David Rockefeller addressed a Trilateral Commission meeting in 1991 with these words:
We are grateful to The Washington Post, The New York Times, Time Magazine, and other great publications, whose directors have attended our meetings and respected their promises of discretion for almost forty years. It would have been impossible for us to develop our plan for the world if we had been subject to the bright lights of publicity during those years.
(Kent 2005, p. 66)
Kent, Deirdre. 2005. Healthy Money Healthy Planet: Developing Sustainability Through New
Money Systems. Nelson, New Zealand: Craig Potton
See also
Smith, J., Karides, M., Becker, M., Brunelle, D., Chase-Dunn, C., & Della Porta, D.. (2015). Global Democracy and the World Social Forums. Global Democracy and the World Social Forums, 2nd Edition. Routledge
“The world social forum quickly became the largest political gathering in human history and continues to offer a direct challenge to the extreme inequities of corporate-led globalisation. it has expanded its presence and continues to be an exciting experiment in global and participatory democracy. the book’s contributors have participated in world social forums around the globe. recounting dozens of dramatic firsthand experiences, they draw on their knowledge of global politics to introduce the process, its foundations and relevance to ongoing transnational efforts toward democracy. this second edition of global democracy shows how the forums have developed since their inception in 2001 and how they are now connected with other global movements including occupy, the arab spring and beyond.”
Guzman-Concha, C.. (2012). Jackie Smith, Social Movements for Global Democracy. International Sociology, 27(5), 661–664.
N° 2065 – Enregistré à la Présidence de l’Assemblée nationale le 26 juin 2014.
PROPOSITION DE LOI MODIFIÉE PAR LE SÉNAT, relative à la sobriété, à la transparence, à l’information et à la concertation en matière d’exposition aux ondes électromagnétiques,
The World Health Organisation (WHO) has confirmed that radiofrequency electromagnetic fields can be carcinogenic to humans (Group 2B). Wireless phone use has been linked to an increased risk for brain cancer. The National Agency Health Safety of Food, Environment and Labour (ANSES) has also recommended to limit exposure of the population to radiofrequencies – especially from mobile phones – and especially for children and heavy users.
What is even more worrying is that exposure to such radiations isn’t limited to Wi-Fi but every other gadget that you and your child love so much – cell phones, cordless phones, wireless laptops, routers, electronic devices…even electrical wiring, smart meters and phone towers! Taking a strong, proactive step to minimise damage, the French National Assembly in January 29, 2015 passed a national law to reduce exposure to wireless radiation and electromagnetic fields. While Wi-Fi and wireless devices have been completely banned in nurseries and daycare centres, their use has also been severely restricted in schools for children up to 11 years.
In fact, after this disturbing finding, even India has started taking emergency steps. The Rajasthan High Court, for instance, has directed telecom service providers to remove towers that are near schools, hospitals and play grounds. Such actions are, as PRIARTEM, France’s association for the regulation of mobile phone base stations, points out – “A first step in the legal recognition of the need to regulate the development of mobile phone communications and all wireless applications. This legislative effort must be an encouragement to go further in protecting people.”
So far, most of the possible practical applications for graphene exist only in our fantasies. A great deal of interest has been spurred by graphene’s conducting ability. Thus graphene transistors are predicted to be substantially faster than those made out of silicon today. Maybe we are on the verge of yet another miniaturization of electronics that will lead to computers becoming even more efficient in the future.
Graphene
Since graphene is practically transparent (up to nearly 98%) whilst being able to conduct electricity, it would be suitable for the production of transparent touch screens, light panels and maybe solar cells. Also plastics could be made into electronic conductors if only 1% of graphene were mixed into them. Likewise, by mixing in just a fraction of a per mille of graphene, the heat resistance of plastics would increase by 30˚ C while at the same time making them more mechanically robust. This resilience could be utilised in new super strong materials, which are also thin, elastic and lightweight.
The perfect structure of graphene also makes it suitable for the production of extremely sensitive sensors that could register pollution at molecular level.
Review: Re-Education in Post-War Germany Reviewed Work: Neuordnung oder Restauration? Das Demokratiekonzept der amerikanischen Besatzungsmacht und die politsche Sozialisation der Westdeutschen: Wirtschaftsordnung. Schulstruktur. Politsche Bildung by Jutta-B. Lange-Quassowski Review by: Konrad Jaraush History of Education Quarterly Vol. 22, No. 3, Special Issue: Educational Policy and Reform in Modern Germany (Autumn, 1982), pp. 387-390 (4 pages) Published By: Cambridge University Press History of Education Quarterly doi.org/10.2307/367777 www.jstor.org/stable/367777
Kreis, R.. (2018). From reeducation to partnership: Amerikahäuser and German-American institutes in Bavaria. In German-American Encounters in Bavaria and Beyond, 1945-2015
Show/hide publication abstract
“The amerikahäuser were probably the most important instrument of u.s. cultural diplomacy in the federal republic. this article traces the foundation of ‘america houses’ in several cities of the u.s. occupation zone and how they shaped german-american relations between occupation and partnership on the local, regional, and federal level.”
Anderton, A.. (2016). Hearing democracy in the ruins of Hitler’s Reich: American musicians in postwar Germany. Comparative Critical Studies
“During his 1947 visit to berlin, american pianist webster aitken was shocked to find the kroll opera reduced to ‘tangles of twisted girders, resembling empty bird cages. beyond the brandenburger tor, the blocks seem to be made of brown sugar that has gone hard in lumps and streaks’.1aitken was one of dozens of artists invited by the american military government to concertize throughout postwar germany to demonstrate the strength of american musical achievement. between 1945 and 1949, american musicians visited the ruins of the third reich to perform for german audiences, and this article explores the efficacy their postwar concerts had for the reeducation programme. american cultural officers believed music could play a redemptive role in the service of democracy to promote racial and religious tolerance among german audiences.”
Druffner, F.. (2014). Education is reeducation: Peter suhrkamp’s programmatic work in cooperation with the military government in Germany. Germanic Review
“Peter suhrkamp, who in 1950 separated from his former publishing partner gottfried bermann fischer and founded his own company, was considered an apt collaborator for the british allies in their reeducation efforts. still a partner in the s. fischer verlag, he had stayed in germany during the nazi regime and protected his authors against official attacks, while bermann fischer published the ‘un-german’ émigré writers in exile. imprisoned on charges of treason in 1944, suhrkamp was not released from concentration camp until february 1945. the first german publisher to receive a license from the british military government, suhrkamp developed a pedagogical publishing program for postwar germany. in 1948, he was invited by the british to prepare german pows in english camps for their return to germany.”
Wegner, G. P., & Füssl, K.. (1997). Wissenschaft als säkularer Kreuzzug: Thomas V. Smith und die Deutschen Kriegsgefangenen in den USA (1944–1946). International Journal of Phytoremediation
“Can a population or community be ‘taught’ to feel and act differently through an externally imposed politico-cultural paradigm? what kinds of unexpected feelings (at odds with behavioral norms and expectations of the educator/observer) emerge from the collision of different political histories and cultural orientations? this paper examines american and west german social theories concerned with democratizing west germany in the context of the cold war and in the wake of initial us-allied attempts at ‘reeducation’ in the postwar period. based on an analysis of theodor w. adorno’s radio broadcasts and writings on the possibility of an ‘education to autonomy’ after auschwitz, this paper explores how west germans came to ‘feel differently’ through the gradual and contradictory negotiation of a democratic ‘habitus’, ultimately demonstrating the agonistic and ambivalent processes constitutive of substantial democracy.”
Shuster, G. N.. (1949). German Reeducation: Success or Failure. Proceedings of the Academy of Political Science, 23(3), 12.
Weiner, D. R. P.. (2020). American and british efforts to democratize schoolbooks in occupied Italy and Germany from 1943 to 1949. Journal of Educational Media, Memory, and Society
“During the allied occupation of the axis countries, education and the revision of educational materials were seen as a means of ensuring future peace in europe. most scholarly literature on this topic has focused on the german case or has engaged in a german-japanese comparison, neglecting the country in which the textbook revision process was first pioneered: italy. drawing primarily on the papers of the allied occupying military governments, this article explores the parallels between the textbook revision processes in allied-occupied italy and germany. it argues that, for the allied occupiers involved in reeducation in italy and germany, the reeducation processes in these countries were inextricably linked. furthermore, the institutional learning process that occurred in occupied italy enabled the more thorough approach later applied in germany.”
Inoculation theory
“McGuire’s (Papageorgis & McGuire, 1961, 1961a; 1961b) original conceptualization
of inoculation theory proposed that individuals can be inoculated against counter-
attitudinal attacks in a manner similar to immunization against viral attacks.
Just as preemptive immunization shots protect people from future exposure to viruses,
McGuire posited that preemptive messages could protect attitudes from subsequent
exposure to counterattitudinal persuasive messages. Inoculation treatments contain
two essential message features: threat and refutational preemption (Compton & Pfau,
2005; Szabo & Pfau, 2002). Threat is the motivational component of an inoculation.
It forewarns of a persuasive attack, highlighting the vulnerability of an individual’s
current attitudes, and thereby motivates resistance. The refutational preemption
component contains specific content that can be used to bolster attitudes against
an impending attack (Pfau et al., 1997). The purpose of the refutational component
is twofold: It provides individuals with arguments or evidence that can be used to
counter persuasive attacks, and it also allows individuals to practice defending their
beliefs through counterarguing (Compton & Pfau, 2005; Insko, 1967; Wyer, 1974).
Research reveals inoculation to be an effective strategy for conferring resistance
to persuasion. … Conspiracy theories present an interesting challenge for inoculation scholars
because they defy the rational, logical, and reasoned approach exemplified by
inoculation interventions. Conspiratorial arguments often employ circular reasoning,
repetition of unproven premises, nonfalsifiable premises, and a host of other logical
flaws (Miller, 2002). Persuasion is not a purely rational process, however, and dual-
process theories (e.g., Petty & Cacioppo, 1986) apply the metaphor of two separate
routes to persuasion: A central route, based on careful processing of the evidence;
and a peripheral route, based on some mental shortcut instead of careful evaluation
of arguments and evidence. These theories propose that both motivation and ability
to process persuasive messages are necessary for central route processing to occur.
Watching a film is a more passive process than reading, which should reduce the
ability to counterargue or process many of the empirical claims presented (Compton
& Pfau, 2005).”
Abstract
This investigation examined the boundaries of inoculation theory by examining how inoculation can be applied to conspiracy theory propaganda as well as inoculation itself (called metainoculation). A 3-phase experiment with 312 participants compared 3 main groups: no-treatment control, inoculation, and metainoculation. Research questions explored how inoculation and metainoculation effects differ based on the argument structure of inoculation messages (fact- vs. logic-based). The attack message was a 40-minute chapter from the 9/11 Truth conspiracy theory film, Loose Change: Final Cut. The results indicated that both the inoculation treatments induced more resistance than the control message, with the fact-based treatment being the most effective. The results also revealed that metainoculation treatments reduced the efficacy of the inoculation treatments.
Banas, J. A., & Miller, G.. (2013). Inducing Resistance to Conspiracy Theory Propaganda: Testing Inoculation and Metainoculation Strategies. Human Communication Research, 39(2), 184–207.
van der Linden, S., Leiserowitz, A., Rosenthal, S., & Maibach, E.. (2017). Inoculating the Public against Misinformation about Climate Change. Global Challenges, 1(2), 1600008.
“Effectively addressing climate change requires significant changes in individual and collective human behavior and decision-making. yet, in light of the increasing politicization of (climate) science, and the attempts of vested-interest groups to undermine the scientific consensus on climate change through organized ‘disinformation campaigns,’ identifying ways to effectively engage with the public about the issue across the political spectrum has proven difficult. a growing body of research suggests that one promising way to counteract the politicization of science is to convey the high level of normative agreement (‘consensus’) among experts about the reality of human-caused climate change. yet, much prior research examining public opinion dynamics in the context of climate change has done so under conditions with limited external validity. moreover, no research to date has examined how to protect the public from the spread of influential misinformation about climate change. the current research bridges this divide by exploring how people evaluate and process consensus cues in a polarized information environment. furthermore, evidence is provided that it is possible to pre-emptively protect (‘inoculate’) public attitudes about climate change against real-world misinformation.”
Maertens, R., Anseel, F., & van der Linden, S.. (2020). Combatting climate change misinformation: Evidence for longevity of inoculation and consensus messaging effects. Journal of Environmental Psychology
“Despite the fact that there is a 97% consensus among climate scientists that humans are causing global warming, the spread of misinformation continues to undermine public support for climate action. previous studies have found that resistance to misinformation can be induced by cognitively inoculating individuals against doubt-sowing about climate change. however, the long-term effectiveness of this approach is currently unknown. in a preregistered replication and extension experiment we combined a scientific consensus message with an inoculation treatment, and exposed participants to an influential misinformation message one week later. we explored 1) whether we can replicate the finding that inoculation is able to protect against a misinformation attack, and 2) whether or not the consensus and inoculation effects remain stable over the course of one week. successfully replicating the effects of the original study, we found a strong initial consensus effect that is sensitive to doubt-sowing misinformation. importantly, we also found that the consensus effect can be inoculated against misinformation. extending the replication, we found that the consensus effect shows partial decay over time, while the inoculation effect remains stable for at least one week. we discuss the impact of our findings for inoculation theory, climate change psychology, and public policy.”
“…people who value social conformity… support the government when it wants to increase its control over social behavior and punish nonconformity…valuing social conformity increases the motivation for placing restrictions on behavior…the desire for social freedom is now subservient to the enforcement of social norms and rules. Thus, groups will be targeted for repression to the extent that they challenge social conformity…
~ Stanley Feldman, Enforcing Social Conformity: A Theory of Authoritarianism
Abstract
Fifty years after the publication of The Authoritarian Personality, the empirical literature on authoritarianism continues to grow even though there is no widely accepted theory to account for the phenomenon. The absence of a secure theoretical grounding severely limits our understanding of authoritarianism. This paper offers a new conceptualization in which authoritarian predispositions originate in the conflict between the values of social conformity and personal autonomy. Prejudice and intolerance should be observed among those who value social conformity and perceive a threat to social cohesion. These hypotheses were tested with a sample of undergraduate students; the questionnaire included new measures of the dimension of social conformity–autonomy as well as items from Altemeyer’s RWA (right–wing authoritarianism) scale.
“We are fast approaching the stage of the ultimate inversion: the stage where the government is free to do anything it pleases, while the citizens may act only by permission; which is the stage of the darkest periods of human history, the stage of rule by brute force.”
~ Ayn Rand, Capitalism: The Unknown Ideal
Feldman, S.. (2003). Enforcing Social Conformity: A Theory of Authoritarianism. Political Psychology
“Fifty years after the publication of the authoritarian personality, the empirical literature on authoritarianism continues to grow even though there is no widely accepted theory to account for the phenomenon. the absence of a secure theoretical grounding severely limits our understanding of authoritarianism. this paper offers a new conceptualization in which authoritarian predispositions originate in the conflict between the values of social conformity and personal autonomy. prejudice and intolerance should be observed among those who value social conformity and perceive a threat to social cohesion. these hypotheses were tested with a sample of undergraduate students; the questionnaire included new measures of the dimension of social conformity-autonomy as well as items from altemeyer’s rwa (right-wing authoritarianism) scale.”
Passini, S.. (2022). Songs and flags: Concern for Covid-19 and submission to authority. Personality and Individual Differences
“The literature on authoritarianism has shown that the perception of threat and social insecurity is connected to attitudes of submission to authority and a greater acceptance of freedom restraints. in the present research, the relationship between concerns for covid-19 – measured in terms of the fear of personal and close to others exposure to covid-19 – on authoritarianism was analysed in italy while considering participants’ basic values as a potential mediator. results on 406 participants show high mean values on concerns for covid-19 experienced during the lockdown phase. as hypothesized, such concerns are positively related to authoritarianism, and a mediation analysis showed that the link between these two variables can be explained by the relevance attributed to conservation values.”
“The Internet of Bodies (IoBs) is an imminent extension to the vast Internet of Things domain, where interconnected devices (e.g., worn, implanted, embedded, swallowed, etc.) are located in-on-and-around the human body form a network. Thus, the IoB can enable a myriad of services and applications for a wide range of sectors, including medicine, safety, security, wellness, entertainment, to name but a few. Especially, considering the recent health and economic crisis caused by the novel coronavirus pandemic, also known as COVID-19, the IoB can revolutionize today’s public health and safety infrastructure. Nonetheless, reaping the full benefit of IoB is still subject to addressing related risks, concerns, and challenges. Hence, this survey first outlines the IoB requirements and related communication and networking standards. Considering the lossy and heterogeneous dielectric properties of the human body, one of the major technical challenges is characterizing the behavior of the communication links in-on-and-around the human body. Therefore, this article presents a systematic survey of channel modeling issues for various link types of human body communication (HBC) channels below 100 MHz, the narrowband (NB) channels between 400 and 2.5 GHz, and ultrawideband (UWB) channels from 3 to 10 GHz. After explaining bio-electromagnetics attributes of the human body, physical, and numerical body phantoms are presented along with electromagnetic propagation tool models. Then, the first-order and the second-order channel statistics for NB and UWB channels are covered with a special emphasis on body posture, mobility, and antenna effects. For capacitively, galvanically, and magnetically coupled HBC channels, four different channel modeling methods (i.e., analytical, numerical, circuit, and empirical) are investigated, and electrode effects are discussed. Finally, interested readers are provided with open research challenges and potential future research directions.”
Celik, A., Salama, K. N., & Eltawil, A. M.. (2022). The Internet of Bodies: A Systematic Survey on Propagation Characterization and Channel Modeling. IEEE Internet of Things Journal
“The internet of bodies (iobs) is an imminent extension to the vast internet of things domain, where interconnected devices (e.g., worn, implanted, embedded, swallowed, etc.) are located in-on-and-around the human body form a network. thus, the iob can enable a myriad of services and applications for a wide range of sectors, including medicine, safety, security, wellness, entertainment, to name but a few. especially, considering the recent health and economic crisis caused by the novel coronavirus pandemic, also known as covid-19, the iob can revolutionize today’s public health and safety infrastructure. nonetheless, reaping the full benefit of iob is still subject to addressing related risks, concerns, and challenges. hence, this survey first outlines the iob requirements and related communication and networking standards. considering the lossy and heterogeneous dielectric properties of the human body, one of the major technical challenges is characterizing the behavior of the communication links in-on-and-around the human body. therefore, this article presents a systematic survey of channel modeling issues for various link types of human body communication (hbc) channels below 100 mhz, the narrowband (nb) channels between 400 and 2.5 ghz, and ultrawideband (uwb) channels from 3 to 10 ghz. after explaining bio-electromagnetics attributes of the human body, physical, and numerical body phantoms are presented along with electromagnetic propagation tool models. then, the first-order and the second-order channel statistics for nb and uwb channels are covered with a special emphasis on body posture, mobility, and antenna effects. for capacitively, galvanically, and magnetically coupled hbc channels, four different channel modeling methods (i.e., analytical, numerical, circuit, and empirical) are investigated, and electrode effects are discussed. finally, interested readers are provided with open research challenges and potential future research directions.”
Lee, M., Boudreaux, B., Chaturvedi, R., Romanosky, S., & Downing, B.. (2020). The Internet of Bodies: Opportunities, Risks, and Governance. The Internet of Bodies: Opportunities, Risks, and Governance
“The work described in this report was conducted as part of a fellowship awarded by the rand corporation’s center for global risk and security. this report describes emerging technologies, herein referred to as the internet of bodies; analyzes their benefits and risks; and suggests ways various stakeholders can balance those benefits and risks. this report should be of interest to the general public, internet of bodies and medical device makers, health-care providers, and policy decisionmakers. the research was conducted within the center for global risk and security between february 2019 and september 2019.”
Makitalo, N., Flores-Martin, D., Berrocal, J., Garcia-Alonso, J., Ihantola, P., Ometov, A., … Mikkonen, T.. (2020). The Internet of Bodies Needs a Human Data Model. IEEE Internet Computing
“Today, creating innovative internet of bodies solutions requires manually gathering the needed information from an increasing number of services and personal devices. in this article, we tackle this challenge by presenting human data model-a programming framework for combining information from several sources, performing computations over that information to high-level abstractions, and then providing these abstractions to proactively schedule computer-human interactions.”
Boddington, G.. (2021). The Internet of Bodies—alive, connected and collective: the virtual physical future of our bodies and our senses. AI and Society
“This paper is going to discuss, what will be called, ‘the internet of bodies’. our physical and virtual worlds are blending and shifting our understanding of three key areas: (1) our identities are diversifying, as they become hyper-enhanced and multi-sensory; (2) our collaborations are co-created, immersive and connected; (3) our innovations are diverse and inclusive. it is proposed that our bodies have finally become the interface.”
Blake, M. B., Kandasamy, N., Dustdar, S., & Liu, X.. (2020). Internet of Bodies/Internet of Sports. IEEE Internet Computing
“As healthcare solutions and augmented monitoring of human mobility overlap with the new concepts of the internet of things, an emerging area of interest leverages sensor networks that monitor personal health data and human activity. this special issue presents research innovation that address advances in this evolving paradigm of internet of bodies/internet of sports.”
Ray, P. P.. (2020). Intelligent Ingestibles: Future of Internet of Body. IEEE Internet Computing
“In this article, we first provide the basics of ingestibles. then, we provide a detailed survey on ingestibles as per their applicability in leveraging gastrointestinal disease detection, management, and treatment. next, we show how ingestibles could be related with the concept of internet of body. lastly, we discuss various key challenges and future directions to mitigate these issues.”
El-Khoury, M., & Arikan, C. L.. (2021). From the internet of things toward the internet of bodies: Ethical and legal considerations. Strategic Change
“The proliferation of the internet of things makes the gray area of ethics darker and lighter simultaneously, and the law is currently not construed to accompany the steady progression toward the internet of bodies. the internet of things is challenging the traditional construct of ownership, and users are progressively losing control over their iot devices. the internet of bodies is the awaiting new normal where human bodies and minds form a connected network pervaded by the internet. the integrity of human bodies will rely more and more on the internet. in this context, the future calls for a balance between divergent interests of appealing technological progress and vital human safety.”
Peng, B., Fan, H., Wang, W., Dong, J., & Lyu, S.. (2021). A Unified Framework for High Fidelity Face Swap and Expression Reenactment. IEEE Transactions on Circuits and Systems for Video Technology
“Face manipulation techniques improve fast with the development of powerful image generation models. two particular face manipulation methods, namely face swap and expression reenactment attract much attention for their flexibility and ease to generate high quality synthesis results. recently, these two subjects are actively studied. however, most existing methods treat the two tasks separately, ignoring their underlying similarity. in this paper, we propose to tackle the two problems within a unified framework that achieves high quality synthesis results. the enabling component for our unified framework is the clean disentanglement of 3d pose, shape, and expression factors and then recombining them for different tasks accordingly. we then use the same set of 2d representations for face swap and expression reenactment tasks that are input to a common image translation model to directly generate the final synthetic images. once trained, the proposed model can accomplish both face swap and expression reenactment tasks for previously unseen subjects. comprehensive experiments and comparisons show that the proposed method achieves high fidelity results in multiple aspects, and it is especially good at faithfully preserving source facial shape in the face swap task, and accurately transferring facial movements in the expression reenactment task.”
Zhang, W., Zhao, C., & Li, Y.. (2020). A novel counterfeit feature extraction technique for exposing face-swap images based on deep learning and error level analysis. Entropy
“The quality and efficiency of generating face-swap images have been markedly strengthened by deep learning. for instance, the face-swap manipulations by deepfake are so real that it is tricky to distinguish authenticity through automatic or manual detection. to augment the efficiency of distinguishing face-swap images generated by deepfake from real facial ones, a novel counterfeit feature extraction technique was developed based on deep learning and error level analysis (ela). it is related to entropy and information theory such as cross-entropy loss function in the final softmax layer. the deepfake algorithm is only able to generate limited resolutions. therefore, this algorithm results in two different image compression ratios between the fake face area as the foreground and the original area as the background, which would leave distinctive counterfeit traces. through the ela method, we can detect whether there are different image compression ratios. convolution neural network (cnn), one of the representative technologies of deep learning, can extract the counterfeit feature and detect whether images are fake. experiments show that the training efficiency of the cnn model can be significantly improved by the ela method. in addition, the proposed technique can accurately extract the counterfeit feature, and therefore achieves outperformance in simplicity and efficiency compared with direct detection methods. specifically, without loss of accuracy, the amount of computation can be significantly reduced (where the required floating-point computing power is reduced by more than 90%).”
Korshunova, I., Shi, W., Dambre, J., & Theis, L.. (2017). Fast Face-Swap Using Convolutional Neural Networks. In Proceedings of the IEEE International Conference on Computer Vision
“We consider the problem of face swapping in images, where an input identity is transformed into a target identity while preserving pose, facial expression and lighting. to perform this mapping, we use convolutional neural networks trained to capture the appearance of the target identity from an unstructured collection of his/her photographs. this approach is enabled by framing the face swapping problem in terms of style transfer, where the goal is to render an image in the style of another one. building on recent advances in this area, we devise a new loss function that enables the network to produce highly photorealistic results. by combining neural networks with simple pre- and post-processing steps, we aim at making face swap work in real-time with no input from the user.”
Senses, M., & Topal, C.. (2019). Real time face swap based on patch warping. In 27th Signal Processing and Communications Applications Conference, SIU 2019
“Face swap is one of the popular machine vision problems recently. important problems in real-time face swap are the need for high computation power and the decrease in success of applications in case of face pose variations. in this paper, an original face swap algorithm which can work in real time is proposed. after detecting two faces in the input image, 68 landmark points of each face are localized. using these points, a facial model has been formed which separates the faces into 23 quadrilateral planar regions. homographies are calculated between the quadrilaterals formed by the same points on both sides in order not to disrupt the holistic structure of the selected regions. face swap is performed by warping the face patches with calculated homographies. with the proposed method, the calculation resources used in the algorithm have been used efficiently and identity information such as eye, mustache and eyebrow has been preserved. the symmetries of the visible regions are used for the invisible parts in the viewing angles that the camera cannot see the entire face. the success of our study has been tested with different camera resolutions and faces at different angles and qualitative results are given.”
Mahajan, S., Chen, L. J., & Tsai, T. C.. (2017). SwapItUp: A face swap application for privacy protection. In Proceedings – International Conference on Advanced Information Networking and Applications, AINA
“There is a growing concern over the issues related to online privacy due to large availability of high quality images. to tackle the privacy concerns a face swapping application is proposed. there is a library of face images, which is created by downloading images from different sources on internet. for any given image, first of all facial landmarks are detected. the second image is rotated and scaled so that it can properly fit over the input image. to make sure that the new image looks natural, color balance adjustment is done. after that blending of features from the second image onto the input image is done. it is also shown how this system can be used to creating appealing and funny photographs for entertainment purposes. we conclude with a study that shows the high quality of images produced by this system as compared to existing face swap applications and also limitations of this system.”
Sadu, C., & Das, P. K.. (2020). Swapping face images based on augmented facial landmarks and its detection. In IEEE Region 10 Annual International Conference, Proceedings/TENCON
“Facial landmark points that are precisely extracted from the face images improve the performance of many applications in the domains of computer vision and graphics. face swapping is one of such applications. with the availability of sophisticated image editing tools and the use of deep learning models, it is easy to create swapped face images or face swap attacks in images or videos even for non-professionals. face swapping transfers a face from a source to a destination image, while preserving photo realism. it has potential applications in computer games, privacy protection, etc. however, it could also be used for fraudulent purposes. in this paper, we propose an approach to create face swap attacks and detect them from the original images. the augmented 81-facial landmark points are extracted for creating the face swap attacks. the feature descriptors weighted local magnitude patterns (wlmp) and support vector machines (svm) are utilized for the swapped face images detection. the performance of the proposed approach is demonstrated by different types of svm classifiers on a real-world dataset. experimental results show that the proposed system effectively does face swapping and detection with an accuracy of 95%.”
Zhao, Y., Tang, F., Dong, W., Huang, F., & Zhang, X.. (2019). Joint face alignment and segmentation via deep multi-task learning. Multimedia Tools and Applications
“Face alignment and segmentation are challenging problems which have been extensively studied in the field of multimedia. these two tasks are closely related and their learning processes are supposed to benefit each other. hence, we present a joint multi-task learning algorithm for both face alignment and segmentation using deep convolutional neural network (cnn). the proposed multi-task learning approach allows cnn model to simultaneously share visual knowledge between different tasks. with a carefully designed refinement residual module, the cross-layer features are fused in a collaborative manner. to the best of our knowledge, this is the first time that face alignment and segmentation are learned together via deep multi-task learning. our experiments show that learning these two related tasks simultaneously builds a synergy between them, improves the performance of each individual task, and rivals recent approaches. furthermore, we demonstrate the effectiveness of our model in two practical applications: virtual makeup and face swap.”
Chawla, R.. (2019). Deepfakes: How a pervert shook the world. International Journal of Advance Research and Development
Show/hide publication abstract
“Recently a machine learning based open source software (i.e. a free to use the software) tool has made it easy to create hyper-realistic face swaps in videos that leave little to no traces of manipulation, in what is known as ‘deepfake’ videos. scenarios, where these ai manipulated/generated videos, are used for political distress, blackmail or even terrorism are easily envisioned as a near dystopia. this paper explores the various aspects of deepfake videos including its consequences and newly developed innovations in detecting deepfakes.”
Jiang, J., Li, B., Wei, B., Li, G., Liu, C., Huang, W., … Yu, M.. (2021). FakeFilter: A cross-distribution Deepfake detection system with domain adaptation. Journal of Computer Security
“Abuse of face swap techniques poses serious threats to the integrity and authenticity of digital visual media. more alarmingly, fake images or videos created by deep learning technologies, also known as deepfakes, are more realistic, high-quality, and reveal few tampering traces, which attracts great attention in digital multimedia forensics research. to address those threats imposed by deepfakes, previous work attempted to classify real and fake faces by discriminative visual features, which is subjected to various objective conditions such as the angle or posture of a face. differently, some research devises deep neural networks to discriminate deepfakes at the microscopic-level semantics of images, which achieves promising results. nevertheless, such methods show limited success as encountering unseen deepfakes created with different methods from the training sets. therefore, we propose a novel deepfake detection system, named fakefilter, in which we formulate the challenge of unseen deepfake detection into a problem of cross-distribution data classification, and address the issue with a strategy of domain adaptation. by mapping different distributions of deepfakes into similar features in a certain space, the detection system achieves comparable performance on both seen and unseen deepfakes. further evaluation and comparison results indicate that the challenge has been successfully addressed by fakefilter.”
Mirsky, Y., & Lee, W.. (2021). The Creation and Detection of Deepfakes. ACM Computing Surveys
“Generative deep learning algorithms have progressed to a point where it is difficult to tell the difference between what is real and what is fake. in 2018, it was discovered how easy it is to use this technology for unethical and malicious applications, such as the spread of misinformation, impersonation of political leaders, and the defamation of innocent individuals. since then, these ‘deepfakes’have advanced significantly. in this article, we explore the creation and detection of deepfakes and provide an in-depth view as to how these architectures work. the purpose of this survey is to provide the reader with a deeper understanding of (1) how deepfakes are created and detected, (2) the current trends and advancements in this domain, (3) the shortcomings of the current defense solutions, and (4) the areas that require further research and attention.”
Zhang, W., & Zhao, C.. (2019). Exposing Face-Swap Images Based on Deep Learning and ELA Detection. Proceedings
“New developments in artificial intelligence (ai) have significantly improved the quality and efficiency in generating fake face images; for example, the face manipulations by deepfake are so realistic that it is difficult to distinguish their authenticity—either automatically or by humans. in order to enhance the efficiency of distinguishing facial images generated by ai from real facial images, a novel model has been developed based on deep learning and error level analysis (ela) detection, which is related to entropy and information theory, such as cross-entropy loss function in the final softmax layer, normalized mutual information in image preprocessing, and some applications of an encoder based on information theory. due to the limitations of computing resources and production time, the deepfake algorithm can only generate limited resolutions, resulting in two different image compression ratios between the fake face area as the foreground and the original area as the background, which leaves distinctive artifacts. by using the error level analysis detection method, we can detect the presence or absence of different image compression ratios and then use convolution neural network (cnn) to detect whether the image is fake. experiments show that the training efficiency of the cnn model can be significantly improved by using the ela method. and the detection accuracy rate can reach more than 97% based on cnn architecture of this method. compared to the state-of-the-art models, the proposed model has the advantages such as fewer layers, shorter training time, and higher efficiency.”
Yan, S., He, S., Lei, X., Ye, G., & Xie, Z.. (2018). Video Face Swap Based on Autoencoder Generation Network. In ICALIP 2018 – 6th International Conference on Audio, Language and Image Processing
“Video facial swap usually has strong entertainment applications, and it is also applicable for the post-production of films and has great application value. at present, the popular face swap is done manually by the ps software, and the synthetic effect of the automatic face changing technology is not good. in order to make up for the lack of these features, this paper proposes a method of video face swap based on autoencoder generation network. the network learns the mapping relationship between distorted face and original face: the encoder can distinguish and extract facial information, and the decoder can restore face separately. first, the local information of tow face is sent to the network to get the initial model; then, the global information is put into the network for fine-tuning; finally, the face exchange between a and b is completed with face alignment and alpha fusion. the experimental results show that the quality of the method is improved significantly.”
“Deepfakes are a recent off-the-shelf manipulation technique that allows anyone to swap two identities in a single video. in addition to deepfakes, a variety of gan-based face swapping methods have also been published with accompanying code. to counter this emerging threat, we have constructed an extremely large face swap video dataset to enable the training of detection models, and organized the accompanying deepfake detection challenge (dfdc) kaggle competition. importantly, all recorded subjects agreed to participate in and have their likenesses modified during the construction of the face-swapped dataset. the dfdc dataset is by far the largest currently and publicly available face swap video dataset, with over 100,000 total clips sourced from 3,426 paid actors, produced with several deepfake, gan-based, and non-learned methods. in addition to describing the methods used to construct the dataset, we provide a detailed analysis of the top submissions from the kaggle contest. we show although deepfake detection is extremely difficult and still an unsolved problem, a deepfake detection model trained only on the dfdc can generalize to real ‘in-the-wild’ deepfake videos, and such a model can be a valuable analysis tool when analyzing potentially deepfaked videos. training, validation and testing corpuses can be downloaded from ai.facebook.com/datasets/dfdc.”
Wöhler, L., Henningson, J. O., Castillo, S., & Magnor, M.. (2020). PEFS: A Validated Dataset for Perceptual Experiments on Face Swap Portrait Videos. In Communications in Computer and Information Science
“Videos obtained by current face swapping techniques can contain artifacts potentially detectable, yet unobtrusive to human observers. however, the perceptual differences between real and altered videos, as well as properties leading humans to classify a video as manipulated, are still unclear. thus, to support the research on perceived realism and conveyed emotions in face swap videos, this paper introduces a high-resolution dataset providing the community with the necessary sophisticated stimuli. our recording process has been specifically designed to focus on human perception research and entails three scenarios (text-reading, emotion-triggering, and free-speech). we assess the perceived realness of our dataset through a series of experiments. the results indicate that our stimuli are overall convincing, even for long video sequences. furthermore, we partially annotate the dataset with noticeable facial distortions and artifacts reported by participants.”
Bode, L., Lees, D., & Golding, D.. (2021). The Digital Face and Deepfakes on Screen. Convergence
Cole, S.. (2018). We Are Truly Fucked: Everyone Is Making AI-Generated Fake Porn Now. Motherboard
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“A user-friendly application has resulted in an explosion of convincing face-swap porn.”
Tolosana, R., Vera-Rodriguez, R., Fierrez, J., Morales, A., & Ortega-Garcia, J.. (2020). Deepfakes and beyond: A Survey of face manipulation and fake detection. Information Fusion
“The free access to large-scale public databases, together with the fast progress of deep learning techniques, in particular generative adversarial networks, have led to the generation of very realistic fake content with its corresponding implications towards society in this era of fake news. this survey provides a thorough review of techniques for manipulating face images including deepfake methods, and methods to detect such manipulations. in particular, four types of facial manipulation are reviewed: i) entire face synthesis, ii) identity swap (deepfakes), iii) attribute manipulation, and iv) expression swap. for each manipulation group, we provide details regarding manipulation techniques, existing public databases, and key benchmarks for technology evaluation of fake detection methods, including a summary of results from those evaluations. among all the aspects discussed in the survey, we pay special attention to the latest generation of deepfakes, highlighting its improvements and challenges for fake detection. in addition to the survey information, we also discuss open issues and future trends that should be considered to advance in the field.”
Baek, J. Y., Yoo, Y. S., & Bae, S. H.. (2020). Generative Adversarial Ensemble Learning for Face Forensics. IEEE Access
“The recent advance of synthetic image generation and manipulation methods allows us to generate synthetic face images close to real images. on the other hand, the importance of identifying the synthetic face images increases more and more to protect personal privacy from those. although some deep learning-based image forensic methods have been developed recently, it is still challenging to distinguish synthetic images generated by recent image generation and manipulation methods such as the deep fake, face2face, and face swap. to resolve this challenge, we propose a novel generative adversarial ensemble learning method. we train multiple discriminative and generative networks based on the adversarial learning. compared to the conventional adversarial learning, our method is however more focused on improving the discrimination ability rather than image generation one. to this end, we improve the discriminabilty by ensembling outputs from different two discriminators. in addition, we train two generators in order to generate general and hard synthetic images. by ensemble learning of all the generators and discriminators, we improve the discriminators by using the generated synthetic face images, and improve the generators by passing the combined feedback of the discriminators. on the faceforensics benchmark challenge, we thoroughly evaluate our methods by comparing the recent methods. we also provide the ablation study to prove the effectiveness and usefulness of our method.”
Kaur, S., Kumar, P., & Kumaraguru, P.. (2020). Deepfakes: temporal sequential analysis to detect face-swapped video clips using convolutional long short-term memory. Journal of Electronic Imaging
“Deepfake (a bag of ‘deep learning’ and ‘fake’) is a technique for human image synthesis based on artificial intelligence, i.e., to superimpose the existing (source) images or videos onto destination images or videos using neural networks (nns). deepfake enthusiasts have been using nns to produce convincing face swaps. deepfakes are a type of video or image forgery developed to spread misinformation, invade privacy, and mask the truth using advanced technologies such as trained algorithms, deep learning applications, and artificial intelligence. they have become a nuisance to social media users by publishing fake videos created by fusing a celebrity’s face over an explicit video. the impact of deepfakes is alarming, with politicians, senior corporate officers, and world leaders being targeted by nefarious actors. an approach to detect deepfake videos of politicians using temporal sequential frames is proposed. the proposed approach uses the forged video to extract the frames at the first level followed by a deep depth-based convolutional long short-term memory model to identify the fake frames at the second level. also the proposed model is evaluated on our newly collected ground truth dataset of forged videos using source and destination video frames of famous politicians. experimental results demonstrate the effectiveness of our method.”
Gu, S., Bao, J., Yang, H., Chen, D., Wen, F., & Yuan, L.. (2019). Mask-guided portrait editing with conditional gans. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
“Portrait editing is a popular subject in photo manipulation.the generative adversarial network (gan) advances the generating of realistic faces and allows more face editing. in this paper, we argue about three issues in existing techniques: diversity, quality, and controllability for portrait synthesis and editing. to address these issues, we propose a novel end-to-end learning framework that leverages conditional gans guided by provided face masks for generating faces. the framework learns feature embeddings for every face component (e.g., mouth, hair, eye), separately, contributing to better correspondences for image translation, and local face editing. with the mask, our network is available to many applications, like face synthesis driven by mask, face swap+ (including hair in swapping), and local manipulation. it can also boost the performance of face parsing a bit as an option of data augmentation.”
Wen, L., & Xu, D.. (2019). Face Image Manipulation Detection. In IOP Conference Series: Materials Science and Engineering
“This paper proposes a cnn-based (convolutional neural network based) network to detect altered face picture, which can cover the most common face swap methods. the network uses an autoencoder which is pre-trained on the original images to reconstruct the input images. the reconstructed one and the input image are then processed by the srm filter which can extract the noise distribution of images. we then feed the minus result of two processed results into a cnn architecture to predict whether the input image is original or tampered. the model was trained and evaluated in faceforensics dataset and state-of-art face swap method. experimental results demonstrate the effectiveness of our network.”
Guera, D., & Delp, E. J.. (2019). Deepfake Video Detection Using Recurrent Neural Networks. In Proceedings of AVSS 2018 – 2018 15th IEEE International Conference on Advanced Video and Signal-Based Surveillance
“In recent months a machine learning based free software tool has made it easy to create believable face swaps in videos that leaves few traces of manipulation, in what are known as deepfake videos. scenarios where these realistic fake videos are used to create political distress, blackmail someone or fake terrorism events are easily envisioned. this paper proposes a temporal-aware pipeline to automatically detect deepfake videos. our system uses a convolutional neural network (cnn) to extract frame-level features. these features are then used to train a recurrent neural network (rnn) that learns to classify if a video has been subject to manipulation or not. we evaluate our method against a large set of deepfake videos collected from multiple video websites. we show how our system can achieve competitive results in this task while using a simple architecture.”
Hashmi, M. F., Ashish, B. K. K., Keskar, A. G., Bokde, N. D., Yoon, J. H., & Geem, Z. W.. (2020). An Exploratory Analysis on Visual Counterfeits Using Conv-LSTM Hybrid Architecture. IEEE Access
“In recent years, with the advancements in the deep learning realm, it has been easy to create and generate synthetically the face swaps from gans and other tools, which are very realistic, leaving few traces which are unclassifiable by human eyes. these are known as ‘Deepfakes’ and most of them are anchored in video formats. such realistic fake videos and images are used to create a ruckus and affect the quality of public discourse on sensitive issues; defaming one’s profile, political distress, blackmailing and many more fake cyber terrorisms are envisioned. this work proposes a microscopic-typo comparison of video frames. this temporal-detection pipeline compares very minute visual traces on the faces of real and fake frames using convolutional neural network (cnn) and stores the abnormal features for training. a total of 512 facial landmarks were extracted and compared. parameters such as eye-blinking lip-synch; eyebrows movement, and position, are few main deciding factors that classify into real or counterfeit visual data. the recurrent neural network (rnn) pipeline learns based on these features-fed inputs and then evaluates the visual data. the model was trained with the network of videos consisting of their real and fake, collected from multiple websites. the proposed algorithm and designed network set a new benchmark for detecting the visual counterfeits and show how this system can achieve competitive results on any fake generated video or image.”
Nagarajan, A., & Soghrati, S.. (2018). Conforming to interface structured adaptive mesh refinement: 3D algorithm and implementation. Computational Mechanics
“A new non-iterative mesh generation algorithm named conforming to interface structured adaptive mesh refinement (cisamr) is introduced for creating 3d finite element models of problems with complex geometries. cisamr transforms a structured mesh composed of tetrahedral elements into a conforming mesh with low element aspect ratios. the construction of the mesh begins with the structured adaptive mesh refinement of elements in the vicinity of material interfaces. an r-adaptivity algorithm is then employed to relocate selected nodes of nonconforming elements, followed by face-swapping a small fraction of them to eliminate tetrahedrons with high aspect ratios. the final conforming mesh is constructed by sub-tetrahedralizing remaining nonconforming elements, as well as tetrahedrons with hanging nodes. in addition to studying the convergence and analyzing element-wise errors in meshes generated using cisamr, several example problems are presented to show the ability of this method for modeling 3d problems with intricate morphologies.”
Gerstner, E.. (2020). Face/off:” DeepFake” face swaps and privacy laws. Def. Counsel J.
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“… detect various prohibited content such as copyright violations, it would not be too complicated to add face swap detection as well to the bots which scan each and every post uploaded to their servers.the final potential target of legislation would be the software used to create …”
