hospital
of Perugia, S. Maria della Misericordia Hospital, Perugia,
Italy (A. Fiacca, R. Pantaleoni, and M. Hamam); IRCCS
Santa Lucia, Rome, Italy (P. Calabresi).
Address all correspondence to Michele Romoli, MD, Neurology
Clinic, University Hospital of Perugia, Piazzale Menghini 1,
Perugia, 06132, Italy, email: [email protected]
Accepted for publication August 29, 2018
Transcatheter aortic valve replacement and chronic kidney disease: Close friends or sworn enemies?
No full textsystem for contrast media volume reduction in percutaneous procedures: “Make kidney safe again”
No full textPrivacy-Friendly De-Authentication with BLUFADE: Blurred Face Detection
No full textIdeally, secure user sessions should start and end with authentication and de-Authentication phases, respectively. While the user must pass the former to start a secure session, the latter's importance is often ignored or underestimated. Dangling or unattended sessions expose users to well-known Lunchtime Attacks. To mitigate this threat, the research community focused on automated de-Authentication systems. Unfortunately, no single approach offers security, privacy, and usability. For instance, although facial recognition-based methods might be a good fit for security and usability, they violate user privacy by constantly recording the user and the surrounding environment.In this work, we propose BLUFADE, a fast, secure, and transparent de-Authentication system that takes advantage of blurred faces to preserve user privacy. We obfuscate a webcam with a physical blur layer and use deep learning algorithms to perform face detection continuously. To assess BLUFADE's practicality, we collected two datasets formed by 30 recruited subjects (users) and thousands of physically blurred celebrity photos. The former was used to train and evaluate the deauthentication system performances, the latter to assess the privacy and to increase variance in training data. We show that our approach outperforms state-of-The-Art methods in detecting blurred faces, achieving up to 95% accuracy. Furthermore, we demonstrate that BLUFADE effectively de-Authenticates users up to 100% accuracy in under 3 seconds, while satisfying security, privacy, and usability requirements.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Cyber Securit
recognition for privacy-friendly continuous authentication
No full textAuthentication and de-authentication phases should occur at the beginning and end of secure user sessions, respectively. A secure session requires the user to pass the former, but the latter is often underestimated or ignored. Unattended or dangling sessions expose users to well-known Lunchtime Attacks. To mitigate this threat, researchers focused on automated de-authentication systems, either as a stand-alone mechanism or as a result of continuous authentication failures. Unfortunately, no single approach offers security, privacy, and usability. Face-recognition methods, for example, may be suitable for security and usability, but they violate user privacy by continuously recording their actions and surroundings. In this work, we propose BLUFADER, a novel continuous authentication system that takes advantage of blurred face detection and recognition to fast, secure, and transparent de-authenticate users, preserving their privacy. We obfuscate a webcam with a physical blur layer and use deep learning algorithms to perform face detection and recognition continuously. To evaluate BLUFADER's practicality, we collected two datasets formed by 30 recruited subjects (users) and thousands of physically blurred celebrity photos. The de-authentication system was trained and evaluated using the former, while the latter was used to appraise the privacy and increase variance at training time. To guarantee the privacy-preserving effectiveness of the selected physical blurring filter, we show that state-of-the-art deblurring models are not able to revert our physical blur. Further, we demonstrate that our approach outperforms state-of-the-art methods in detecting blurred faces, achieving up to 95% accuracy. Moreover, BLUFADER effectively de-authenticates users up to 100% accuracy in under 3 seconds, while satisfying security, privacy, and usability requirements. Last, our continuous authentication face recognition module based on Siamese Neural Network preventively protect users from adversarial attacks, enhancing the overall system security.Cyber Securit
Your PIN is Mine: Uncovering Users' PINs at Point of Sale Machines
No full textPoint of Sale (PoS) machines have become extremely popular recently. In many economies, most transactions occur using them. Although PoS technology is evolving, PINs are still heavily used. In this paper, we perform a large-scale study to understand how difficult it is to uncover user PINs at PoS, even when the users cover the pad with their hands. Our study involves 142 participants, two types of PoS, and around 13,800 PINs. We develop machine learning techniques to infer PoS PINs by using hidden cameras. Our results show that uncovering PINs in PoS is more complex than in other cases where a user PIN is used, e.g., ATMs, because of the small pad area of PoS. Nevertheless, we could achieve more than 50% Top-3 accuracy for 4-digit PINs and 45% Top-3 accuracy for 5-digit PINs, even when the PIN is covered by the user's hand. We comment on the impact of the camera's position and PoS on the successful inference of the user's PINs. We also comment on the hardness of inferring PINs depending on the physical distance of digits and recommend what are good practices to generate PINs and cover PoS to make PIN inference difficult
We Can Hear Your PIN Drop: An Acoustic Side-Channel Attack on ATM PIN Pads
No full textPersonal Identification Numbers (PINs) are the most common user authentication method for in-person banking transactions at ATMs. The US Federal Reserve reported that, in 2018, PINs secured 31.4 billion transactions in the US, with an overall worth of US$ 1.19 trillion. One well-known attack type involves the use of cameras to spy on the ATM PIN pad during PIN entry. Countermeasures include covering the PIN pad with a shield or with the other hand while typing. Although this protects PINs from visual attacks, acoustic emanations from the PIN pad itself open the door for another attack type. In this paper, we show the feasibility of an acoustic side-channel attack (called PinDrop ) to reconstruct PINs by profiling acoustic signatures of individual keys of a PIN pad. We demonstrate the practicality of PinDrop via two sets of data collection experiments involving two commercially available metal PIN pad models and 58 participants who entered a total of 5,800 5-digit PINs. We simulated two realistic attack scenarios: (1) a microphone placed near the ATM (0.3 m away) and (2) a real-time attacker (with a microphone) standing in the queue at a common courtesy distance of 2 m. In the former case, we show that PinDrop recovers 96% of 4-digit, and up to 94% of 5-digits, PINs. Whereas, at 2 m away, it recovers up to 57% of 4-digit, and up to 39% of 5-digit PINs in three attempts. We believe that these results are both significant and worrisome
Failed Amplatzer Septal Occluder device implantation due to an embryonic septal remnant.
No full textTranscatheter closure of complex iatrogenic ventricular septal defect: A case report
No full textBackground: Iatrogenic membranous ventricular septal defects (VSDs) are rare complications of cardiothoracic surgery, such as septal myectomy for hypertrophic obstructive cardiomyopathy (HOCM). Transcatheter closure is considered an appealing alternative to surgery, given the increased mortality associated with repeated surgical procedures, but reports are extremely limited. Case summary: We herein report the case of a 63-year-old woman with HOCM who underwent successful percutaneous closure of an iatrogenic VSD after septal myectomy. Two percutaneous techniques are discussed, namely the 'muscular anchoring' and the 'buddy wire delivery', aimed at increasing support and providing stability to the system during percutaneous intervention. Discussion: Transcatheter closure represents an attractive minimally invasive approach for the management of symptomatic iatrogenic VSDs. The new techniques described could help operators to cross tortuous and tunnelled defects and to deploy closure devices in case of complex VSD anatomy
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