Faked Speech Detection with Zero Knowledge

Audio is one of the most used ways of human communication, but at the same time it can be easily misused to trick people. With the revolution of AI, the related technologies are now accessible to almost everyone thus making it simple for the criminals to commit crimes and forgeries. In this work, we introduce a neural network method to develop a classifier that will blindly classify an input audio as real or mimicked; the word 'blindly' refers to the ability to detect mimicked audio without references or real sources. The proposed model was trained on a set of important features extracted from a large dataset of audios to get a classifier that was tested on the same set of features from different audios. The data was extracted from two raw datasets, especially composed for this work; an all English dataset and a mixed dataset (Arabic plus English). These datasets have been made available, in raw form, through GitHub for the use of the research community at https://github.com/SaSs7/Dataset. For the purpose of comparison, the audios were also classified through human inspection with the subjects being the native speakers. The ensued results were interesting and exhibited formidable accuracy.Comment: 14 pages, 4 figures (6 if you count subfigures), 2 table

FedBiometric: Image Features Based Biometric Presentation Attack Detection Using Hybrid CNNs-SVM in Federated Learning

In the past few years, biometric identification systems have become popular for personal, national, and global security. In addition to other biometric modalities, facial and fingerprint recognition have gained popularity due to their uniqueness, stability, convenience, and cost-effectiveness compared to other biometric modalities. However, the evolution of fake biometrics, such as printed materials, 2D or 3D faces, makeup, and cosmetics, has brought new challenges. As a result of these modifications, several facial and fingerprint Presentation Attack Detection methods have been proposed to distinguish between live and spoof faces or fingerprints. Federated learning can play a significant role in this problem due to its distributed learning setting and privacy-preserving advantages. This work proposes a hybrid ResNet50-SVM based federated learning model for facial Presentation Attack Detection utilizing Local Binary Pattern (LBP), or Gabor filter-based extracted image features. For fingerprint Presentation Attack Detection (PAD), this work proposes a hybrid CNN-SVM based federated learning model utilizing Local Binary Pattern (LBP), or Histograms of Oriented Gradient (HOG)-based extracted image features

A survey on artificial intelligence-based acoustic source identification

The concept of Acoustic Source Identification (ASI), which refers to the process of identifying noise sources has attracted increasing attention in recent years. The ASI technology can be used for surveillance, monitoring, and maintenance applications in a wide range of sectors, such as defence, manufacturing, healthcare, and agriculture. Acoustic signature analysis and pattern recognition remain the core technologies for noise source identification. Manual identification of acoustic signatures, however, has become increasingly challenging as dataset sizes grow. As a result, the use of Artificial Intelligence (AI) techniques for identifying noise sources has become increasingly relevant and useful. In this paper, we provide a comprehensive review of AI-based acoustic source identification techniques. We analyze the strengths and weaknesses of AI-based ASI processes and associated methods proposed by researchers in the literature. Additionally, we did a detailed survey of ASI applications in machinery, underwater applications, environment/event source recognition, healthcare, and other fields. We also highlight relevant research directions

A Story of her own: The Absence of Romance in Zero Dark Thirty

Tomando como base la teoría de que La noche más oscura puede describirse como la auténtica película para chicas, el presente artículo explora los precedentes, efectos y potencial de una protagonista sin rastro de subtrama sentimental en una producción hollywoodense. Maya responde a la evolución de tres papeles legendarios en el cine contemporáneo: Ripley, Sarah Connor y Clarice Sterling. Obviamente comparada con Carrie Mathison de Homeland, la principal diferencia es que el guión de la película borra del mapa la sexualidad de Maya. Esto explica el tono de Juana de Arco en la construcción del personaje, cuya identidad se reinventa de modo incesante, lejos de las exigencias patriarcales. Maya no encaja en ninguna parte, disfrazando a menudo su apariencia física y transformando su lenguaje corporal. Bin Laden es su único interés en toda la película, un hombre cuyo cadáver nunca se llega a ver. En definitiva, La noche más oscura demuestra que un papel femenino puede ser lo suficientemente consistente y serio como para concentrar la atención del espectador sin recurrir a ningún cliché tal como la pareja masculina que la apoyeBased on the theory that Zero Dark Thirty can be described as the authentic chick flick, the present paper explores the precedents, effects and potential of a female protagonist with no trace of a sentimental subplot in a Hollywood production. Maya responds to the evolution of three legendary parts in contemporary cinema: Ripley, Sarah Connor and Clarice Sterling. Obviously compared to Carrie Mathison from Homeland, the main difference is that the screenplay of the film erases Maya’s sexuality. This explains the Joan of Arc tone in the construction of the character, whose identity is incessantly being reinvented, away from patriarchal demands. Maya does not fit in anywhere, often disguising her appearance and transforming her body language. Bin Laden is her only concern in the entire feature, a man whose corpse is never seen. In the end, Zero Dark Thirty proves that a female role can be consistent and serious enough to concentrate the spectator’s attention without recurring to any cliché like the supporting male partner

Automated Dynamic Analysis of Ransomware: Benefits, Limitations and use for Detection

Recent statistics show that in 2015 more than 140 millions new malware samples have been found. Among these, a large portion is due to ransomware, the class of malware whose specific goal is to render the victim's system unusable, in particular by encrypting important files, and then ask the user to pay a ransom to revert the damage. Several ransomware include sophisticated packing techniques, and are hence difficult to statically analyse. We present EldeRan, a machine learning approach for dynamically analysing and classifying ransomware. EldeRan monitors a set of actions performed by applications in their first phases of installation checking for characteristics signs of ransomware. Our tests over a dataset of 582 ransomware belonging to 11 families, and with 942 goodware applications, show that EldeRan achieves an area under the ROC curve of 0.995. Furthermore, EldeRan works without requiring that an entire ransomware family is available beforehand. These results suggest that dynamic analysis can support ransomware detection, since ransomware samples exhibit a set of characteristic features at run-time that are common across families, and that helps the early detection of new variants. We also outline some limitations of dynamic analysis for ransomware and propose possible solutions