This paper describes a new database for the assessment of automatic speaker verification (ASV) vulnerabilities to spoofing attacks. In contrast to other recent data collection efforts, the new database has been designed to support the development of replay spoofing countermeasures tailored towards the protection of text-dependent ASV systems from replay attacks in the face of variable recording and playback conditions. Derived from the re-recording of the original RedDots database, the effort is aligned with that in text-dependent ASV and thus well positioned for future assessments of replay spoofing countermeasures, not just in isolation, but in integration with ASV. The paper describes the database design and re-recording, a protocol and some early spoofing detection results. The new "RedDots Replayed" database is publicly available through a creative commons license.

Action recognition in videos is a relevant and challenging task of automatic semantic video analysis. Most successful approaches exploit local space-time descriptors. These descriptors are usually carefully engineered in order to obtain feature invariance to photometric and geometric variations. The main drawback of space-time descriptors is high dimensionality and efficiency. In this paper we propose a novel descriptor based on 3D Zernike moments computed for space-time patches. Moments are by construction not redundant and therefore optimal for compactness. Given the hierarchical structure of our descriptor we propose a novel similarity procedure that exploits this structure comparing features as pyramids. The approach is tested on a public dataset and compared with state-of-the art descriptors.

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A novel system of image retrieval, based on Hadoop and Spark, is presented. Managing and extracting information from Big Data is a challenging and fundamental task. For these reasons, the system is scalable and it is designed to be able to manage small collections of images as well as huge collections of images. Hadoop and Spark are based on the MapReduce framework, but they have different characteristics. The proposed system is designed to take advantage of these two technologies. The performances of the proposed system are evaluated and analysed in terms of computational cost in order to understand in which context it could be successfully used. The experimental results show that the proposed system is efficient for both small and huge collections.