Fingerprint recognition with embedded presentation attacks detection: are we ready?

The diffusion of fingerprint verification systems for security applications makes it urgent to investigate the embedding of software-based presentation attack detection algorithms (PAD) into such systems. Companies and institutions need to know whether such integration would make the system more “secure” and whether the technology available is ready, and, if so, at what operational working conditions. Despite significant improvements, especially by adopting deep learning approaches to fingerprint PAD, current research did not state much about their effectiveness when embedded in fingerprint verification systems. We believe that the lack of works is explained by the lack of instruments to investigate the problem, that is, modeling the cause-effect relationships when two non-zero error-free systems work together. Accordingly, this paper explores the fusion of PAD into verification systems by proposing a novel investigation instrument: a performance simulator based on the probabilistic modeling of the relationships among the Receiver Operating Characteristics (ROC) of the two individual systems when PAD and verification stages are implemented sequentially. As a matter of fact, this is the most straightforward, flexible, and widespread approach. We carry out simulations on the PAD algorithms’ ROCs submitted to the most recent editions of LivDet (2017-2019), the state-of-the-art NIST Bozorth3, and the top-level Veryfinger 12 matchers. Reported experiments explore significant scenarios to get the conditions under which fingerprint matching with embedded PAD can improve, rather than degrade, the overall personal verification performance

Mitigating Sensor and Acquisition Method-Dependence of Fingerprint Presentation Attack Detection Systems by Exploiting Data from Multiple Devices

The problem of interoperability is still open in fingerprint presentation attack detection (PAD) systems. This involves costs for designers and manufacturers who intend to change sensors of personal recognition systems or design multi-sensor systems, because they need to obtain sensor-specific spoofs and retrain the system. The solutions proposed in the state of the art to mitigate the problem still require data from the target sensor and are therefore not exempt from the problem of obtaining new data. In this paper, we provide insights for the design of PAD systems thanks to an overview of an interoperability analysis on modern systems: hand-crafted, deep-learning-based, and hybrid. We investigated realistic use cases to determine the pros and cons of training with data from multiple sensors compared to training with single sensor data, and drafted the main guidelines to follow for deciding the most convenient PAD design technique depending on the intended use of the fingerprint identification/authentication system

Fingerprint Adversarial Presentation Attack in the Physical Domain

With the advent of the deep learning era, Fingerprint-based Authentication Systems (FAS) equipped with Fingerprint Presentation Attack Detection (FPAD) modules managed to avoid attacks on the sensor through artificial replicas of fingerprints. Previous works highlighted the vulnerability of FPADs to digital adversarial attacks. However, in a realistic scenario, the attackers may not have the possibility to directly feed a digitally perturbed image to the deep learning based FPAD, since the channel between the sensor and the FPAD is usually protected. In this paper we thus investigate the threat level associated with adversarial attacks against FPADs in the physical domain. By materially realising fakes from the adversarial images we were able to insert them into the system directly from the “exposed” part, the sensor. To the best of our knowledge, this represents the first proof-of-concept of a fingerprint adversarial presentation attack. We evaluated how much liveness score changed by feeding the system with the attacks using digital and printed adversarial images. To measure what portion of this increase is due to the printing itself, we also re-printed the original spoof images, without injecting any perturbation. Experiments conducted on the LivDet 2015 dataset demonstrate that the printed adversarial images achieve ∼ 100% attack success rate against an FPAD if the attacker has the ability to make multiple attacks on the sensor (10) and a fairly good result (∼ 28%) in a one-shot scenario. Despite this work must be considered as a proof-of-concept, it constitutes a promising pioneering attempt confirming that an adversarial presentation attack is feasible and dangerous

Analysis of Score-Level Fusion Rules for Deepfake Detection

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

Development of Technologies for the Detection of (Cyber)Bullying Actions: The BullyBuster Project

Bullying and cyberbullying are harmful social phenomena that involve the intentional, repeated use of power to intimidate or harm others. The ramifications of these actions are felt not just at the individual level but also pervasively throughout society, necessitating immediate attention and practical solutions. The BullyBuster project pioneers a multi-disciplinary approach, integrating artificial intelligence (AI) techniques with psychological models to comprehensively understand and combat these issues. In particular, employing AI in the project allows the automatic identification of potentially harmful content by analyzing linguistic patterns and behaviors in various data sources, including photos and videos. This timely detection enables alerts to relevant authorities or moderators, allowing for rapid interventions and potential harm mitigation. This paper, a culmination of previous research and advancements, details the potential for significantly enhancing cyberbullying detection and prevention by focusing on the system’s design and the novel application of AI classifiers within an integrated framework. Our primary aim is to evaluate the feasibility and applicability of such a framework in a real-world application context. The proposed approach is shown to tackle the pervasive issue of cyberbullying effectively

The future of Cybersecurity in Italy: Strategic focus area

This volume has been created as a continuation of the previous one, with the aim of outlining a set of focus areas and actions that the Italian Nation research community considers essential. The book touches many aspects of cyber security, ranging from the definition of the infrastructure and controls needed to organize cyberdefence to the actions and technologies to be developed to be better protected, from the identification of the main technologies to be defended to the proposal of a set of horizontal actions for training, awareness raising, and risk management

Persistent elevation of urine aquaporin-2 during water loading in a child with nephrogenic syndrome of inappropriate antidiuresis (NSIAD) caused by a R137L mutation in the V2 vasopressin receptor

Nephrogenic Syndrome of Inappropriate Antidiuresis (NSIAD) is a novel disease caused by a gain-of-function mutation in the V2 vasopressin receptor (V2R), which results in water overload and hyponatremia. We report the effect of water loading in a 3-year old boy with NSIAD, diagnosed in infancy, to assess urine aquaporin-2 (AQP2) excretion as a marker for V2R activation, and to evaluate the progression of the disease since diagnosis. The patient is one of the first known NSIAD patients and the only patient with a R137L mutation. Patient underwent a standard water loading test in which serum and urine sodium and osmolality, serum AVP, and urine AQP2 excretion were measured. The patient was also evaluated for ad lib fluid intake before and after the test. This patient demonstrated persistent inability to excrete free water. Only 39% of the water load (20 ml/kg) was excreted during a 4-hour period (normal ≥ 80-90%). Concurrently, the patient developed hyponatremia and serum hypoosmolality. Serum AVP levels were detectable at baseline and decreased one hour after water loading; however, urine AQP2 levels were elevated and did not suppress normally during the water load. The patient remained eunatremic but relatively hypodipsic during ad lib intake. In conclusion, this is the first demonstration in a patient with NSIAD caused by a R137L mutation in the V2R that urine AQP2 excretion is inappropriately elevated and does not suppress normally with water loading. In addition, this is the first longitudinal report of a pediatric patient with NSIAD diagnosed in infancy who demonstrates the ability to maintain eunatremia during ad lib dietary intake