references.bib

@ARTICLE{9721302,
  author={Rana, Md Shohel and Nobi, Mohammad Nur and Murali, Beddhu and Sung, Andrew H.},
  journal={IEEE Access}, 
  title={Deepfake Detection: A Systematic Literature Review}, 
  year={2022},
  volume={10},
  number={},
  pages={25494-25513},
  doi={10.1109/ACCESS.2022.3154404}}
@misc{masood2021deepfakes,
      title={Deepfakes Generation and Detection: State-of-the-art, open challenges, countermeasures, and way forward}, 
      author={Momina Masood and Marriam Nawaz and Khalid Mahmood Malik and Ali Javed and Aun Irtaza},
      year={2021},
      eprint={2103.00484},
      archivePrefix={arXiv},
      primaryClass={cs.CR}
}
@misc{rössler2019faceforensics,
      title={FaceForensics++: Learning to Detect Manipulated Facial Images}, 
      author={Andreas Rössler and Davide Cozzolino and Luisa Verdoliva and Christian Riess and Justus Thies and Matthias Nießner},
      year={2019},
      eprint={1901.08971},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}
@article{GUO2022100067,
title = {A survey on blockchain technology and its security},
journal = {Blockchain: Research and Applications},
volume = {3},
number = {2},
pages = {100067},
year = {2022},
issn = {2096-7209},
doi = {https://doi.org/10.1016/j.bcra.2022.100067},
url = {https://www.sciencedirect.com/science/article/pii/S2096720922000070},
author = {Huaqun Guo and Xingjie Yu},
keywords = {Blockchain, Consensus algorithm, Smart contract, Risk, Blockchain security},
abstract = {Blockchain is a technology that has desirable features of decentralization, autonomy, integrity, immutability, verification, fault-tolerance, anonymity, auditability, and transparency. In this paper, we first carry out a deeper survey about blockchain technology, especially its history, consensus algorithms' quantitative comparisons, details of cryptography in terms of public key cryptography, Zero-Knowledge Proofs, and hash functions used in the blockchain, and the comprehensive list of blockchain applications. Further, the security of blockchain itself is a focus in this paper. In particular, we assess the blockchain security from risk analysis to derive comprehensive blockchain security risk categories, analyze the real attacks and bugs against blockchain, and summarize the recently developed security measures on blockchain. Finally, the challenges and research trends are presented to achieve more scalable and securer blockchain systems for the massive deployments.}
}
@article{doi:10.1080/17517575.2021.1939895,
author = {Xian Rong Zheng and Yang Lu},
title = {Blockchain technology – recent research and future trend},
journal = {Enterprise Information Systems},
volume = {16},
number = {12},
pages = {1939895},
year = {2022},
publisher = {Taylor & Francis},
doi = {10.1080/17517575.2021.1939895},


URL = { 
    
        https://doi.org/10.1080/17517575.2021.1939895
    
    

},
eprint = { 
    
        https://doi.org/10.1080/17517575.2021.1939895
    
    

}

}
@article{10.1371/journal.pone.0258995,
    doi = {10.1371/journal.pone.0258995},
    author = {Levis, Daniel AND Fontana, Francesco AND Ughetto, Elisa},
    journal = {PLOS ONE},
    publisher = {Public Library of Science},
    title = {A look into the future of blockchain technology},
    year = {2021},
    month = {11},
    volume = {16},
    url = {https://doi.org/10.1371/journal.pone.0258995},
    pages = {1-20},
    abstract = {In this paper, we use a Delphi approach to investigate whether, and to what extent, blockchain-based applications might affect firms’ organizations, innovations, and strategies by 2030, and, consequently, which societal areas may be mainly affected. We provide a deep understanding of how the adoption of this technology could lead to changes in Europe over multiple dimensions, ranging from business to culture and society, policy and regulation, economy, and technology. From the projections that reached a significant consensus and were given a high probability of occurrence by the experts, we derive four scenarios built around two main dimensions: the digitization of assets and the change in business models.},
    number = {11},

}
@misc{wang2023deepfake,
      title={Deepfake Detection: A Comprehensive Study from the Reliability Perspective}, 
      author={Tianyi Wang and Xin Liao and Kam Pui Chow and Xiaodong Lin and Yinglong Wang},
      year={2023},
      eprint={2211.10881},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}
@INPROCEEDINGS{10245532,
  author={Puri, Bharat and Kumar, Jagadeesh and Mukherjee, Somnath and V, Bhaskar S.},
  booktitle={2023 International Conference on Circuit Power and Computing Technologies (ICCPCT)}, 
  title={Analysis of Deepfake Detection Techniques}, 
  year={2023},
  volume={},
  number={},
  pages={71-76},
  doi={10.1109/ICCPCT58313.2023.10245532}}
@article{Nguyen_2022,
   title={Deep learning for deepfakes creation and detection: A survey},
   volume={223},
   ISSN={1077-3142},
   url={http://dx.doi.org/10.1016/j.cviu.2022.103525},
   DOI={10.1016/j.cviu.2022.103525},
   journal={Computer Vision and Image Understanding},
   publisher={Elsevier BV},
   author={Nguyen, Thanh Thi and Nguyen, Quoc Viet Hung and Nguyen, Dung Tien and Nguyen, Duc Thanh and Huynh-The, Thien and Nahavandi, Saeid and Nguyen, Thanh Tam and Pham, Quoc-Viet and Nguyen, Cuong M.},
   year={2022},
   month=oct, pages={103525} }
@article{nakamoto2009bitcoin,
  abstract = {A purely peer-to-peer version of electronic cash would allow online payments to be sent directly from one party to another without going through a financial institution. Digital signatures provide part of the solution, but the main benefits are lost if a trusted third party is still required to prevent double-spending. We propose a solution to the double-spending problem using a peer-to-peer network. The network timestamps transactions by hashing them into an ongoing chain of hash-based proof-of-work, forming a record that cannot be changed without redoing the proof-of-work. The longest chain not only serves as proof of the sequence of events witnessed, but proof that it came from the largest pool of CPU power. As long as a majority of CPU power is controlled by nodes that are not cooperating to attack the network, they'll generate the longest chain and outpace attackers. The network itself requires minimal structure. Messages are broadcast on a best effort basis, and nodes can leave and rejoin the network at will, accepting the longest proof-of-work chain as proof of what happened while they were gone.},
  added-at = {2022-06-15T13:43:05.000+0200},
  author = {Nakamoto, Satoshi},
  biburl = {https://www.bibsonomy.org/bibtex/2974d35fdb27dea57296ed2245556aa18/daniel_grm9},
  interhash = {423c2cdff70ba0cd0bca55ebb164d770},
  intrahash = {974d35fdb27dea57296ed2245556aa18},
  keywords = {itsecseminar},
  month = may,
  timestamp = {2022-06-15T13:43:05.000+0200},
  title = {Bitcoin: A Peer-to-Peer Electronic Cash System},
  url = {http://www.bitcoin.org/bitcoin.pdf},
  year = 2009
}
@article{Wang2021TheAO,
  title={The Applications of Blockchain in Artificial Intelligence},
  author={Ruonan Wang and Min Luo and Yihong Wen and Lianhai Wang and Kim-Kwang Raymond Choo and De-biao He},
  journal={Secur. Commun. Networks},
  year={2021},
  volume={2021},
  pages={6126247:1-6126247:16},
  url={https://api.semanticscholar.org/CorpusID:238213460}
}
@article{vyas2022,
author = {Vyas, Dr Sonali and Shabaz, Dr. Mohammad and Pandit, Prajjjawal and Parvathy, Rama and Isaac, Ofori},
year = {2022},
month = {05},
pages = {1-11},
title = {Integration of Artificial Intelligence and Blockchain Technology in Healthcare and Agriculture},
volume = {2022},
journal = {Journal of Food Quality},
doi = {10.1155/2022/4228448}
}
@article{zhang2021,
author = {Zhang, Zhonghua and Song, Xifei and Liu, Lei and Yin, Jie and Wang, Yu and Lan, Dapeng},
year = {2021},
month = {06},
pages = {1-15},
title = {Recent Advances in Blockchain and Artificial Intelligence Integration: Feasibility Analysis, Research Issues, Applications, Challenges, and Future Work},
volume = {2021},
journal = {Security and Communication Networks},
doi = {10.1155/2021/9991535}
}
@article{Wu2022,
author = {Wu, Yue and Li, Junxiang and Zhou, Jiru and Luo, Shichang and Song, Liwei},
year = {2022},
month = {01},
pages = {},
title = {Evolution Process and Supply Chain Adaptation of Smart Contracts in Blockchain},
volume = {2022},
journal = {Journal of Mathematics},
doi = {10.1155/2022/2839566}
}
@misc{passos2023review,
      title={A Review of Deep Learning-based Approaches for Deepfake Content Detection}, 
      author={Leandro A. Passos and Danilo Jodas and Kelton A. P. da Costa and Luis A. Souza Júnior and Douglas Rodrigues and Javier Del Ser and David Camacho and João Paulo Papa},
      year={2023},
      eprint={2202.06095},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}
@misc{tolosana2020deepfakes,
      title={DeepFakes and Beyond: A Survey of Face Manipulation and Fake Detection}, 
      author={Ruben Tolosana and Ruben Vera-Rodriguez and Julian Fierrez and Aythami Morales and Javier Ortega-Garcia},
      year={2020},
      eprint={2001.00179},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}
@inproceedings{afchar2018mesonet,
  title={MesoNet: a Compact Facial Video Forgery Detection Network},
  author={Afchar, Darius and Nozick, Vincent and Yamagishi, Junichi and Echizen, Isao},
  booktitle={2018 IEEE International Workshop on Information Forensics and Security (WIFS)},
  pages={1--7},
  year={2018},
  organization={IEEE}
}

@article{li2021multi,
  title={Multi-attentional Deepfake Detection},
  author={Li, Yuezun and Zhang, Siwei and Lyu, Siwei},
  journal={arXiv preprint arXiv:2101.04906},
  year={2021}
}
@misc{sariboz2021offchain,
      title={Off-chain Execution and Verification of Computationally Intensive Smart Contracts}, 
      author={Emrah Sariboz and Kartick Kolachala and Gaurav Panwar and Roopa Vishwanathan and Satyajayant Misra},
      year={2021},
      eprint={2104.09569},
      archivePrefix={arXiv},
      primaryClass={cs.CR}
}
@Article{ijgi10030137,
AUTHOR = {Kang, Youngok and Cho, Nahye and Yoon, Jiyoung and Park, Soyeon and Kim, Jiyeon},
TITLE = {Transfer Learning of a Deep Learning Model for Exploring Tourists’ Urban Image Using Geotagged Photos},
JOURNAL = {ISPRS International Journal of Geo-Information},
VOLUME = {10},
YEAR = {2021},
NUMBER = {3},
ARTICLE-NUMBER = {137},
URL = {https://www.mdpi.com/2220-9964/10/3/137},
ISSN = {2220-9964},
ABSTRACT = {Recently, as computer vision and image processing technologies have rapidly advanced in the artificial intelligence (AI) field, deep learning technologies have been applied in the field of urban and regional study through transfer learning. In the tourism field, studies are emerging to analyze the tourists’ urban image by identifying the visual content of photos. However, previous studies have limitations in properly reflecting unique landscape, cultural characteristics, and traditional elements of the region that are prominent in tourism. With the purpose of going beyond these limitations of previous studies, we crawled 168,216 Flickr photos, created 75 scenes and 13 categories as a tourist’ photo classification by analyzing the characteristics of photos posted by tourists and developed a deep learning model by continuously re-training the Inception-v3 model. The final model shows high accuracy of 85.77% for the Top 1 and 95.69% for the Top 5. The final model was applied to the entire dataset to analyze the regions of attraction and the tourists’ urban image in Seoul. We found that tourists feel attracted to Seoul where the modern features such as skyscrapers and uniquely designed architectures and traditional features such as palaces and cultural elements are mixed together in the city. This work demonstrates a tourist photo classification suitable for local characteristics and the process of re-training a deep learning model to effectively classify a large volume of tourists’ photos.},
DOI = {10.3390/ijgi10030137}
}
@article{castillo2021comprehensive,
  title={A Comprehensive Review of Deep-Learning-Based Methods for Image Forensics},
  author={Castillo Camacho, S. and Wang, Y.},
  journal={Journal of Image Forensics},
  year={2021}
}
@article{deng2019exploiting,
  title={Exploiting time-frequency patterns with LSTM-RNNs for low-bitrate audio restoration},
  author={Deng, Jiqiang and Zhang, Zixing and Zhang, Zhiyong and Schuller, Bj{\"o}rn},
  journal={IEEE/ACM Transactions on Audio, Speech, and Language Processing},
  volume={27},
  number={11},
  pages={1696--1708},
  year={2019},
  publisher={IEEE}
}

@article{radford2021robust,
  title={Robust Speech Recognition via Large-Scale Weak Supervision},
  author={Radford, Alec and Wu, Jeff and Child, Rewon and Luan, David and Amodei, Dario and Sutskever, Ilya},
  journal={arXiv preprint arXiv:2101.00374},
  year={2021}
}