Deepfakes: trick or treat?

Although manipulations of visual and auditory media are as old as the media themselves, the recent entrance of deepfakes has marked a turning point in the creation of fake content. Powered by latest technological advances in AI and machine learning, they offer automated procedures to create fake content that is harder and harder to detect to human observers. The possibilities to deceive are endless, including manipulated pictures, videos and audio, that will have large societal impact. Because of this, organizations need to understand the inner workings of the underlying techniques, as well as their strengths and limitations. This article provides a working definition of deepfakes together with an overview of the underlying technology. We classify different deepfake types: photo (face- and body-swapping), audio (voice-swapping, text to speech), video (face-swapping, face-morphing, full body puppetry) and audio and video (lip-synching), and identify risks and opportunities to help organizations think about the future of deepfakes. Finally, we propose the R.E.A.L. framework to manage deepfake risks: Record original content to assure deniability, Expose deepfakes early, Advocate for legal protection and Leverage trust to counter credulity. Following these principles, we hope that our society can be more prepared to counter the deepfake tricks as we appreciate its treats

From photos to sketches-how humans and deep neural networks process objects across different levels of visual abstraction

Line drawings convey meaning with just a few strokes. Despite strong simplifications, humans can recognize objects depicted in such abstracted images without effort. To what degree do deep convolutional neural networks (CNNs) mirror this human ability to generalize to abstracted object images? While CNNs trained on natural images have been shown to exhibit poor classification performance on drawings, other work has demonstrated highly similar latent representations in the networks for abstracted and natural images. Here, we address these seemingly conflicting findings by analyzing the activation patterns of a CNN trained on natural images across a set of photographs, drawings, and sketches of the same objects and comparing them to human behavior. We find a highly similar representational structure across levels of visual abstraction in early and intermediate layers of the network. This similarity, however, does not translate to later stages in the network, resulting in low classification performance for drawings and sketches. We identified that texture bias in CNNs contributes to the dissimilar representational structure in late layers and the poor performance on drawings. Finally, by fine-tuning late network layers with object drawings, we show that performance can be largely restored, demonstrating the general utility of features learned on natural images in early and intermediate layers for the recognition of drawings. In conclusion, generalization to abstracted images, such as drawings, seems to be an emergent property of CNNs trained on natural images, which is, however, suppressed by domain-related biases that arise during later processing stages in the network

Ab initio Equation of State data for hydrogen, helium, and water and the internal structure of Jupiter

The equation of state of hydrogen, helium, and water effects interior structure models of giant planets significantly. We present a new equation of state data table, LM-REOS, generated by large scale quantum molecular dynamics simulations for hydrogen, helium, and water in the warm dense matter regime, i.e.for megabar pressures and temperatures of several thousand Kelvin, and by advanced chemical methods in the complementary regions. The influence of LM-REOS on the structure of Jupiter is investigated and compared with state-of-the-art results within a standard three-layer model consistent with astrophysical observations of Jupiter. Our new Jupiter models predict an important impact of mixing effects of helium in hydrogen with respect to an altered compressibility and immiscibility.Comment: to appear in ApJ in August 2008, 11 figure

Carbon clusters near the crossover to fullerene stability

The thermodynamic stability of structural isomers of $\mathrm{C}_{24}$, $\mathrm{C}_{26}$, $\mathrm{C}_{28}$ and $\mathrm{C}_{32}$, including fullerenes, is studied using density functional and quantum Monte Carlo methods. The energetic ordering of the different isomers depends sensitively on the treatment of electron correlation. Fixed-node diffusion quantum Monte Carlo calculations predict that a $\mathrm{C}_{24}$ isomer is the smallest stable graphitic fragment and that the smallest stable fullerenes are the $\mathrm{C}_{26}$ and $\mathrm{C}_{28}$ clusters with $\mathrm{C}_{2v}$ and $\mathrm{T}_{d}$ symmetry, respectively. These results support proposals that a $\mathrm{C}_{28}$ solid could be synthesized by cluster deposition.Comment: 4 pages, includes 4 figures. For additional graphics, online paper and related information see http://www.tcm.phy.cam.ac.uk/~prck

Building Online Platforms for Peer Support Groups as a Persuasive Behavioural Change Technique

Online peer group approach is inherently a persuasive technique as it is centered on peer pressure and surveillance. They are persuasive social net- works equipped with tools and facilities that enable behaviour change. This paper presents the case for domain-specific persuasive social networks and provides insights on problematic and addictive behaviour change. A 4-month study was conducted in an addiction rehab centre in the UK, followed by 2-month study in an online peer group system. The study adopted qualitative methods to under- stand the broad parameters of peer groups including the sessions' environment, norms, interaction styles occurring between groups' members and how such in- teractions are governed. The qualitative techniques used were (1) observations, (2) form and document analysis, and (3) semi-structured interviews. The findings concern governing such groups in addition to the roles to be enabled and tasks to be performed. The Honeycomb framework was revisited to comment on its build- ing blocks with the purpose of highlighting points to consider when building do- main-specific social networks for such domain, i.e. online peer groups to combat addictive behaviour

Measures and Limits of Models of Fixation Selection

Models of fixation selection are a central tool in the quest to understand how the human mind selects relevant information. Using this tool in the evaluation of competing claims often requires comparing different models' relative performance in predicting eye movements. However, studies use a wide variety of performance measures with markedly different properties, which makes a comparison difficult. We make three main contributions to this line of research: First we argue for a set of desirable properties, review commonly used measures, and conclude that no single measure unites all desirable properties. However the area under the ROC curve (a classification measure) and the KL-divergence (a distance measure of probability distributions) combine many desirable properties and allow a meaningful comparison of critical model performance. We give an analytical proof of the linearity of the ROC measure with respect to averaging over subjects and demonstrate an appropriate correction of entropy-based measures like KL-divergence for small sample sizes in the context of eye-tracking data. Second, we provide a lower bound and an upper bound of these measures, based on image-independent properties of fixation data and between subject consistency respectively. Based on these bounds it is possible to give a reference frame to judge the predictive power of a model of fixation selection . We provide open-source python code to compute the reference frame. Third, we show that the upper, between subject consistency bound holds only for models that predict averages of subject populations. Departing from this we show that incorporating subject-specific viewing behavior can generate predictions which surpass that upper bound. Taken together, these findings lay out the required information that allow a well-founded judgment of the quality of any model of fixation selection and should therefore be reported when a new model is introduced

Expression pattern of the urokinase-plasminogen activator system in rat DS-sarcoma: Role of oxygenation status and tumour size

The urokinase plasminogen activator system plays a central role in malignant tumour progression. Both tumour hypoxia and enhancement of urokinase plasminogen activator, urokinase plasminogen activator-receptor and plasminogen activator inhibitor type 1 have been identified as adverse prognostic factors. Upregulation of urokinase plasminogen activator or plasminogen activator inhibitor type 1 could present means by which hypoxia influences malignant progression. Therefore, the impact of hypoxia on the expression pattern of the urokinase plasminogen activator system in rat DS-sarcoma in vivo and in vitro was examined. In the in vivo setting, tumour cells were implanted subcutaneously into rats, which were housed under either hypoxia, atmospheric air or hyperoxia. For in vitro studies, DS-sarcoma cells were incubated for 24 h under hypoxia. Urokinase plasminogen activator and urokinase plasminogen activator-receptor expression were analysed by flow cytometry. Urokinase plasminogen activator activity was measured using zymography. Plasminogen activator inhibitor type 1 protein levels in vitro and in vivo were examined with ELISA. PAI-1 mRNA levels were determined by RT–PCR. DS-sarcoma cells express urokinase plasminogen activator, urokinase plasminogen activator-receptor, and plasminogen activator inhibitor type 1 in vitro and in vivo. The urokinase plasminogen activator activity is enhanced in DS-sarcomas compared to normal tissues and rises with increasing tumour volume. The oxygenation level has no impact on the urokinase plasminogen activator activity in cultured DS-sarcoma cells or in solid tumours, although in vitro an increase in plasminogen activator inhibitor type 1 protein and mRNA expression after hypoxic challenge is detectable. The latter plasminogen activator inhibitor type 1 changes were not detectable in vivo. Hypoxia has been demonstrated to contribute to the upregulation of some components of the system in vitro, although this effect was not reproducible in vivo. This may indicate that the serum level of plasminogen activator inhibitor type 1 is not a reliable surrogate marker of tumour hypoxia

Collection of Aerosolized Human Cytokines Using Teflon® Filters

Background: Collection of exhaled breath samples for the analysis of inflammatory biomarkers is an important area of research aimed at improving our ability to diagnose, treat and understand the mechanisms of chronic pulmonary disease. Current collection methods based on condensation of water vapor from exhaled breath yield biomarker levels at or near the detection limits of immunoassays contributing to problems with reproducibility and validity of biomarker measurements. In this study, we compare the collection efficiency of two aerosol-to-liquid sampling devices to a filter-based collection method for recovery of dilute laboratory generated aerosols of human cytokines so as to identify potential alternatives to exhaled breath condensate collection. Methodology/Principal Findings: Two aerosol-to-liquid sampling devices, the SKC® Biosampler and Omni 3000™, as well as Teflon® filters were used to collect aerosols of human cytokines generated using a HEART nebulizer and single-pass aerosol chamber setup in order to compare the collection efficiencies of these sampling methods. Additionally, methods for the use of Teflon® filters to collect and measure cytokines recovered from aerosols were developed and evaluated through use of a high-sensitivity multiplex immunoassay. Our results show successful collection of cytokines from pg/m3 aerosol concentrations using Teflon® filters and measurement of cytokine levels in the sub-picogram/mL concentration range using a multiplex immunoassay with sampling times less than 30 minutes. Significant degradation of cytokines was observed due to storage of cytokines in concentrated filter extract solutions as compared to storage of dry filters. Conclusions: Use of filter collection methods resulted in significantly higher efficiency of collection than the two aerosol-to-liquid samplers evaluated in our study. The results of this study provide the foundation for a potential new technique to evaluate biomarkers of inflammation in exhaled breath samples