Academic literature on the topic 'Image trustworthiness'
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Journal articles on the topic "Image trustworthiness"
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Hu, Donghui, Xiaotian Zhang, Yuqi Fan, Zhong-Qiu Zhao, Lina Wang, Xintao Wu, and Xindong Wu. "On digital image trustworthiness." Applied Soft Computing 48 (November 2016): 240–53. http://dx.doi.org/10.1016/j.asoc.2016.07.010.
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Lago, Federica, Quoc-Tin Phan, and Giulia Boato. "Visual and Textual Analysis for Image Trustworthiness Assessment within Online News." Security and Communication Networks 2019 (April 14, 2019): 1–14. http://dx.doi.org/10.1155/2019/9236910.
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The majority of news published online presents one or more images or videos, which make the news more easily consumed and therefore more attractive to huge audiences. As a consequence, news with catchy multimedia content can be spread and get viral extremely quickly. Unfortunately, the availability and sophistication of photo editing software are erasing the line between pristine and manipulated content. Given that images have the power of bias and influence the opinion and behavior of readers, the need of automatic techniques to assess the authenticity of images is straightforward. This paper aims at detecting images published within online news that have either been maliciously modified or that do not represent accurately the event the news is mentioning. The proposed approach composes image forensic algorithms for detecting image tampering, and textual analysis as a verifier of images that are misaligned to textual content. Furthermore, textual analysis can be considered as a complementary source of information supporting image forensics techniques when they falsely detect or falsely ignore image tampering due to heavy image postprocessing. The devised method is tested on three datasets. The performance on the first two shows interesting results, with F1-score generally higher than 75%. The third dataset has an exploratory intent; in fact, although showing that the methodology is not ready for completely unsupervised scenarios, it is possible to investigate possible problems and controversial cases that might arise in real-world scenarios.
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Kristian, Galih. "Pengaruh Brand Ambassador Dan Brand Image Terhadap Keputusan Pembelian Dengan Trustworthiness Sebagai Variabel Intervening." Jurnal Ilmiah Manajemen Kesatuan 9, no. 3 (November 29, 2021): 41–50. http://dx.doi.org/10.37641/jimkes.v9i3.570.
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This study aims to determine the effect of brand ambassador and brand image on purchasing decisions with trustworthiness as an intervening variable in the Yogyakarta Starcross distribution. the type of research used in this research is descriptive quantitative. The population used is the customer of the Starcross Yogyakarta distribution as many as 100 people. The sample used in this study is 100 customers using the sampling technique used is non-probability sampling using a data collection method using a questionnaire. The data analysis technique in this study used multiple linear regression. Based on the results of this study, brand ambassadors have a significant positive effect on purchasing decisions, brand image has a significant positive effect on purchasing decisions, brand ambassadors have a significant positive effect on trustworthiness, brand image has a significant positive effect on trustworthiness, trustworthiness has a significant positive effect on purchasing decisions. In this case, trustworthiness as an intervening variable is able to mediate the relationship between brand ambassadors on purchasing decisions, and brand image on purchasing decisions.
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Saenger, Christina, and Doori Song. "Fostering beneficial consumer responses to in-feed sponsored articles." Journal of Consumer Marketing 36, no. 5 (August 12, 2019): 643–54. http://dx.doi.org/10.1108/jcm-12-2017-2475.
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Purpose This paper aims to explore content-related factors that can foster beneficial consumer responses to one kind of native advertising: in-feed sponsored articles. Specifically, studies examine how informational versus entertaining content interact with the content’s brand image congruity to affect brand attitudes through brand trustworthiness and identify the roles played by advertising value and perceived deceptiveness. Design/methodology/approach Experimental survey-based research with a between-subjects design was conducted, manipulating the content type (entertaining/informational) and brand image congruity (low/high) and measuring brand attitude, brand trustworthiness, advertising value and perceived deceptiveness. Participants were recruited via Amazon’s MTurk, and data were collected via online surveys in Qualtrics. Findings Results reveal that high brand image congruity generates more favorable brand attitudes for informational in-feed sponsored articles, and low brand image congruity generates more favorable brand attitudes for entertaining in-feed sponsored articles, through perceptions of brand trustworthiness. Enhanced brand trustworthiness results from increased advertising value for informational in-feed sponsored articles that are high in brand image congruity. Reduced brand trustworthiness results from increased perceptions of deceptiveness for entertaining in-feed sponsored articles that are high in brand image congruity. Originality/value While much academic research on native advertising focuses on its negative aspects, the present research identifies content-related factors that foster beneficial consumer responses to in-feed sponsored articles, including enhanced perceptions of brand trustworthiness and more favorable brand attitudes, due to differences in consumers’ perceptions of advertising value and deceptiveness. Managerially, this work can help branded content creators design effective in-feed sponsored articles.
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Shen, Cuihua, Mona Kasra, Wenjing Pan, Grace A. Bassett, Yining Malloch, and James F. O’Brien. "Fake images: The effects of source, intermediary, and digital media literacy on contextual assessment of image credibility online." New Media & Society 21, no. 2 (September 24, 2018): 438–63. http://dx.doi.org/10.1177/1461444818799526.
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Fake or manipulated images propagated through the Web and social media have the capacity to deceive, emotionally distress, and influence public opinions and actions. Yet few studies have examined how individuals evaluate the authenticity of images that accompany online stories. This article details a 6-batch large-scale online experiment using Amazon Mechanical Turk that probes how people evaluate image credibility across online platforms. In each batch, participants were randomly assigned to 1 of 28 news-source mockups featuring a forged image, and they evaluated the credibility of the images based on several features. We found that participants’ Internet skills, photo-editing experience, and social media use were significant predictors of image credibility evaluation, while most social and heuristic cues of online credibility (e.g. source trustworthiness, bandwagon, intermediary trustworthiness) had no significant impact. Viewers’ attitude toward a depicted issue also positively influenced their credibility evaluation.
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Marselina, Dian, and Edward H. Siregar. "Pengaruh Celebrity Endorser terhadap Brand Image pada Kosmetik Wardah di Bogor." Jurnal Manajemen dan Organisasi 8, no. 1 (November 3, 2017): 15–27. http://dx.doi.org/10.29244/jmo.v8i1.18598.
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PT Paragon Technology and Innovation merupakan salah satu perusahaan lokal yang bergerak di bidang produksi kosmetik yang menggunakan celebrity endorser sebagai pendukung dalam kegiatan promosinya. Tujuan dari penelitian ini adalah untuk mengetahui pengaruh celebrity endorser terhadap brand image, atribut celebrity endorser yang meliputi trustworthiness, expertise, attractiveness, respect dan similarity dari endorser Wardah yang bernama Inneke Koesherawati (IK). Penelitian ini menggunakan teknik purposive sampling di wilayah Bogor Tengah dan Bogor Barat. Penelitian diuji dengan menggunakan metode analisis regresi linier berganda dilengkapi dengan uji F dan uji T. Hasil analisis menunjukkan bahwa variabel independen yaitu trustworthiness, expertise, attractiveness, respect dan similarity secara bersama-sama berpengaruh signifikan terhadap brand image Wardah. Secara parsial hanya tiga variabel yaitu trustworthiness, attractiveness and similarity yang berpengaruh signifikan terhadap brand image Wardah. Variabel attractiveness memiliki pengaruh yang dominan terhadap brand image Wardah.
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Rosa, Aslamia, Iisnawati Iisnawati, and Islahuddin Daud. "Key Factors Analysis of E-Commerce and Marketplace Purchasing Decision in Palembang." SRIWIJAYA INTERNATIONAL JOURNAL OF DYNAMIC ECONOMICS AND BUSINESS 2, no. 4 (January 22, 2019): 347. http://dx.doi.org/10.29259/sijdeb.v2i4.347-362.
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We could find the existing of many e-commerce and market place in Palembang, such as MatahariMall.com, Zalora, Lazada, Tokopedia, Blibli, Shopee, Elevania and Bukalapak. To win the competition and build a sustainable competitiveness, those e-commerce and market place need to hold the key factors of online purchase decision. This research purpose is to investigate online shopping experience, word of mouth and web brand image affect to web trustworthiness and whether purchase decision affected by web trustworthiness and online shopping experience. The data taken from questionnaires that distributed to 100 respondents in Palembang. This research analyzed by using Structural Equation Modelling (SEM). The results show a positive influence between online shopping experience and branding image to web trustworthiness. Word of mouth does not have influence to web trustworthiness. The result found that positive influence between web trustworthiness to purchase decision, whereas online shopping experience does not have influence to purchase decision.
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Gogan, Taylor, Jennifer Beaudry, and Julian Oldmeadow. "Within-Person Variability in First Impressions From Faces." Perception 50, no. 7 (May 30, 2021): 595–614. http://dx.doi.org/10.1177/03010066211019727.
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Perceptions of an individual can change dramatically across different images of their face. Questions remain as to whether some traits are more sensitive to image variability than others. To investigate this issue, we constructed a database of 340 naturalistic images consisting of 20 photos of 17 individuals. In this preregistered study, 95 participants rated all 340 images on one of three traits: trustworthiness, dominance, or attractiveness. Across images, participants’ trustworthiness ratings tended to vary more than dominance, which in turn varied more than attractiveness; however, the relative differences between traits depended on the identity in question. Importantly, despite the variability in ratings within identities, there were substantial differences between individuals, suggesting that these trait judgements are based to some degree on relatively invariant facial characteristics. We found greater between-identity variability for attractiveness judgements compared to trustworthiness and dominance. Future research should further investigate the extent to which each trait dimension is tied to the identity of the faces.
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Said, Christopher P., Sean G. Baron, and Alexander Todorov. "Nonlinear Amygdala Response to Face Trustworthiness: Contributions of High and Low Spatial Frequency Information." Journal of Cognitive Neuroscience 21, no. 3 (March 2009): 519–28. http://dx.doi.org/10.1162/jocn.2009.21041.
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Previous neuroimaging research has shown amygdala sensitivity to the perceived trustworthiness of neutral faces, with greater responses to untrustworthy compared with trustworthy faces. This observation is consistent with the common view that the amygdala encodes fear and is preferentially responsive to negative stimuli. However, some studies have shown greater amygdala activation to positive compared with neutral stimuli. The first goal of this study was to more fully characterize the amygdala response to face trustworthiness by modeling its activation with both linear and nonlinear predictors. Using fMRI, we report a nonmonotonic response profile, such that the amygdala responds strongest to highly trustworthy and highly untrustworthy faces. This finding complicates future attempts to make inferences about mental states based on activation in the amygdala. The second goal of the study was to test for modulatory effects of image spatial frequency filtering on the amygdala response. We predicted greater amygdala sensitivity to face trustworthiness for low spatial frequency images compared with high spatial frequency images. Instead, we found that both frequency ranges provided sufficient information for the amygdala to differentiate faces on trustworthiness. This finding is consistent with behavioral results and suggests that trustworthiness information may reach the amygdala through pathways carrying both coarse and fine resolution visual signals.
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Desfiandi, Andi, S. Suman Rajest, P. S. Venkateswaran, M. Palani Kumar, and Sonia Singh. "COMPANY CREDIBILITY: A TOOL TO TRIGGER POSITIVE CSR IMAGE IN THE CAUSE-BRAND ALLIANCE CONTEXT IN INDONESIA." Humanities & Social Sciences Reviews 7, no. 6 (November 25, 2019): 320–31. http://dx.doi.org/10.18510/hssr.2019.7657.
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Purpose of study: This research aims to analyze the mediating effect of corporate social responsibility (CSR) image in the effect of company credibility dimensions (trustworthiness and expertise) on participation intention, in the cause–brand alliance (CBA) context.
Methodology: The sample design which is used is purposive sampling with the sample criteria as the stakeholders of the University of Lampung, Indonesia. Data were collected by direct interview. Multiple regression analysis is used to test the hypotheses with 160 university’s stakeholders, using purposive sampling.
Result: The results show that trustworthiness and expertise have a directly positive significant effect on CSR image. However, expertise statistically has a greater positive significant effect on consumer participation intention toward the CBA than trustworthiness. This finding is contrary to the contrast-effect theoretical framework (Dean, 2003) and balance theory (Heider, 1958). Newly finding is that CSR image considered a mediating role in the effect of trustworthiness and expertise credibility on participation intention.
Implications: This implies that in the context of CBA, the company’s trustworthiness and expertise can be a more useful tool to trigger the positive CSR image in encouraging the stakeholders’ perception to buy the products and services or brand of the company implementing CSR, because CBA practices are considered as a genuine social cause, not as a promotion tool.
Novelty/Originality of this study: In this study, the collected data uses the cross-sectional design and the CBA context uses CBA practices implemented by agriculture, education service, beverage industry, and a bank stated-owned enterprise.
Dissertations / Theses on the topic "Image trustworthiness"
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Islam, Mohammad. "Measuring trustworthiness of image data in the internet of things environment." Thesis, Federation University Australia, 2021. http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/177521.
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Internet of Things (IoT) image sensors generate huge volumes of digital images every day. However, easy availability and usability of photo editing tools, the vulnerability in communication channels and malicious software have made forgery attacks on image sensor data effortless and thus expose IoT systems to cyberattacks. In IoT applications such as smart cities and surveillance systems, the smooth operation depends on sensors’ sharing data with other sensors of identical or different types. Therefore, a sensor must be able to rely on the data it receives from other sensors; in other words, data must be trustworthy. Sensors deployed in IoT applications are usually limited to low processing and battery power, which prohibits the use of complex cryptography and security mechanism and the adoption of universal security standards by IoT device manufacturers. Hence, estimating the trust of the image sensor data is a defensive solution as these data are used for critical decision-making processes. To our knowledge, only one published work has estimated the trustworthiness of digital images applied to forensic applications. However, that study’s method depends on machine learning prediction scores returned by existing forensic models, which limits its usage where underlying forensics models require different approaches (e.g., machine learning predictions, statistical methods, digital signature, perceptual image hash). Multi-type sensor data correlation and context awareness can improve the trust measurement, which is absent in that study’s model. To address these issues, novel techniques are introduced to accurately estimate the trustworthiness of IoT image sensor data with the aid of complementary non-imagery (numeric) data-generating sensors monitoring the same environment. The trust estimation models run in edge devices, relieving sensors from computationally intensive tasks. First, to detect local image forgery (splicing and copy-move attacks), an innovative image forgery detection method is proposed based on Discrete Cosine Transformation (DCT), Local Binary Pattern (LBP) and a new feature extraction method using the mean operator. Using Support Vector Machine (SVM), the proposed method is extensively tested on four well-known publicly available greyscale and colour image forgery datasets and on an IoT-based image forgery dataset that we built. Experimental results reveal the superiority of our proposed method over recent state-of-the-art methods in terms of widely used performance metrics and computational time and demonstrate robustness against low availability of forged training samples. Second, a robust trust estimation framework for IoT image data is proposed, leveraging numeric data-generating sensors deployed in the same area of interest (AoI) in an indoor environment. As low-cost sensors allow many IoT applications to use multiple types of sensors to observe the same AoI, the complementary numeric data of one sensor can be exploited to measure the trust value of another image sensor’s data. A theoretical model is developed using Shannon’s entropy to derive the uncertainty associated with an observed event and Dempster-Shafer theory (DST) for decision fusion. The proposed model’s efficacy in estimating the trust score of image sensor data is analysed by observing a fire event using IoT image and temperature sensor data in an indoor residential setup under different scenarios. The proposed model produces highly accurate trust scores in all scenarios with authentic and forged image data. Finally, as the outdoor environment varies dynamically due to different natural factors (e.g., lighting condition variations in day and night, presence of different objects, smoke, fog, rain, shadow in the scene), a novel trust framework is proposed that is suitable for the outdoor environments with these contextual variations. A transfer learning approach is adopted to derive the decision about an observation from image sensor data, while also a statistical approach is used to derive the decision about the same observation from numeric data generated from other sensors deployed in the same AoI. These decisions are then fused using CertainLogic and compared with DST-based fusion. A testbed was set up using Raspberry Pi microprocessor, image sensor, temperature sensor, edge device, LoRa nodes, LoRaWAN gateway and servers to evaluate the proposed techniques. The results show that CertainLogic is more suitable for measuring the trustworthiness of image sensor data in an outdoor environment.Doctor of Philosophy
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Lago, Federica. "On the preservation of media trustworthiness in the social media era." Doctoral thesis, Università degli studi di Trento, 2022. http://hdl.handle.net/11572/334996.
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The amount of multimedia content shared everyday online recently underwent a dramatic increase. This, combined with the stunning realism of fake images that can be generated with AI-based technologies, undermines the trustworthiness of online information sources. In this work, we tackle the problem of preserving media trustworthiness online from two different points of view. The first one consists in assessing the human ability to spot fake images, focusing in particular on synthetic faces, which are extremely realistic and can represent a severe threat if used to disseminate fake news. A perception study allowed us to prove for the first time how people are more prone to question the reality of authentic pictures rather than the one of last-generation AI-generated images. Secondly, we focused on social media forensics: our goal is to reconstruct the history of an image shared or re-shared online as typically happens nowadays. We propose a new framework that is able to trace the history of an image over multiple sharings. This framework improves the state of the art and has the advantage of being easily extensible with new methods and thus adapt to new datasets and scenarios. In fact, in this environment of fast-paced technological evolution, being able to adapt is fundamental to preserve our trust in what we see.
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Diep, Lisa, and Amanda Stedt. "In HOs we trust : how crises affect brand image and trustworthiness in humanitarian organizations." Thesis, Högskolan Kristianstad, Sektionen för hälsa och samhälle, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:hkr:diva-12736.
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During the past five years the Red Cross has been involved in two different internal crises. All of them can be related to trustworthiness and brand image. Why would someone keep donating money to an organization that has been involved in fraud and suspicion of corruption? The purpose of this dissertation is to explore how non-governmental (NGO) and non-profit (NPO) organizations rebuild their brand image and trustworthiness after a crisis. We will look at the problem from both the organization’s and the public’s point of view. The dissertation is based on theories about brand image, trustworthiness and crisis management. For the theoretical framework a model was created from these different theories. This study is conducted as a qualitative case study with a realistic philosophy. The findings and analysis shows that the Red Cross has an elaborated crisis management with a good base that can be adapted to suit each situation. However, there are some miscommunications between the organization and the public. Therefore, we suggest three improvements that can be made: communicate directly to the public, investigate immediately when suspicions occur, communicate the internal functions to the public. The first conclusion is that their form of reactive crisis management is that they have a weak pre-emptive crisis management, which consists of diffuse guidelines. After the crisis, these guidelines are adapted to the crisis. The second conclusion is that the organizations brand image and trustworthiness was affected negatively directly after the crisis occurred. However, today both the brand image and the trustworthiness have become stronger than before the crisis. Recommendation for future research is that it can be conducted in another area and include the behavior aspects of the respondents. In addition, the crisis communication team could be investigated.
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Quan, Weize. "Detection of computer-generated images via deep learning." Thesis, Université Grenoble Alpes, 2020. http://www.theses.fr/2020GRALT076.
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Avec les progrès des outils logiciels d'édition et de génération d'images, il est devenu plus facile de falsifier le contenu des images ou de créer de nouvelles images, même pour les novices. Ces images générées, telles que l'image de rendu photoréaliste et l'image colorisée, ont un réalisme visuel de haute qualité et peuvent potentiellement menacer de nombreuses applications importantes. Par exemple, les services judiciaires doivent vérifier que les images ne sont pas produites par la technologie de rendu infographique, les images colorisées peuvent amener les systèmes de reconnaissance / surveillance à produire des décisions incorrectes, etc. Par conséquent, la détection d'images générées par ordinateur a attiré une large attention dans la communauté de recherche en sécurité de multimédia. Dans cette thèse, nous étudions l'identification de différents types d'images générées par ordinateur, y compris l'image de rendu et l'image coloriée. Nous nous intéressons à identifier si une image est acquise par une caméra ou générée par un programme informatique. L'objectif principal est de concevoir un détecteur efficace, qui a une précision de classification élevée et une bonne capacité de généralisation. Nous considérons la construction de jeux de données, l'architecture du réseau de neurones profond, la méthode d'entraînement, la visualisation et la compréhension, pour les problèmes d'investigation légale des images considérés. Nos principales contributions sont : (1) une méthode de détection d'image colorisée basée sur l'insertion d'échantillons négatifs, (2) une méthode d'amélioration de la généralisation pour la détection d'image colorisée, (3) une méthode d'identification d'image naturelle et d'image de rendu basée sur le réseau neuronal convolutif, et (4) une méthode d'identification d'image de rendu basée sur l'amélioration de la diversité des caractéristiques et des échantillons contradictoiresWith the advances of image editing and generation software tools, it has become easier to tamper with the content of images or create new images, even for novices. These generated images, such as computer graphics (CG) image and colorized image (CI), have high-quality visual realism, and potentially throw huge threats to many important scenarios. For instance, the judicial departments need to verify that pictures are not produced by computer graphics rendering technology, colorized images can cause recognition/monitoring systems to produce incorrect decisions, and so on. Therefore, the detection of computer-generated images has attracted widespread attention in the multimedia security research community. In this thesis, we study the identification of different computer-generated images including CG image and CI, namely, identifying whether an image is acquired by a camera or generated by a computer program. The main objective is to design an efficient detector, which has high classification accuracy and good generalization capability. Specifically, we consider dataset construction, network architecture, training methodology, visualization and understanding, for the considered forensic problems. The main contributions are: (1) a colorized image detection method based on negative sample insertion, (2) a generalization method for colorized image detection, (3) a method for the identification of natural image (NI) and CG image based on CNN (Convolutional Neural Network), and (4) a CG image identification method based on the enhancement of feature diversity and adversarial samples
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Hunter, Erik J. "Celebrity entrepreneurship and celebrity endorsement : similarities, differences and the effect of deeper engagement." Thesis, Queensland University of Technology, 2010. https://eprints.qut.edu.au/38902/1/Erik_Hunter_Thesis.pdf.
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Increasingly, celebrities appear not only as endorsers for products but are apparently engaged in entrepreneurial roles as initiators, owners and perhaps even managers in the ventures that market the products they promote. Despite being extensively referred to in popular media, scholars have been slow to recognise the importance of this new phenomenon. This thesis argues theoretically and shows empirically that celebrity entrepreneurs are more effective communicators than typical celebrity endorsers because of their increased engagement with ventures. I theorise that greater engagement increases the celebrity‘s emotional involvement as perceived by consumers. This is an endorser quality thus far neglected in the marketing communications literature. In turn, emotional involvement, much like the empirically established dimensions trustworthiness, expertise and attractiveness, should affect traditional outcome variables such as attitude towards the advertisement and brand. On the downside, increases in celebrity engagement may lead to relatively stronger and worsening changes in attitudes towards the brand if and when negative information about the celebrity is revealed. A series of eight experiments was conducted on 781 Swedish and Baltic students and 151 Swedish retirees. Though there were nuanced differences and additional complexities in each experiment, participants‘ reactions to advertisements containing a celebrity portrayed as a typical endorser or entrepreneur were recorded. The overall results of these experiments suggest that emotional involvement can be successfully operationalised as distinct from variables previously known to influence communication effectiveness. In addition, emotional involvement has positive effects on attitudes toward the advertisement and brand that are as strong as the predictors traditionally applied in the marketing communications literature. Moreover, the celebrity entrepreneur condition in the experimental manipulation consistently led to an increase in emotional involvement and to a lesser extent trustworthiness, but not expertise and attractiveness. Finally, negative celebrity information led to a change in participants‘ attitudes towards the brand which were more strongly negative for celebrity entrepreneurs than celebrity endorsers. In addition, the effect of negative celebrity information on a company‘s brand is worse when they support the celebrity rather than fire them. However, this effect did not appear to interact with the celebrity‘s purported engagement.
Book chapters on the topic "Image trustworthiness"
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Bruza, Peter D., and Lauren Fell. "Are Decisions of Image Trustworthiness Contextual? A Pilot Study." In Quantum Interaction, 39–50. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-35895-2_3.
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Beetz, Michael, Uwe Engel, Nina Hoyer, Lorenz Kähler, Hagen Langer, Holger Schultheis, and Sirko Straube. "Trustworthiness and Well-Being: The Ethical, Legal, and Social Challenge of Robotic Assistance." In Robots in Care and Everyday Life, 1–26. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-11447-2_1.
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AbstractIf a technology lacks social acceptance, it cannot realize dissemination into society. The chapter thus illuminates the ethical, legal, and social implications of robotic assistance in care and daily life. It outlines a conceptual framework and identifies patterns of trust in human–robot interaction. The analysis relates trust in robotic assistance and its anticipated use to open-mindedness toward technical innovation and reports evidence that this self-image unfolds its psychological impact on accepting robotic assistance through the imagined well-being that scenarios of future human–robot interaction evoke in people today. All findings come from the population survey of the Bremen AI Delphi study.
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Gragnaniello, Diego, Francesco Marra, and Luisa Verdoliva. "Detection of AI-Generated Synthetic Faces." In Handbook of Digital Face Manipulation and Detection, 191–212. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-87664-7_9.
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AbstractIn recent years there have been astonishing advances in AI-based synthetic media generation. Thanks to deep learning methods it is now possible to generate visual data with a high level of realism. This is especially true for human faces. Advanced deep learning tools allow one to easily change some specific attributes of a real face or even create brand new identities. Although this opens up a large number of new opportunities, just think of the entertainment industry, it also undermines the trustworthiness of media content and supports the spread of fake identities over the internet. In this context, there is a fundamental need to develop robust and automatic tools capable of distinguishing synthetic faces from real ones. The scientific community is making a huge research effort in this field, proposing several interesting approaches. However, a universal detector is yet to come. Fundamentally, the research in this field is like a cat and mouse game, with new detectors that are designed to deal with powerful synthetic face generators, while the latter keep improving to produce more and more realistic images. In this chapter we will present the most effective techniques proposed in the literature for the detection of synthetic faces. We will analyze their rationale, present real-world application scenarios , and compare different approaches in terms of accuracy and generalization ability.
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Danbury, Annie, Maria Palazzo, Kathleen Mortimer, and Alfonso Siano. "Cultural Perspectives on Advertising Perceptions and Brand Trustworthiness." In Brand Culture and Identity, 1524–43. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-7116-2.ch082.
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The creation of a trustworthy brand identity through advertising has received relatively little attention in European marketing research. Thus, in this chapter, we explore this relationship by undertaking focus group research in the UK and Italy to identify the characteristics of print advertisements perceived as portraying a trustworthy image. The results show that advertisements that are simple, straightforward and clear are perceived as being more trustworthy. However, findings in this chapter also show some differences between our national samples in relation to factors such as colour perception and consumer ethnocentrism. Young consumers are also quite critical of current advertising efforts in building a trustworthy brand image. A discussion of potential solutions and future research directions is provided.
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Danbury, Annie, Maria Palazzo, Kathleen Mortimer, and Alfonso Siano. "Cultural Perspectives on Advertising Perceptions and Brand Trustworthiness." In Analyzing the Cultural Diversity of Consumers in the Global Marketplace, 207–26. IGI Global, 2015. http://dx.doi.org/10.4018/978-1-4666-8262-7.ch010.
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The creation of a trustworthy brand identity through advertising has received relatively little attention in European marketing research. Thus, in this chapter, we explore this relationship by undertaking focus group research in the UK and Italy to identify the characteristics of print advertisements perceived as portraying a trustworthy image. The results show that advertisements that are simple, straightforward and clear are perceived as being more trustworthy. However, findings in this chapter also show some differences between our national samples in relation to factors such as colour perception and consumer ethnocentrism. Young consumers are also quite critical of current advertising efforts in building a trustworthy brand image. A discussion of potential solutions and future research directions is provided.
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Alberola Amores, Leonor, Susana Miquel Segarra, Irene García Medina, and Zahaira Fabiola González Romo. "Influence of Celebrity Endorsement on Mature Female Luxury Cosmetic Consumers." In Advances in Marketing, Customer Relationship Management, and E-Services, 274–95. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-5882-9.ch015.
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A ‘celebrity endorser' is any individual easily recognisable by the general public who leverages this visibility and goodwill to either appear alongside the product in an ad or endorse the product. This helps cosmetics brands to architect a strong brand image in the eyes of end users, a result due in large to the transference of the endorser's trustworthiness to the brand she/he backs. The study revealed that mature female consumers were more likely to relate to an ad featuring celebrities of similar age as themselves and who are actual users of the product rather than armchair or hands-off endorsers.
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Ferraz, Sofia, Rute Almeida, Pedro Vieira-Marques, and Nuno Escudeiro. "Use of the Smartphone Camera to Monitor Adherence to Inhaled Therapy." In ATHENA Research Book, Volume 1, 313–19. University of Maribor Press, 2022. http://dx.doi.org/10.18690/um.3.2022.26.
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Adherence to inhaled controller medications is crucial for patients with chronic respiratory illnesses to achieve favorable clinical outcomes. Selfmanagement measures have been shown to enhance health outcomes, decrease unnecessary interventions, and improve disease control. However, compliance evaluations have difficulties in establishing a high level of trustworthiness, as patient’s self-reported high compliance rates are frequently regarded unreliable. A mobile application module to objectively verify inhalation usage using image snapshots of the inhalation counter and optic character recognition has shown to be promising, but insufficient for some inhaler models. In this paper a model specific approach was explored to enable reliable adherence measurement. To achieve this, a machine learning model was trained on an inhaler image dataset. The trained model had an average accuracy of 88% in recognizing the digits on the dose counter of an inhaler model. These results show the potential to gain additional evidence for inhaler compliance.
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Pantti, Mervi, and Kari Andén-Papadopoulos. "Transparency and Trustworthiness:." In Amateur Images and Global News, 97–112. Intellect Books, 2014. http://dx.doi.org/10.2307/j.ctv36xvqsz.8.
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Lu, Gehao, and Joan Lu. "Introduction to the Investigating in Neural Trust and Multi Agent Systems." In Examining Information Retrieval and Image Processing Paradigms in Multidisciplinary Contexts, 269–73. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-1884-6.ch015.
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Introducing trust and reputation into multi-agent systems can significantly improve the quality and efficiency of the systems. The computational trust and reputation also creates an environment of survival of the fittest to help agents recognize and eliminate malevolent agents in the virtual society. The research redefines the computational trust and analyzes its features from different aspects. A systematic model called Neural Trust Model for Multi-agent Systems is proposed to support trust learning, trust estimating, reputation generation, and reputation propagation. In this model, the research innovates the traditional Self Organizing Map (SOM) and creates a SOM based Trust Learning (STL) algorithm and SOM based Trust Estimation (STE) algorithm. The STL algorithm solves the problem of learning trust from agents' past interactions and the STE solve the problem of estimating the trustworthiness with the help of the previous patterns. The research also proposes a multi-agent reputation mechanism for generating and propagating the reputations. The mechanism exploits the patterns learned from STL algorithm and generates the reputation of the specific agent. Three propagation methods are also designed as part of the mechanism to guide path selection of the reputation. For evaluation, the research designs and implements a test bed to evaluate the model in a simulated electronic commerce scenario. The proposed model is compared with a traditional arithmetic based trust model and it is also compared to itself in situations where there is no reputation mechanism. The results state that the model can significantly improve the quality and efficacy of the test bed based scenario. Some design considerations and rationale behind the algorithms are also discussed based on the results.
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Parhi, Arpit Kumar, and Pooja Misra. "Factors Affecting Customer Loyalty in NCR." In Start-Up Enterprises and Contemporary Innovation Strategies in the Global Marketplace, 265–75. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-4831-7.ch020.
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In India, the retail industry is rapidly growing and the US$ 600 Billion industry is all set to touch the US$ 1 Trillion mark by 2020. With rising disposable income levels, Indian consumers are getting sophisticated day by day and gone are the days when customers are dependent on a handful of brands. All the retailers are trying to woo customers by making their products available according to the convenience of customers and gain a share of the customer's wallet. The availability of many brands is making the customers try different brand which is decreasing customer loyalty. Loyal customers are always helpful for a retailer as the life time value of loyal customers is much higher than that of disloyal customers. The research study aims to understand and analyse the factors affecting customer loyalty in NCR. The research studies four major factors which impact customer loyalty, i.e., customer service of the retailer, brand image of the retailer, trustworthiness of the retailer and special benefits provided by the retailer to the loyal customers.
Conference papers on the topic "Image trustworthiness"
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Sander, Jörg, Bob D. de Vos, Jelmer M. Wolterink, and Ivana Išgum. "Towards increased trustworthiness of deep learning segmentation methods on cardiac MRI." In Image Processing, edited by Elsa D. Angelini and Bennett A. Landman. SPIE, 2019. http://dx.doi.org/10.1117/12.2511699.
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Hu, Donghui, Lina Wang, Yu Zhou, Yang Zhou, Xiaqiu Jiang, and Longfei Ma. "D-S Evidence Theory Based Digital Image Trustworthiness Evaluation Model." In 2009 International Conference on Multimedia Information Networking and Security. IEEE, 2009. http://dx.doi.org/10.1109/mines.2009.154.
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Islam, Mohammad Manzurul, Gour Karmakar, Joarder Kamruzzaman, and Manzur Murshed. "Measuring Trustworthiness of IoT Image Sensor Data Using Other Sensors’ Complementary Multimodal Data." In 2019 18th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/13th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE). IEEE, 2019. http://dx.doi.org/10.1109/trustcom/bigdatase.2019.00112.
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CHAN, Fanny F. Y., Sunny H. F. CHAN, and Felix TANG. "LEARNING-ORIENTATION AND TRUSTWORTHINESS: TWO KEY COMPONENTS OF CORPORATE IMAGE IN THE ASIAN RETAIL CONTEXT." In Bridging Asia and the World: Globalization of Marketing & Management Theory and Practice. Global Alliance of Marketing & Management Associations, 2014. http://dx.doi.org/10.15444/gmc2014.04.04.02.
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Olmedilla Fernández, María, José Carlos Romero, Rocío Martínez-Torres, and Sergio Toral. "Applying NLP techniques to characterize what makes an online review trustworthy." In CARMA 2022 - 4th International Conference on Advanced Research Methods and Analytics. valencia: Universitat Politècnica de València, 2022. http://dx.doi.org/10.4995/carma2022.2022.15085.
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Users spend a significantly amount of time reading and exchanging reviews online in e‑commerce and eWOM communities that help them with their purchase decisions. Source credibility theory is gaining more importance as some online reviews are currently being damaged by those fake reviews that promote an untruthful image not only of the products but also of those online websites. Thus, trustworthiness of online reviews is a key aspect not only for the users that want to make more informed decisions regarding the products, but also for the websites whose credibility might be affected. In this regard, this study proposes a classification system using two Natural Language Processing (NLP) models that can predict trustworthy online reviews (helpful and truthful) applied to the product category “Cell phones & accessories” of Amazon. After using a keyword extractor among those trustworthy online reviews we can characterize their most important features. The results reveal that those features are related to brands, physical and technical features and the UX of the mobile phones.
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Asadizanjani, Navid, Sina Shahbazmohamadi, Mark Tehranipoor, and Domenic Forte. "Non-Destructive PCB Reverse Engineering Using X-Ray Micro Computed Tomography." In ISTFA 2015. ASM International, 2015. http://dx.doi.org/10.31399/asm.cp.istfa2015p0164.
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Abstract Reverse engineering of electronics systems is performed for various reasons ranging from honest ones such as failure analysis, fault isolation, trustworthiness verification, obsolescence management, etc. to dishonest ones such as cloning, counterfeiting, identification of vulnerabilities, development of attacks, etc. Regardless of the goal, it is imperative that the research community understands the requirements, complexities, and limitations of reverse engineering. Until recently, the reverse engineering was considered as destructive, time consuming, and prohibitively expensive, thereby restricting its application to a few remote cases. However, the advents of advanced characterization and imaging tools and software have counteracted this point of view. In this paper, we show how X-ray micro-tomography imaging can be combined with advanced 3D image processing and analysis to facilitate the automation of reverse engineering, and thereby lowering the associated time and cost. In this paper, we demonstrate our proposed process on two different printed circuit boards (PCBs). The first PCB is a four-layer custom designed board while the latter is a more complex commercial system. Lessons learned from this effort can be used to both develop advanced countermeasures and establish a more efficient workflow for instances where reverse engineering is deemed necessary. Keywords: Printed circuit boards, non-destructive imaging, X-ray tomography, reverse engineering.
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Diethei, Daniel, Ashley Colley, Lisa Dannenberg, Muhammad Fawad Jawaid Malik, and Johannes Schoning. "The Usability and Trustworthiness of Medical Eye Images." In 2021 IEEE 9th International Conference on Healthcare Informatics (ICHI). IEEE, 2021. http://dx.doi.org/10.1109/ichi52183.2021.00065.
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Shibina, T., and S. S. Lijina. "Trust only on what we see: Ensure trustworthiness of images on vehicular cloud." In 2017 International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT). IEEE, 2017. http://dx.doi.org/10.1109/icicict1.2017.8342824.
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He, Yang, Ning Yu, Margret Keuper, and Mario Fritz. "Beyond the Spectrum: Detecting Deepfakes via Re-Synthesis." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/349.
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The rapid advances in deep generative models over the past years have led to highly realistic media, known as deepfakes, that are commonly indistinguishable from real to human eyes. These advances make assessing the authenticity of visual data increasingly difficult and pose a misinformation threat to the trustworthiness of visual content in general. Although recent work has shown strong detection accuracy of such deepfakes, the success largely relies on identifying frequency artifacts in the generated images, which will not yield a sustainable detection approach as generative models continue evolving and closing the gap to real images. In order to overcome this issue, we propose a novel fake detection that is designed to re-synthesize testing images and extract visual cues for detection. The re-synthesis procedure is flexible, allowing us to incorporate a series of visual tasks - we adopt super-resolution, denoising and colorization as the re-synthesis. We demonstrate the improved effectiveness, cross-GAN generalization, and robustness against perturbations of our approach in a variety of detection scenarios involving multiple generators over CelebA-HQ, FFHQ, and LSUN datasets. Source code is available at https://github.com/SSAW14/BeyondtheSpectrum.