An evaluation of entropy measures for microphone identification

Research findings have shown that microphones can be uniquely identified by audio recordings since physical features of the microphone components leave repeatable and distinguishable traces on the audio stream. This property can be exploited in security applications to perform the identification of a mobile phone through the built-in microphone. The problem is to determine an accurate but also efficient representation of the physical characteristics, which is not known a priori. Usually there is a trade-off between the identification accuracy and the time requested to perform the classification. Various approaches have been used in literature to deal with it, ranging from the application of handcrafted statistical features to the recent application of deep learning techniques. This paper evaluates the application of different entropy measures (Shannon Entropy, Permutation Entropy, Dispersion Entropy, Approximate Entropy, Sample Entropy, and Fuzzy Entropy) and their suitability for microphone classification. The analysis is validated against an experimental dataset of built-in microphones of 34 mobile phones, stimulated by three different audio signals. The findings show that selected entropy measures can provide a very high identification accuracy in comparison to other statistical features and that they can be robust against the presence of noise. This paper performs an extensive analysis based on filter features selection methods to identify the most discriminating entropy measures and the related hyper-parameters (e.g., embedding dimension). Results on the trade-off between accuracy and classification time are also presented

Red blood cell alloimmunisation in transfusion-dependent thalassaemia: a systematic review.

BACKGROUND: Chronic red blood cell transfusion is the first-line treatment for severe forms of thalassaemia. This therapy is, however, hampered by a number of adverse effects, including red blood cell alloimmunisation. The aim of this systematic review was to collect the current literature data on erythrocyte alloimmunisation. MATERIALS AND METHODS: We performed a systematic search of the literature which identified 41 cohort studies involving 9,256 patients. RESULTS: The prevalence of erythrocyte alloimmunisation was 11.4% (95% CI: 9.3-13.9%) with a higher rate of alloimmunisation against antigens of the Rh (52.4%) and Kell (25.6%) systems. Overall, alloantibodies against antigens belonging to the Rh and Kell systems accounted for 78% of the cases. A higher prevalence of red blood cell alloimmunisation was found in patients with thalassaemia intermedia compared to that among patients with thalassaemia major (15.5 vs 12.8%). DISCUSSION: Matching transfusion-dependent thalassaemia patients and red blood cell units for Rh and Kell antigens should be able to reduce the risk of red blood cell alloimmunisation by about 80%

Online Distributed Denial of Service (DDoS) intrusion detection based on adaptive sliding window and morphological fractal dimension

Distributed Denial of Service (DDOS) attacks are important threats to network services and applications. Studies in literature have proposed various approaches including Intrusion Detection Systems (IDS) based on the application of machine learning and deep learning, but their computational cost can be significant. For this reason, other studies have proposed efficient IDS algorithms based on the online real-time analysis of the network traffic with a sliding window and entropy or other statistical measures. This paper proposes an online algorithm based on a sliding window with the novel application of the Morphological Fractal Dimension (MFD) to this problem. The results presented in this study show that the application of MFD to the recent CICIDS2017 public data set can obtain a significant improvement in the detection of the DDoS attack in comparison to entropy based approaches. In addition, this paper proposes a novel algorithm for the automatic definition of the sliding window size. This paper reports the impact of the different hyper-parameters, including the parameters present in the definition of MFD and the evaluation of the distance measures, where the Chebyschev distance provides the optimal detection accuracy. The results show a detection accuracy of 99.30%, which performs better than similar approaches on the same data set

SIFT match removal and keypoint preservation through dominant orientation shift

In Image Forensics, very often, copy-move attack is countered by resorting at instruments based on matching local features descriptors, usually SIFT. On the other side, to overcome such techniques, smart hackers can try firstly to remove keypoints before performing image patch cloning in order to inhibit the successive matching operation. However, keypoint removal determines per se some suspicious empty areas that could indicate that a manipulation has occurred. In this paper, the goal to nullify SIFT matches while preserving keypoints is pursued. The basic idea is to succeed in altering the features descriptor by means of shifting the dominant orientation associated to a specific keypoint. In fact, to provide rotation invariance, all the values of the descriptor are computed according to such orientation. So doing, it should impair the whole matching phase. © 2015 EURASIP

Distinguishing between camera and scanned images by means of frequency analysis

Distinguishing the kind of sensor which has acquired a digital image could be crucial in many scenarios where digital forensic techniques are called to give answers. In this paper a new methodology which permits to determine if a digital photo has been taken by a camera or has been scanned by a scanner is proposed. Such a technique exploits the specific geometrical features of the sensor pattern noise introduced by the sensor in both cases and by resorting to a frequency analysis can infer if a periodicity is present and consequently which is the origin of the digital content. Experimental results are presented to support the theoretical framework

Media forensics on social media platforms: a survey

The dependability of visual information on the web and the authenticity of digital media appearing virally in social media platforms has been raising unprecedented concerns. As a result, in the last years the multimedia forensics research community pursued the ambition to scale the forensic analysis to real-world web-based open systems. This survey aims at describing the work done so far on the analysis of shared data, covering three main aspects: forensics techniques performing source identification and integrity verification on media uploaded on social networks, platform provenance analysis allowing to identify sharing platforms, and multimedia verification algorithms assessing the credibility of media objects in relation to its associated textual information. The achieved results are highlighted together with current open issues and research challenges to be addressed in order to advance the field in the next future