Deepfakes: Deceptions, mitigations, and opportunities

Mustak, Mekhail; Salminen, Joni; Mäntymäki, Matti; Rahman, Arafat; Dwivedi, Yogesh K.

doi:10.1016/j.jbusres.2022.113368

Cited by 61 publications

(34 citation statements)

References 72 publications

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“…Conversely, if "easy" AI governance problems are not solved satisfactorily, they may accumulate to form more complex emergent problems. For example, organizations lacking quality assurance mechanisms and offering irresponsible image manipulation products may exacerbate the problems that deepfakes pose vis-à-vis trust [41]. Indeed, portraying the two problem areas as unconnected would be irresponsible, and more work should be devoted to understanding the complex connections between them.…”

Section: Discussionmentioning

confidence: 99%

“…At the societal level, the widespread use of AI raises questions about the future of democracy in the context of sophisticated electoral manipulation, the future of work due to increased automation, and warfare with autonomous lethal robots [13]. For example, misinformation, such as the deepfakes produced by generative adversarial networks, can seriously threaten citizens' trust in media sources [41], [42].…”

Section: The "Hard Problem" Of Ai Governancementioning

confidence: 99%

“…Organizational strategies and processes [11], organizational ethics guidelines [32], technical tools and methods (e.g., software engineering workflows, audit trails) [16], [47] Binding legislation [30], human rights frameworks [53], establishment of new global or regional institutions [49] Ethical basis Business ethics, corporate social responsibility [36], [37], [51], deontology [54] Ethics of emerging technologies, data justice, human rights, technology for good [55] Example problem domains Organizational governance processes [11], [12], creation of responsible AI-enabled consumer products [56] Deepfake technologies' undermining of trust in media [41], [42], frameworks for responsible innovation [57] Illustrative literature examples [10]- [12], [31]- [33] [1], [13], [40], [53] The first characteristic, time horizon, is implicit rather than explicit in the literature. Arguably, short-and long-term concerns are involved in both problem areas.…”

Section: Potential Solutionsmentioning

confidence: 99%

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Discerning Between the “Easy” and “Hard” Problems of AI Governance

Minkkinen

Mäntymäki

2023

IEEE Trans. Technol. Soc.

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While there is widespread consensus that artificial intelligence (AI) needs to be governed owing to its rapid diffusion and societal implications, the current scholarly discussion on AI governance is dispersed across numerous disciplines and problem domains. This paper clarifies the situation by discerning two problem areas, metaphorically titled the "easy" and "hard" problems of AI governance, using a dialectic theory synthesis approach. The "easy problem" of AI governance concerns how organizations' design, development, and use of AI systems align with laws, values, and norms stemming from legislation, ethics guidelines, and the surrounding society. Organizations can provisionally solve the "easy problem" by implementing appropriate organizational mechanisms to govern data, algorithms, and algorithmic systems. The "hard problem" of AI governance concerns AI as a general-purpose technology that transforms organizations and societies. Rather than a matter to be resolved, the "hard problem" is a sensemaking process regarding socio-technical change. Partial solutions to the "hard problem" may open unforeseen issues. While societies should not lose track of the "hard problem" of AI governance, there is significant value in solving the "easy problem" for two reasons. First, the "easy problem" can be provisionally solved by tackling bias, harm, and transparency issues. Second, solving the "easy problem" helps solve the "hard problem," as responsible organizational AI practices create virtuous rather than vicious cycles.

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Section: Discussionmentioning

confidence: 99%

Section: The "Hard Problem" Of Ai Governancementioning

confidence: 99%

Section: Potential Solutionsmentioning

confidence: 99%

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Discerning Between the “Easy” and “Hard” Problems of AI Governance

Minkkinen

Mäntymäki

2023

IEEE Trans. Technol. Soc.

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“…There exist some review papers on this topic (e.g., [ 5 , 24 , 47 , 48 ]), but they are focused mainly on deepfake or synthetic samples using generative adversarial networks. Moreover, most survey articles (e.g., [ 4 , 49 , 50 ]) were mainly written from an academic point of view and not from a practical development point of view. Also, they did not cover the advent of very recent face manipulation methods and new deepfake generation and detection techniques.…”

Section: Introductionmentioning

confidence: 99%

Deepfakes Generation and Detection: A Short Survey

Akhtar

2023

J. Imaging

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Advancements in deep learning techniques and the availability of free, large databases have made it possible, even for non-technical people, to either manipulate or generate realistic facial samples for both benign and malicious purposes. DeepFakes refer to face multimedia content, which has been digitally altered or synthetically created using deep neural networks. The paper first outlines the readily available face editing apps and the vulnerability (or performance degradation) of face recognition systems under various face manipulations. Next, this survey presents an overview of the techniques and works that have been carried out in recent years for deepfake and face manipulations. Especially, four kinds of deepfake or face manipulations are reviewed, i.e., identity swap, face reenactment, attribute manipulation, and entire face synthesis. For each category, deepfake or face manipulation generation methods as well as those manipulation detection methods are detailed. Despite significant progress based on traditional and advanced computer vision, artificial intelligence, and physics, there is still a huge arms race surging up between attackers/offenders/adversaries (i.e., DeepFake generation methods) and defenders (i.e., DeepFake detection methods). Thus, open challenges and potential research directions are also discussed. This paper is expected to aid the readers in comprehending deepfake generation and detection mechanisms, together with open issues and future directions.

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“…Thus, social media fake news (hereafter "fake news") becomes an important research agenda among SC researchers (e.g. Wisker, 2020;Spanaki et al, 2021;Mustak et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

Impact of fake news on firm performance during COVID-19: an assessment of moderated serial mediation using PLS-SEM

Khan

Chowdhury

Hossain

et al. 2022

IJPDLM

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PurposeFake news on social media about COVID-19 pandemic and its associated issues (e.g. lockdown) caused public panic that lead to supply chain (SC) disruptions, which eventually affect firm performance. The purpose of this study is to understand how social media fake news effects firm performance, and how to mitigate such effects.Design/methodology/approachGrounded on dynamic capability view (DCV), this study suggests that social media fake news effects firm performance via SC disruption (SCD) and SC resilience (SCR). Moreover, the relation between SCD and SCR is contingent upon SC learning (SCL) – a moderated mediation effect. To validate this complex model, the authors suggest effectiveness of using partial least squares structural equation modeling (PLS-SEM). Using an online survey, the results support the authors’ hypotheses.FindingsThe results suggest that social media fake news does not affect firm performance directly. However, the authors’ serial mediation test confirms that SCD and SCR sequentially mediate the relationship between social media fake news and firm performance. In addition, a moderated serial mediation test confirms that a higher level of SCL strengthens the SCD–SCR relationship.Research limitations/implicationsThis work offers a new theoretical and managerial perspective to understand the effect of fake news on firm performance, in the context of crises, e.g. COVID-19. In addition, this study offers the advancement of PLS as more robust for real-world applications and more advantageous when models are complex.Originality/valuePrior studies in the SC and marketing domain suggest different effects of social media fake news on consumer behavior (e.g. panic buying) and SCD, respectively. This current study is a unique effort that investigates the ultimate effect of fake news on firm performance with complex causal relationships via SCD, SCR and SCL.

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Deepfakes: Deceptions, mitigations, and opportunities

Cited by 61 publications

References 72 publications

Discerning Between the “Easy” and “Hard” Problems of AI Governance

Discerning Between the “Easy” and “Hard” Problems of AI Governance

Deepfakes Generation and Detection: A Short Survey

Impact of fake news on firm performance during COVID-19: an assessment of moderated serial mediation using PLS-SEM

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