dataesr/scientific-paragraphs-categorization huggingface dataset:
A deep learning method to detect deepfakes based on the artifacts observed during the face warping step of the deepfake generation algorithms was proposed in  [103]. The proposed method is evaluated on two deepfake datasets, namely the UADFV and DeepfakeTIMIT. The UADFV dataset  [104]  contains 49 real videos and 49 fake videos with 32,752 frames in total. The DeepfakeTIMIT dataset  [64]  includes a set of low quality videos of 64 x 64 size and another set of high quality videos of 128 x 128 with totally 10,537 pristine images and 34,023 fabricated images extracted from 320 videos for each quality set. Performance of the proposed method is compared with other prevalent methods such as two deepfake detection MesoNet methods, i. e. Meso-4 and MesoInception-4  [94] , HeadPose  [104] , and the face tampering detection method two-stream NN  [105].
The recent development of capsule network based on dynamic routing algorithm  [108]  demonstrates its ability to describe the hierarchical pose relationships between object parts. This development is employed as a component in a pipeline for detecting fabricated images and videos as demonstrated in Fig. 6. A dynamic routing algorithm is deployed to route the outputs of the three capsules to the output capsules through a number of iterations to separate between fake and real images. The method is evaluated through four datasets covering a wide range of forged image and video attacks. They include the well-known Idiap Research Institute replay-attack dataset  [109] , the deepfake face swapping dataset created by Afchar et al. [94] , the facial reenactment FaceForensics dataset  [110] , produced by the Face2Face method  [111] , and the fully computer-generated image dataset generated by Rahmouni et al.
[112]. The proposed method yields the best performance compared to its competing methods in all of these datasets. This shows the potential of the capsule network in building a general detection system that can work effectively for various forged image and video attacks. Fig. 6. Capsule network takes features obtained from the VGG-19 network  [101]  to distinguish fake images or videos from the real ones (top). The pre-processing step detects face region and scales it to the size of 128x128 before VGG-19 is used to extract latent features for the capsule network, which comprises three primary capsules and two output capsules, one for real and one for fake images (bottom). The statistical pooling constitutes an important part of capsule network that deals with forgery detection  [106]. b) Shallow classifiers: Deepfake detection methods mostly rely on the artifacts or inconsistency of intrinsic features between fake and real images or videos.
Yang et al. [104]  proposed a detection method by observing the differences between 3D head poses comprising head orientation and position, which are estimated based on 68 facial landmarks of the central face region. The 3D head poses are examined because there is a shortcoming in the deepfake face generation pipeline. The extracted features are fed into an SVM classifier to obtain the detection results. Experiments on two datasets show the great performance of the proposed approach against its competing methods. The first dataset, namely UADFV, consists of 49 deep fake videos and their respective real videos  [104]. The second dataset comprises 241 real images and 252 deep fake images, which is a subset of data used in the DARPA MediFor GAN Image/Video Challenge  [113]. Likewise, a method to exploit artifacts of deepfakes and face manipulations based on visual features of eyes, teeth and facial contours was studied in  [114].
The visual artifacts arise from lacking global consistency, wrong or imprecise estimation of the incident illumination, or imprecise estimation of the underlying geometry. For deepfakes detection, missing reflections and missing details in the eye and teeth areas are exploited as well as texture features extracted from the facial region based on facial landmarks. Accordingly, the eye feature vector, teeth feature vector and features extracted from the full-face crop are used. After extracting the features, two classifiers including logistic regression and small neural network are employed to classify the deepfakes from real videos. Experiments carried out on a video dataset downloaded from YouTube show the best result of 0. 851 in terms of the area under the receiver operating characteristics curve.
The analysis is widely used in image forensics  [117] -  [120]  and advocated to use in  [115]  because the swapped face is supposed to alter the local PRNU pattern in the facial area of video frames. The videos are converted into frames, which are cropped to the questioned facial region. The cropped frames are then separated sequentially into eight groups where an average PRNU pattern is computed for each group. Normalised cross correlation scores are calculated for comparisons of PRNU patterns among these groups. The authors in  [115]  created a test dataset consisting of 10 authentic videos and 16 manipulated videos, where the fake videos were produced from the genuine ones by the DeepFaceLab tool  [34]. The analysis shows a significant statistical difference in terms of mean normalised cross correlation scores between deepfakes and the genuine.
This analysis therefore suggests that PRNU has a potential in deepfake detection although a larger dataset would need to be tested. When seeing a video or image with suspicion, users normally want to search for its origin. However, there is currently no feasibility for such a tool. Hasan and Salah  [121]  proposed the use of blockchain and smart contracts to help users detect deepfake videos based on the assumption that videos are only real when their sources are traceable. Each video is associated with a smart contract that links to its parent video and each parent video has a link to its child in a hierarchical structure. Through this chain, users can credibly trace back to the original smart contract associated with pristine video even if the video has been copied multiple times. An important attribute of the smart contract is the unique hashes of the interplanetary file system, which is used to store video and its metadata in a decentralized and content-addressable manner  [122].
Detection methods are still in their early stage and various methods have been proposed and evaluated but using fragmented datasets. An approach to improve performance of detection methods is to create a growing updated benchmark dataset of deepfakes to validate the ongoing development of detection methods. This will facilitate the training process of detection models, especially those based on deep learning, which requires a large training set  [126]. On the other hand, current detection methods mostly focus on drawbacks of the deepfake generation pipelines, i. e. finding weakness of the competitors to attack them. This kind of information and knowledge is not always available in adversarial environments where attackers commonly attempt not to reveal such deepfake creation technologies. Recent works on adversarial perturbation attacks to fool DNN-based detectors make the deepfake detection task more difficult  [127] -  [131].
Videos -UADFV  [104] , containing 49 real videos and 49 fake videos with 32752 frames in total. -DeepfakeTIMIT  [64]  MesoNet  [94]  CNN -Two deep networks, i. e. Meso-4 and MesoInception-4 are introduced to examine deepfake videos at the mesoscopic analysis level. -Accuracy obtained on deepfake and FaceForensics datasets are 98% and 95% respectively. Videos Two datasets: deepfake one constituted from online videos and the FaceForensics one created by the Face2Face approach  [111]. Capsuleforensics  [106]  Capsule networks -Latent features extracted by VGG-19 network  [101]  are fed into the capsule network for classification. -A dynamic routing algorithm  [108]  is used to route the outputs of three convolutional capsules to two output capsules, one for fake and another for real images, through a number of iterations.
Videos/ Images -UADFV consists of 49 deep fake videos and their respective real videos. -241 real images and 252 deep fake images from DARPA MediFor GAN Image/Video Challenge. Eye, teach and facial texture  [114]  Logistic regression and neural network -Exploit facial texture differences, and missing reflections and details in eye and teeth areas of deepfakes. -Logistic regression and neural network are used for classifying. Videos A video dataset downloaded from YouTube.
RCN Temporal discrepancies across frames are explored using RCN that integrates convolutional network DenseNet  [79]  and the gated recurrent unit cells  [96]  Videos FaceForensics++ dataset, including 1,000 videos  [97]. Spatio-temporal features with LSTM  [140]  Convolutional bidirectional recurrent LSTM network -An XceptionNet CNN is used for facial feature extraction while audio embeddings are obtained by stacking multiple convolution modules. -Two loss functions, i. e. cross-entropy and Kullback-Leibler divergence, are used. Videos FaceForensics++  [97]  and Celeb-DF (5,639 deepfake videos)  [141]  datasets and the ASVSpoof 2019 Logical Access audio dataset  [142]. Analysis of PRNU  [115]  PRNU -Analysis of noise patterns of light sensitive sensors of digital cameras due to their factory defects.
-Explore the differences of PRNU patterns between the authentic and deepfake videos because face swapping is believed to alter the local PRNU patterns. Videos Created by the authors, including 10 authentic and 16 deepfake videos using DeepFaceLab  [34]. Phoneme-viseme mismatches  [133]  CNN -Exploit the mismatches between the dynamics of the mouth shape, i. e. visemes, with a spoken phoneme. -Focus on sounds associated with the M, B and P phonemes as they require complete mouth closure while deepfakes often incorrectly synthesize it. Videos Four in-the-wild lip-sync deepfakes from Instagram and YouTube (www. instagram. com/bill posters uk and youtu. be/VWMEDacz3L4) and others are created using synthesis techniques, i. e. Audio-to-Video (A2V)  [63]  and Text-to-Video (T2V)  [134]. [138]  and YouTube are used to generate two deepfake datasets by commercial website https://deepfakesweb.
Classifiers/ Techniques Key Features Dealing with Datasets Used Using Videos DeepfakeTIMIT  [139]  and DFDC  [144]. Using appearance and behaviour  [145]  Rules defined based on facial and behavioural features. Temporal, behavioral biometric based on facial expressions and head movements are learned using ResNet-101  [102]  while static facial biometric is obtained using VGG  [60]. Videos The world leaders dataset  [1] , FaceForensics++  [97] , Google/Jigsaw deepfake detection dataset  [146] , DFDC  [144]  and Celeb-DF  [141]. FakeCatcher  [147]  CNN Extract biological signals in portrait videos and use them as an implicit descriptor of authenticity because they are not spatially and temporally well-preserved in deepfakes. Videos UADFV  [104] , FaceForensics  [110] , FaceForen-sics++  [97] , Celeb-DF  [141] , and a new dataset of 142 videos, independent of the generative model, resolution, compression, content, and context.
Preprocessing combined with deep network  [71]  DCGAN, WGAN-GP and PGGAN. -Enhance generalization ability of deep learning models to detect GAN generated images. -Remove low level features of fake images. -Force deep networks to focus more on pixel level similarity between fake and real images to improve generalization ability. Images -Real dataset: CelebA-HQ  [85] , including high quality face images of 1024x1024 resolution. -Fake datasets: generated by DCGAN  [81] , WGAN-GP  [83]  and PGGAN  [85]. Bag of words and shallow classifiers  [67]  SVM, RF, MLP Extract discriminant features using bag of words method and feed these features into SVM, RF and MLP for binary classification: innocent vs fabricated. Images The well-known LFW face database  [148] , containing 13,223 images with resolution of 250x250.
e. , ProGAN  [85]  and evaluate how well the classifier generalizes to other CNNsynthesized images. Images A new dataset of CNN-generated images, namely ForenSynths, consisting of synthesized images from 11 models such as StyleGAN  [154] , superresolution methods  [158]  and FaceForensics++  [97]. of deepfakes to assist users in such judgement is thus worth performing. Videos and photographics have been widely used as evidences in police investigation and justice cases. They may be introduced as evidences in a court of law by digital media forensics experts who have background in computer or law enforcement and experience in collecting, examining and analysing digital information. The development of machine learning and AI technologies might have been used to modify these digital contents and thus the experts' opinions may not be enough to authenticate these evidences because even experts are unable to discern manipulated contents.
CURIOCITY ontology has a three-level architecture (Upper, Middle, and Lower ontologies) in accordance with a purpose of modularity and levels of specificity. In this paper, we describe in detail all modules of CURIOCITY ontology and perform a comparative evaluation with state-of-the-art ontologies. Additionally, to demonstrate the suitability of CURIOCITY ontology, we show several touristic services offered through a framework supported in the ontology. The framework includes an automatic population process, that allows transforming a museum data repository (in CSV format) into RDF triples of CURIOCITY ontology to automatically populate the CURIOCITY repository, and facilities to develop a set of tourism applications and services, following the UNESCO's definitions.
There exist hundreds of studies in this regard, as recent reviews highlight  [18] ,  [41] -  [45]. Some of the efforts in e-tourism start to turn the interest on using semantic web tools, since available data, content, and services are becoming semantically annotated, which allow software components to search through the web and understand its content. Experiences such the ones described in  [13] ,  [46] ,  [47]  reveal the benefits and advantages on using linked open data in e-tourism. The idea is to build cultural heritage knowledge from collaboration between open data published by several institutions (e. g. , governments, people interested, tourists), enriched with data from other sources like DBPedia and social media. The study presented in  [48] , surveys the most popular methods and tools used by touristic providers of information, products, and services to develop and apply machine-processable (semantic) annotations of service, data, and content, and their aggregation in large knowledge graphs (e.
Based on these concepts, Mondeca Tourism Ontology is proposed. Tourism object profiling, tourism and cultural objects, tourism packages, and tourism multimedia content are described  [55] ,  [56]. HiTouch Ontology  [55] , created under the IST/CRAFT European program, and OnTour Ontology  [31] , developed by e-Tourism Working Group at Digital Enterprise Research Institute, both also use the concepts of UNWTO. HiTouch represents additionally tourism products and customers' tourism expectations, while OnTour adds descriptions of leisure activities and geographic data. DataTourisme  5  ontology was created in 2017 by the company PERFECT MEMORY in a french project. The aim of this ontology is to centralize and publish as Linked Open Data (LOD) travel information produced by different tourist information systems in France.
Additionally, this ontology is connected to different existing ontologies as FOAF  6  , Schema 7  , GoodRelations 8  , Dublin core  9  to do not duplicate areas that are described already and in this way, to facilitate links with these open databases. Local tourism ontologies for Australia  [57] , Thailand  [58] ,  [59] , Iran  [60] , and others, have also been proposed to mainly develop applications such as recommendation systems  [56] ,  [60]  and tourism planning  [61].
RDA  [64]  is a set of elements, guidelines, and instructions for creation of metadata about library resources and cultural heritage, according to international models focused on linked data applications. RDA was created as a replacement for the Anglo American Cataloging Rules, and has a most widespread application in the library domain. RDA has a subscription cost. The Committee for Documentation of the International Council of Museums (CIDOC) proposes the Conceptual Reference Model (CIDOC CRM)  [65] , which since December 2006, is recognized as an official ISO 21127: 2014 standard. CIDOC CRM provides definitions, structures, basic classes, and relationships for describing cultural heritage documentation for the querying and exploration of such data. It has extensions that allow adapting it to particular uses, e.
Europeana Data Model (EDM)  [70] , has the aim to standardize the representation of cultural heritage objects from different domains such as libraries, museums, and audiovisual archives. It is not built on a particular standard, but adopts a wide range of these, such as CIDOC LIDO  18  for mu-seums, EAD  19  for archives, and METS  20  for digital libraries; with the intention of being a Semantic Web framework between different domains. ArCo (Arquitecture of Knowledge)  [71]  is an Italian project with the purpose of building a network of aligned ontologies to represent cultural heritage data and publish the General Catalogue proposed by the Italian Ministry of Culture. ArCo ontology version 0. 5 consists of seven modules: (i) arco, is the root of the network, it imports the other six modules and models top level cultural heritage concepts; (ii) core, represents orthogonal concepts imported by the other modules; (iii) catalogue, models catalogue records; (iv) location, represents spatial and geometry information; (v) denotative description, covers measurable characteristics and properties; (vi) context description, models the context covering information related to agents, activities, or situations; and (vii) cultural events, represents cultural events and exhibitions.
The problem approached by authors of CURATE is that narrative meaning cannot be expressed or derived solely from the metadata of cultural heritage objects. Authors of CURATE base their research on the hypothesis that curatorial narrative has generic characteristics and properties that can be found in other narratives, such as novels or films, hence, concepts like Story, Plot, and Narrative can be adopted. CURATE is based on CIDOC CRM and DOLCE + DnS Ultralite (DUL)  [72]  ontologies. MOM  [22] , is a top level ontology that also deals with the heterogeneous nature of cultural heritage and is based mainly on CIDOC CRM and EDM, in addition to ORE  21  , FOAF  22  , DC  23  , and SKOS  24. MOM ontology takes from EDM, classes like Non-Information Resource, which includes Event, Time Span, Place; Information Resource, which includes Web Resource and Provided CHO (Cultural Heritage Object).
); relationships (Receives, Visits, Performs, etc. ). Some concepts are directly referred to CIDOC CRM, however some properties related to the person cannot be described naturally, thus FOAF concepts such as Gender, Person Names, and Person-images relations are included. From DBPedia, properties such as Religion, Profession, Education, Party, and Spouse are included. Marchenkov et al. [23]  propose an ontology aimed at developing a digital environment oriented to visitors and museum service staff. This environment offers personal recommendations based on user context and exhibition characteristics; in addition to the collaborative management of information contained in different museums. Authors propose a layer-based model in which the semantic layer is responsible for providing mainly three advanced services: (i) Visiting Service, consists of creating personalized exhibition of a set of museum objects, based on the available knowledge of the visitor; this service adapts itself dynamically during the museum tour; (ii) Exhibition Service to display descriptions and visual information on visitors' personal screens and devices; thus, physical exhibition is enlarged by using digital media; and (iii) Enrichment Service to support the evolution of a semantic network, allowing to receive notes from visitors and staff in order to improve the database information.
, should be also represented. Concepts related to handicrafts are also urban tourism expressions. However, they present similar characteristics to cultural heritage contained in museums, thus, it is not required to model additional elements to represent craftsmanship. On the other hand, it is necessary concepts related to urban collections and exhibitions, such as ranking and curatorial activity. To cover these requirements, we evaluated popular ontologies in this domain. We considered CIDOC CRM, FINTO, and ArCo as the closest to our objectives, because their degrees of knowledge coverage. Although we knew the com- 25 https://github. com/JADA1979 under construction plex nature of CIDOC CRM, as noted in  [74] , we decided to adopt it as the base ontology. It was not an easy decision. Even though FINTO and ArCo have a greater coverage of concepts, the adoption of CIDOC CRM was due to its status as a standard.
1) Site Middle Ontology Module. Having identified place-related concepts, it is necessary to extend the knowledge, in such a way a site can also be a cultural heritage in its own right. This idea is reinforced by the special status UNESCO grants to certain cities to promote its conservation and protection. Cities, in turn, are home to places and points with cultural and touristic interest, therefore cit:Site CH (Site as Cultural Heritage) is a concept proposed in CURIOCITY ontology. Other placerelated concepts have been identified as cit:Site CH subclasses, such as cit:Park, cit:Protected Area, and cit:Natural Landscape, which are adapted from DBPedia, in addition, the corresponding class description (rdfs:comment) includes comments to distinguish them as classes to represent items with cultural interest.
For instance, the relation intervalOverlaps defined by: P roperInterval(T 1) ∧ P roperInterval(T 2) ∧. Code 6: SWRL inference rule example time: ProperInterval (?T1) ^time: ProperInterval (?T2) time:hasBeginning(?T1, ?T1begin) ^time:in_XSD_g-Year(? T1begin, ?T1beginYear) time:hasEnd(?T1, ?T1end) ^time:in_XSD_g-Year(?T1end, ? T1endYear) time:hasBeginning(?T2, ?T2begin) ^time:in_XSD_g-Year(? T2begin, ?T2beginYear) time:hasEnd(?T2, ?T2end) ^time:in_XSD_g-Year(?T2end, ? T2endYear) ŝwrlb : lessThan(?T1beginYear, ?T2beginYear) ŝwrlb : lessThan(?T2beginYear, ?T1endYear) ŝwrlb : lessThan(?T1endYear, ?T2endYear) -> time: intervalOverlaps (?T1, ?T2) Each of the components of CURIOCITY ontology has been developed taking into account the knowledge representation objectives of the RUTAS project and UNESCO's categorization of cultural heritage.
In our case, the golden standard is defined by the knowledge categorization based on UNESCO's cultural heritage definition presented in Section III-D and the requirements from RUTAS project. We also conduct a structural comparative evaluation using OQuaRE methodology among CURIOCITY, CIDOC CRM Standard (available as ERLANGEN-CRM), and ArCo Ontology  [71]. 9 : Perspectives, levels, and methods for a comparative study of ontologies  [35]  A. LEXICAL LEVEL At this level, the evaluation is based on similarity metrics that allow analyzing the proximity of concepts and related vocabulary within the domain, from the ontologies evaluated. To calculate these similarity metrics, we have developed a parser that allows the extraction of the ontology entities (i. e. , classes, relationships, properties) from their RDF/XML language implementations.