1,721,237 research outputs found
On the transferability of adversarial perturbation attacks against fingerprint based authentication systems
No full textThe growing availability of cheap and reliable fingerprint acquisition scanners is resulting in an increasing spread of Fingerprint-based Authentication Systems (FAS) in consumer electronics. This has giving rise to a new wave in research on both smarter spoofing attacks, aimed to bypass a FAS by using a counterfeit fingerprint, and on more effective Liveness Detectors (LD), aimed to discern authentic (live) fingerprints from fake ones. As in many other computer vision tasks, deep Convolutional Neural Networks (CNN) demonstrated to be very effective also for fingerprint liveness detection. However, we showed that it is possible to adapt adversarial perturbation approaches to mislead CNN-based LD. In this paper, we want to make a step further toward the design of a black-box attack by investigating whether it is possible to transfer a perturbation across different CNN liveness detectors in the case of a target LD very different from the one used to compute the perturbations. To this aim, we designed an attack scenario where a shadow LD (i.e. an adaptation of the substitute technique for the liveness detection application) is used to generate an adversarial fingerprint in a white-box setting before submitting it to the real target LD, invoked in a total back-box manner. Finally, we analysed the impact that such attack has on the authentication system, also analysing if and to what extent the scanner and the spoofing material combinations affect the success of the attack
The Impact of Fair Value Measurements on Comprehensive Income Representation under the Application of IAS/IFRSs in Italy
No full textThe requirement of applying International Financial Reporting Standards (IAS/IFRSs) for all EU listed companies starting from 2005, is causing some adapting issues since the International Accounting System is quite different from that of Continental countries, in particular Italy.
The main difference is the much large reference to market valuation in IAS/IFRSs, above all referring to fair value criterion. Introduction of fair value will involve significant changes on financial representation in Italy.
Drawn on this accounting innovation, this paper is intended to investigate the relationship between fair value measurement and income numbers, according to the features of the Italian accounting system.
The first step is to pinpoint all the different measurement bases (replacement cost, net realizable value, value in use) adopted in IAS/IFRSs according to the dictates of each standard. Then, the focus will be on fair value accounting.
For this purpose it will be analyzed a recent project (Fair Value Measurements) undertaken by the IASB, to provide an organic guidance on defining and measuring fair value.
Furthermore, the impact of changes in fair value will be investigated since it could affect both income or equity, as arranged in each standard.
This difference of treatment could lead to representation distortion so IASB is working, jointly with FASB, to include the Comprehensive Income representation in the Financial Statements to enhance the efficacy of the information in assessing firm performance.
All this considerations will be reflected on the income statement, if a Comprehensive Income representation should be introduced, since it will move from the representation of the realised income towards the representation of different kinds of comprehensive income figures, depending on the related notion of capital maintenance. In fact, the bases adopted for income measurement will imply the size of capital to be maintained
Recent experimental applications of nanomaterials in cement mortars: metakaolin and waste glass
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To Reject or not to Reject...: That's the question. An answer in case of neural classifiers
No full textIEEE TRANSACTIONS ON SYSTEMS, MAN AND CYBERNETICS-PART
Comparing Generalization and Recognition Capability of Learning Vector Quantization and Multi-layer Perceptron Architectures
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Neural machine registration for motion correction in breast DCE-MRI
No full textCancer is one of the leading causes of death in the western world, with medical imaging playing a key role for early diagnosis. Focusing on breast cancer, one of the emerging imaging methodologies is Dynamic Contrast Enhanced-Magnetic Resonance Imaging (DCE-MRI). The flip side of using DCE-MRI is in its long acquisition times that often causes the patient to move. This results in motion artefacts, namely distortions in the acquired image that can affect DCE-MRI analysis. A possible solution consists in the use of Motion Correction Techniques (MCTs), i.e. procedures intended to re-align the post-contrast image to the corresponding pre-contrast (reference) one. This task is particularly critic in DCE-MRI, due to brightness variations introduced in post-contrast images by the contrast-agent flowing. To face this problem, in this work we introduce a new MCT for breast DCE-MRI leveraging Physiologically Based PharmacoKinetic (PBPK) modelling and Artificial Neural Networks (ANN) to determine the most suitable physiologically-compliant transformation. To this aim, we propose a Neural Registration Network relying on a very task-specific loss function explicitly designed to take into account the contrast agent flowing while enforcing a correct re-alignment. We compared the obtained results against some conventional motion correction techniques, evaluating the performance on a patient-by-patient basis. Results show that the proposed approach results to be the best performing even when compared against other techniques designed to take into account for brightness variations
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