dataesr/scientific-paragraphs-categorization huggingface dataset:
• Tackling the sensor interoperability problem by including the experiments of interdatabase and intra-database tests. The remainder of the paper is structured as follows. Section 2 describes related work on face presentation attack detection. Then, our approach is described in detail in Section 3. Section 4 summarizes the experimental results and provides a comparative analysis. The section also describes the databases that we used in our tests. Section 5 draws some conclusions and highlights some future directions. Abbreviations define the main acronyms used in this paper.
They started to run their tests on the Replay Attack Corpus and Moiré databases. Other face presentation attack detection solutions are based on textures on 3D models, such as those used in  [19]. Because the attacker in 3D models utilizes a mask to spoof the system, the introduction of wrinkles might be extremely helpful in detecting the attack. The presented work in  [19]  examines the viability of performing low-cost assaults on 2. 5D and 3D face recognition systems using self-manufactured three-dimensional (3D) printed models.
Deep Learning-Based Techniques Actually, deep learning is used in a variety of systems and applications for biometric authentication  [20] , where the deep network can be trained using a number of patterns. After learning all of the dataset's unique features, the network can be used to identify similar patterns. Deep learning approaches have mostly been used to learn face presentation attack detection features. Moreover, deep learning is efficient at classification (supervised learning) and clustering tasks (unsupervised learning). Thus, the system assigns class labels to the input instances in a classification task, but the instances in clustering approaches are clustered based on their similarity without the usage of class labels. To train models with significant discriminative abilities, Yang et al.
Jourabloo et al. [33]  proposed a new CNN architecture for face presentation attack detection, with appropriate constraints and supplementary supervisions, to discern between living and fake faces, as well as long-term motion. In order to detect presentation attacks effectively and efficiently, Kim et al. [34]  introduced the bipartite auxiliary supervision network (BASN), an architecture that learns to extract and aggregate auxiliary information. Huszár et al. [35]  proposed a deep learning (DL) approach to address the problem of presentation attack instruments occurring from video. The approach was tested in a new database made up of several videos of users juggling a football. Their algorithm is capable of running in parallel with the human activity recognition (HAR) in real-time. Roy et al.
Feature Learning Module Feature learning (FL) is a set of approaches in machine learning that allow a system to discover the representation needed for feature detection, prediction, or classification from a preprocessed dataset automatically. This enables a machine to learn the features and apply them to a specific task-like classification and prediction. Feature learning can be achieved in deep learning by either creating a complete CNN to train and test the collection of images or adapting a pre-trained CNN for classification or prediction for the new images-set. Transfer learning is the latter strategy used in the deep learning domain. Transfer learning is a machine learning technique in which a model created for one task is utilized as the basis for a model on a different task. Transfer learning (TL) is commonly used in deep learning (DL) applications to allow you to use a pre-trained network for solving new classification tasks.
To meet the new learning tasks, the learning parameters of the pre-trained network with randomly initialized weights must be fine-tuned. Transfer learning is typically considerably faster and easier to learn/train than building a network from the initial concept. Transfer learning is an optimization and a quick way that can save time or improve efficiency. In this section, a transfer learning technique is applied by fine-tuning a pretrained ResNet-50 model on ImageNet dataset using multiple spoofing datasets where the output of the last FC layer is changed to output two classes (real/fake). The network is called ResNet-50, due to the fact that it has 48 convolution layers, along with 1 MaxPool and 1 Average Pool layer, and it introduced the use of residual blocks.
Classification Module Data classification is a vital process for separating large datasets into classes for decision-making, pattern detection, and other purposes. For multi-class classification problems with mutually exclusive classes, a classification layer uses a fully connected layer to compute the cross-entropy loss. The features from ResNet-50 are passed via a FC layer made of 1024 neurons with a 40% dropout to prevent over-fitting in the classification module. Having followed that, the units were activated with a rectification mechanism called ReLU. MAX (X, 0) is the ReLu function, which sets all negative values in the matrix X to zero while keeping all other values constant. The reason for choosing ReLU is that deep network training with ReLU tended to converge considerably faster and more reliably than deep network training with sigmoid activation.
Database and Protocol In order to assess of the effectiveness of our proposed presentation attack detection technique, we performed a set of experiments on well known databases where the top three challenging databases were used: the CASIA-FASD (http://www. cbsr. ia. ac. cn/english/FaceAntiSpoofDatabases. asp (accessed on 4 July 2014)); face anti-spoofing database, Replay-Attack (https://www. idiap. ch/dataset/replayattack (accessed on 6 August 2014)) database, and MSU (https://drive. google. com/drive/folders/1nJCPdJ7R6 7xOiklF1omkfz4yHeJwhQsz (accessed on 8 May 2014)) mobile face spoofing databases. Those databases contain video recordings of real and fake attacks. A brief description of these databases is given as fellow: The CASIA-FASD database  [39]  is a dataset for face presentation attack detection.
This database contains 50 genuine subjects in total and the corresponding fake faces are captured with high quality from the original ones. Therefore each subject contains 12 videos (3 genuine and 9 fake) under three different resolutions and light conditions, namely low quality, normal quality, and high quality. Moreover, three fake face attacks are designed, which include warped photo attack, cut photo attack, and video attack. The overall database contains 600 video clips and the subjects are divided into subsets for performing training and tests in which 240 videos of 20 subjects are used for training and 360 videos of 30 subjects for testing. Test protocol is provided, which consists of 7 scenarios for a thorough evaluation from all possible aspects (see Figure  3 ). Among the popular databases designed for the presentation attack detection application, one can find the Replay-Attack database  [40].
This database consists of 1300 video of real-access and attack attempts to 50 subjects, (See Figure  4 ). However, these videos were taken using a built-in webcam on a MacBook laptop under two separate scenarios (controlled and adverse). In addition, two cameras were used to create the faked facial attack for each person in high-resolution images and videos: a Canon PowerShot SX150 IS and an iPhone 3GS camera. Moreover, fixed attacks and hand attacks are the two types of attacks. There are ten videos in each subset: four mobile attacks with a resolution of 480 × 320 pixels on an iPhone 3GS screen, then, by using a first generation iPad with a screen resolution of 1024 × 768 pixels, four high-resolution screen attacks were performed. On A4 paper, two hard-copy print attacks (printed on a Triumph-Adler DCC 2520 colour laser printer) occupied the whole available printing surface.
Low The pattern recognition and image processing (PRIP) group at Michigan State University developed a publicly available MSU-MFSD  [14]  database for face presentation attacks. The database contains 280 video clips of attempted photo and video attacks on 35 clients. It was created using a mobile phone to capture both genuine and presentation attack. This was accomplished using two types of cameras: (1) the built-in camera in the MacBook Air 13 inch (640 × 480) and (  2 ) the front-facing camera on the Google Nexus 5 Android phone (720 × 480). Each subject received two video recordings, the first of which was taken using a laptop camera and the second with an Android camera (See Figure  5 ). High-resolution video was recorded for each subject utilizing two devices to create the attacks: (1) Canon PowerShot 550D SLR camera, which captures 18.
0 megapixel photos and 1080p highdefinition video clips; (2) iPhone 5S back-facing camera, which captures 1080p video clips. There are three types of presentation attack instruments, the first one (1) high-resolution replay video, the first type of presentation attack instrument is a high-resolution replay video attack using an iPad Air screen, with a resolution of 2048 × 1536, the second is a mobile phone replay video attack using an iPhone 5S screen, with a resolution of 1136 × 640, and the third is a printed photo attack using an A3 paper with a fully-occupied printed photo of the client's biometry, with a paper size of: 11 × 17 (279 mm× 432 mm), printed with an HP LaserJet CP6015xh printer at a resolution of 1200 × 600 dpi. Finally, to assess performance, the 35 subjects in the MSU-MFSD database were divided into two subsets: 15 for training and 20 for testing.
Evaluation Metrics The comparative results for cross-scenario testing are expressed in terms of the HTER, which is the mean of the false acceptance rate (FAR) and false rejection rate (FRR). On the development set, we first computed the EER and the corresponding threshold, and then used the threshold to determine the HTER on the testing set. Additionally, the receiver operating characteristic (ROC) is reported to assess the method's performance. We used HTER for the IDIAP Replay-Attack dataset and EER for the CASIA-FASD and MSU-MFSD datasets for intra-scenario testing. We employed area under the curve (AUC) as a performance metric for type-scenario testing. For a deeper idea about the computational cost of the proposed work, we calculated the overall cost using two different execution environments, CPU and GPU; the obtained results are shown in Table  1.
Performance Comparison on Intra-Database We computed the EERs for the seven scenarios, including different quality and media, to meet the official CASIA-FAS test protocol. Low, normal, and high-quality image sequences were provided as quality descriptors, and the used media for presentation attack were warped images, chopped photos, and videos played on an iPad. The last scenario was the overall test, which looked at how image quality and spoofing media affected system performance. In this part, we computed two tests, the first of which was performed per-frame and the second of which was performed per-video. The first test used BS-CNN to determine whether an image was real or fake, while the second test used MV to determine whether a video was real or not (See Table  2 ). In addition, we discovered that the proposed method (BS-CNN+MV) improved the performance.
Moreover, we found that combining MV with BS-CNN yields the best results for picture quality (low, normal, and high), as well as with spoof media (warped photo, cut photo, and video attacks) (see Figure  6 ). This can be explained by the fact that MV improves decision-making performance. Table  2  shows that our proposed technique outperforms the CASIA baseline in all scenarios when compared to the database created by CASIA  [39]. The suggested method is compared to state-of-the-art methods in Table  3  using data from the Replay-Attack database, despite the fact that our EER and HTER are similar to the previous approaches. The following are the types of attacks on replay databases that we computed the performance of using our method: depending on the method used to hold the attack replay device (paper, mobile phone, or tablet), the three attack subsets (print, mobile, and high-def) were recorded in two different modes: (i) fixed-support and (ii) hand-based (See Table  4 ).
We also put our method to the test using the MSU-MFSD database. (See Table  5 ). It should be noted that no articles have been published that detail the results of various sorts of attacks on this database. We can see that our results are better than the state-of-the-art, with our BS-CNN+MV providing the best results. The final comparison results on the intra-database are shown in (Tables 2, 3 and 5), which indicates that our proposed method achieves much lower errors on all three datasets than other state-of-the-art methods. Meanwhile, from Figure  7  it can be observed that BS-CNN+MV performs better than BS-CNN, which further verifies the effectiveness of the proposed background subtraction-based convolution neural network.
Inter-Dataset Cross-Type Testing In this part of the experiments, CASIA-FASD, Replay-Attack, and MSU-MFSD were used to perform the intra-dataset cross-type testing between replay and print attacks. As shown in Table  6 , our proposed method outperforms the state-of-the-art methods in terms of overall performance (99. 99% AUC), indicating that learned features extended well to unknown attacks. As a result, it appears that our method can learn intrinsic material patterns from a wide range of materials and so generalizes well to previously unexplored types of material.
Inter-Dataset Cross-Dataset Testing This experiment includes six cross-dataset testing protocols. The first is that we perform CASIA-FASD training and testing on Replay-Attack, which is known as protocol CR; the second is that we perform CASIA-FASD training and testing on MSU-MFSD, which is known as protocol CM; and the third is that we exchange the training and testing datasets that we have in the first, which is known as protocol REPLAY CASIA (RC). The rest of the protocols are identical, with the exception that we utilize the data to train one time and test the next; the protocols are protocol RM, protocol MSU CASIA (MC), and protocol MSU REPLAY (MR), as may be seen in Table  7. Regarding protocol CR, our suggested BS-CNN+MV has 17. 62% HTER, exceeding the previous state-of-the-art by a convincing margin of 2%.
Conclusions and Future Directions Fake face detection is a problem that was addressed in this work. We analyzed seven scenarios from the MSU-MFSD, the REPLAY-ATTACK, and the CASIA-FASD databases. In fact, texture and motion-based characteristics were used by the majority of authors in the field of face presentation attack detection. However, BS and a CNN with a majority vote seems to determine well if a person is using a fake face. In our paper, we evaluated our approach under different protocols. Firstly, we used all three types of databases to evaluate if they produced satisfactory results when compared to the current state-of-the-art. The proposed technique was then put to the test using cross-type testing to ensure that it could handle all attributes and attacks across the three databases.
In the final test, we used cross-dataset testing to compare each train of any data with the test, to other data, in order to improve the validity of our approach. The obtained results have shown that our proposed methods outperform the current state-of-the-art. We should point out that the BS-CNN+MV investigation is still in its early stages, as evidenced by the limitations listed below; (1) the limitation of the state-of-the-art face presentation attack detection methods is that they require a perfect result in real-time. Thus, we envision trying to tackle and investigate this problem by using cross-database performance, database limitations, and usability; (2) the problem in FAS with 3D masks or makeup cannot be detected with our proposal. So we need to handle it in future work by using background subtraction with 3D CNN models; (3) applicability to various vision tasks with domain generalization.
We discuss the detections using meteorological fields to assess the presence of stratospheric waveguides favoring propagation. We corroborate the detections by considering other datasets, such as satellite observations, a surface lightning detection network, and products mapping the simulated intensity of the swell. High-and low-frequency detections are evidenced for three medicanes at distances ranging between 250 and 1100 km from the station. Several cases of non-detection are also discussed. While deep convective systems, and mostly lightning within them, seem to be the main source of detections above 1 Hz, hotspots of swell (microbarom) related to the medicanes are evidenced between 0. 1 and 0. 5 Hz. In the latter case, simulations of microbarom detections are consistent with the observations.
To our knowledge, no investigation has been conducted to identify signatures of medicanes in infrasound recordings so far, in the microbarom frequency range and above. The present study brings the first evidence of infrasound detections related to the formation and displacement of mesocyclones across the Mediterranean Sea. In Section 2, we describe the different cases of medicanes investigated along with the infrasound data and the other datasets used for our investigation on the origin of the infrasound detections. Section 3 documents the recorded infrasound signatures that can be related to the medicane events. A discussion explaining the detection or lack of detection and identifying the sources responsible for the detections is undertaken in Section 4. Section 5 summarizes the main findings and suggests future directions for improving our understanding of infrasound emission from Mediterranean cyclones.
The criterion for detection (consistency) is set to 0. 1 s and only time-frequency pixels verifying this condition and belonging to a family of pixels are considered. Families are groups of pixels sharing common wavefront parameters, namely backazimuth, trace velocity (horizontal projection of the wavefront celerity), frequency, and time. This allows one to easily filter out artifacts and outliers  [22]. In Section 2, we describe the different cases of medicanes investigated along with the infrasound data and the other datasets used for our investigation on the origin of the infrasound detections. Section 3 documents the recorded infrasound signatures that can be related to the medicane events. A discussion explaining the detection or lack of detection and identifying the sources responsible for the detections is undertaken in Section 4.
Meteorological Data ECMWF (European Centre for Medium-Range Weather Forecasts) reanalysis products ERA5  [54]  provide wind and temperature fields that are used to investigate infrasound guiding in the middle atmosphere. Fields are extracted from the Meteorological Archival and Retrieval System (MARS) of the ECMWF through the Copernicus Climate Change Service (C3S) above the Mediterranean region. The ERA5 fields are provided on a regular 0. 25 • by 0. 25 • horizontal grid and available at an hourly output from the surface of 0. 01 hPa (~80 km altitude). These fields come from ECMWF's Integrated Forecasting System (IFS) model simulations performed at a horizontal resolution of 30 km. The 2-m wind and gusts were also extracted at the station in order to check the ability of ERA5 fields to account for the local wind noise conditions affecting the sensors despite it being adapted to regional-scale dynamics considerations.
The World Wide Lightning Location Network Very-low-frequency (VLF) electromagnetic waves are detected by the World Wide Lightning Location Network (WWLLN), which employs ~70 sensors that allow to locate lightning strokes with a 10 km location accuracy (e. g. ,  [60] ). This dataset has been used to study the evolution of lightning activity in tropical cyclones  [61] [62] [63]. The WWLLN detection efficiency ranges from 10% to 20% for the cloud-to-ground flashes but rises to between 50% and 80% for intense flashes (defined with peak current > 50 kA), varying significantly over land and ocean  [64] , while being higher over oceans by a factor of 2-3 and will largely miss intra-cloud lightning [65]. Farges et al. [42]  extensively used this dataset to explain infrasound detections of lightning from tropical storms in Western Africa.
Acoustic Source Model for Microbaroms: The ARROW Dataset A new source model for microbaroms developed by De Carlo et al. [66]  is used. In De Carlo et al. [67] , this model was successfully compared to a previous source model  [68] , for which theoretical limitations were highlighted, and to IMS observations. This acoustic source model was also successfully used in case studies  [69, 70]. This led to the creation of the AtmospheRic InfRasound by Ocean Waves (ARROW) dataset  [71] , which is hosted by IFREMER and builds on the wave interaction products for which a hindcast covering the 1993-2020 period exists  [72, 73]. In short, the ARROW product offers global mapping of the wave interaction product and the related acoustic source product (microbaroms source model) on a three-hourly time step, both for past periods as well as in near-real time.
Local wind noise is relatively larger at this period (Figure  3c ), challenging the PMCC detector as well. The most obvious feature when considering the three datasets together remains the variation of back-azimuths from 330° to 280° for PMCC detections, DC pixels, and lightning from the 11th of November onward, demonstrating the tracking of the medicane. The distance to the station of the detected lightning also consistently decreases through time (Figure  13b ) as the mesocyclone approaches the coast and IS48. On the 11th of November 2019, lightning was clearly detected during Medicane Trudy and the other aforementioned convective system (between 270° and 300°). The similar distance of the lightning to the station (500-700 km) in both systems backs up the idea that both areas may be equivalent sources of infrasound.
This can explain why no system takes over the other in the infrasound detections, leading to the noisy cluster of detections on that day (Figures  3a  and 13a ). PMCC, as a mono-source algorithm, cannot handle multi-source detections. The most obvious feature when considering the three datasets together remains the variation of back-azimuths from 330 • to 280 • for PMCC detections, DC pixels, and lightning from the 11th of November onward, demonstrating the tracking of the medicane. The distance to the station of the detected lightning also consistently decreases through time (Figure  13b ) as the mesocyclone approaches the coast and IS48. On the 11th of November 2019, lightning was clearly detected during Medicane Trudy and the other aforementioned convective system (between 270 • and 300 • ).
As with Trudy, the distance of lightning to the station decreases through time (Figure  15c ), which is consistent with the decrease in signal amplitude of the infrasound detections (Figure  4a ) related to the medicane moving away from the station (Figure  2 ), however, not weakening (Section 2. 6). The combination of the different datasets allows to clearly identify the Etna detections (Figure  14a ) in the direction of which (~66°) neither convective cells (Figure  14b ) nor The combination of the different datasets allows to clearly identify the Etna detections (Figure  14a ) in the direction of which (~66 • ) neither convective cells (Figure  14b ) nor lightning (Figure  14c ) are observed, and more generally, between 60 • and 80 • azimuths in the time period 15th of September 00:00 UTC-16th of September 12:00 UTC.