IDEAS home Printed from https://ideas.repec.org/p/zbw/itsb21/238060.html
   My bibliography  Save this paper

How will users respond to the adversarial noise that prevents the generation of deepfakes?

Author

Listed:
  • Wang, Soyoung

Abstract

The development of artificial intelligence (AI) technology has made it easy for users to generate hyper-realistic fake media content, and its most representative by-product is called deepfake. However, considerable attention has been paid to the adverse effects of deepfakes as they are tightly connected to the production of fake news, financial frauds, or fake pornographies. The misuse of deepfakes led to a series of deepfake prevention studies, but most were post-detection methods. This study thus investigated deepfake malfunction-inducing technology that may forestall the generation of deepfake with PGD attack. In the next part of the study, overall preferences and intention to use were measured as people's responses to this technology. An online survey especially targeting those exposed to various media like social media influencers (SMIs), was conducted. The deepfakes started to malfunction after adding 0.009 levels of an adversarial noise as a preventive mechanism. From a technical viewpoint, higher noise was a more effective way to prevent deepfake synthesis, but from the user's viewpoint, noise as high as 0.03 was found to be appropriate. Individuals' intention to use was tested with Bulgurcu's ISP compliance model. It was found that SMIs' predictive evaluations on the cost and benefit of this technology influence their attitude, and consequently, their intention to use it. This study shows the value of collaborative studies of AI-based privacy security domain and media industry domain. It also expands the scope of the framework with thorough hypothetical testing in the deepfake context.

Suggested Citation

  • Wang, Soyoung, 2021. "How will users respond to the adversarial noise that prevents the generation of deepfakes?," 23rd ITS Biennial Conference, Online Conference / Gothenburg 2021. Digital societies and industrial transformations: Policies, markets, and technologies in a post-Covid world 238060, International Telecommunications Society (ITS).
    • Handle: RePEc:zbw:itsb21:238060
    as

    Download full text from publisher

    File URL: https://www.econstor.eu/bitstream/10419/238060/1/Wang.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Willison , Robert, 2006. "Understanding the Perpetration of Employee Computer Crime in the Organisational Context," Working Papers 2006-4, Copenhagen Business School, Department of Informatics.
    2. Ajzen, Icek, 1991. "The theory of planned behavior," Organizational Behavior and Human Decision Processes, Elsevier, vol. 50(2), pages 179-211, December.
    3. Kieran Mathieson, 1991. "Predicting User Intentions: Comparing the Technology Acceptance Model with the Theory of Planned Behavior," Information Systems Research, INFORMS, vol. 2(3), pages 173-191, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Muhammad Ali & Syed Ali Raza & Chin-Hong Puah & Mohd Zaini Abd Karim, 2017. "Islamic home financing in Pakistan: a SEM-based approach using modified TPB model," Housing Studies, Taylor & Francis Journals, vol. 32(8), pages 1156-1177, November.
    2. Wan, Calvin & Shen, Geoffrey Qiping & Yu, Ann, 2014. "The role of perceived effectiveness of policy measures in predicting recycling behaviour in Hong Kong," Resources, Conservation & Recycling, Elsevier, vol. 83(C), pages 141-151.
    3. Viswanath Venkatesh, 2000. "Determinants of Perceived Ease of Use: Integrating Control, Intrinsic Motivation, and Emotion into the Technology Acceptance Model," Information Systems Research, INFORMS, vol. 11(4), pages 342-365, December.
    4. Gao, Tao (Tony) & Rohm, Andrew J. & Sultan, Fareena & Pagani, Margherita, 2013. "Consumers un-tethered: A three-market empirical study of consumers' mobile marketing acceptance," Journal of Business Research, Elsevier, vol. 66(12), pages 2536-2544.
    5. van Hoesel, C.P.M. & Goossens, J.H.M. & Kroon, L.G., 2001. "A branch-and-cut approach for solving line planning problems," Research Memorandum 016, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
    6. Sumeet Gupta & Haejung Yun & Heng Xu & Hee-Woong Kim, 2017. "An exploratory study on mobile banking adoption in Indian metropolitan and urban areas: a scenario-based experiment," Information Technology for Development, Taylor & Francis Journals, vol. 23(1), pages 127-152, January.
    7. El Barachi, May & Salim, Taghreed Abu & Nyadzayo, Munyaradzi W. & Mathew, Sujith & Badewi, Amgad & Amankwah-Amoah, Joseph, 2022. "The relationship between citizen readiness and the intention to continuously use smart city services: Mediating effects of satisfaction and discomfort," Technology in Society, Elsevier, vol. 71(C).
    8. Kamrath, Carolin & Rajendran, Srinivasulu & Nenguwo, Ngoni & Afari-Sefa, Victor & Broring, Stefanie, 2018. "Adoption behavior of market traders: an analysis based on Technology Acceptance Model and Theory of Planned Behavior," International Food and Agribusiness Management Review, International Food and Agribusiness Management Association, vol. 21(6), July.
    9. Taneja, Shilpa & Ali, Liaqat, 2021. "Determinants of customers’ intentions towards environmentally sustainable banking: Testing the structural model," Journal of Retailing and Consumer Services, Elsevier, vol. 59(C).
    10. Donglin Han & Huiying (Cynthia) Hou & Hao Wu & Joseph H. K. Lai, 2021. "Modelling Tourists’ Acceptance of Hotel Experience-Enhancement Smart Technologies," Sustainability, MDPI, vol. 13(8), pages 1-19, April.
    11. Jonathan Jan Pieters & Alinda Kokkinou & Ton Kollenburg, 2022. "Understanding Blockchain Technology Adoption by Non-experts: an Application of the Unified Theory of Acceptance and Use of Technology (UTAUT)," SN Operations Research Forum, Springer, vol. 3(1), pages 1-19, March.
    12. Song, Jinzhu & Drennan, Judy C. & Andrews, Lynda M., 2012. "Exploring regional differences in Chinese consumer acceptance of new mobile technology: A qualitative study," Australasian marketing journal, Elsevier, vol. 20(1), pages 80-88.
    13. Giao, Ha Nam Khanh & Tuan, Huynh Quoc, 2021. "Intention To Buy Air Ticket Online Of Vietnamese Consumers," OSF Preprints 867s5, Center for Open Science.
    14. Ahlam Al-Muwil & Vishanth Weerakkody & Ramzi El-haddadeh & Yogesh Dwivedi, 2019. "Balancing Digital-By-Default with Inclusion: A Study of the Factors Influencing E-Inclusion in the UK," Information Systems Frontiers, Springer, vol. 21(3), pages 635-659, June.
    15. Nripendra P. Rana & Yogesh K. Dwivedi & Banita Lal & Michael D. Williams & Marc Clement, 2017. "Citizens’ adoption of an electronic government system: towards a unified view," Information Systems Frontiers, Springer, vol. 19(3), pages 549-568, June.
    16. Xiaoyun Zhang & Feng Dong, 2020. "Why Do Consumers Make Green Purchase Decisions? Insights from a Systematic Review," IJERPH, MDPI, vol. 17(18), pages 1-25, September.
    17. Valentin Ngadi, 2016. "Factors Affecting The Adoption Of The Personality Of Design [Les Facteurs Determinants De La Diffusion/Adoption De La Personnalite Du Design]," Working Papers hal-01296338, HAL.
    18. Wong Lai Soon & Bobby Chai Boon Hui & Wong Kee Luen, 2013. "Joining the New Band: Factors Triggering the Intentions of Malaysian College and University Students to Adopt 4G Broadband," Information Management and Business Review, AMH International, vol. 5(2), pages 58-65.
    19. Waleed A. Hammood & Ruzaini Abdullah Arshah & Salwana Mohamad Asmara & Hussam Al Halbusi & Omar A. Hammood & Salem Al Abri, 2021. "A Systematic Review on Flood Early Warning and Response System (FEWRS): A Deep Review and Analysis," Sustainability, MDPI, vol. 13(1), pages 1-24, January.
    20. Kathrin Dudenhöffer, 2013. "Why electric vehicles failed," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 24(2), pages 95-124, July.

    More about this item

    Keywords

    Deepfake; Adversarial noise; Image quality; Intention to use;
    All these keywords.

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:zbw:itsb21:238060. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ZBW - Leibniz Information Centre for Economics (email available below). General contact details of provider: http://www.itsworld.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.