Digital Platform As a Double-Edged Sword: How to Interpret Cultural Flows in the Platform Era
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Deepfakes and Cheap Fakes
DEEPFAKES AND CHEAP FAKES THE MANIPULATION OF AUDIO AND VISUAL EVIDENCE Britt Paris Joan Donovan DEEPFAKES AND CHEAP FAKES - 1 - CONTENTS 02 Executive Summary 05 Introduction 10 Cheap Fakes/Deepfakes: A Spectrum 17 The Politics of Evidence 23 Cheap Fakes on Social Media 25 Photoshopping 27 Lookalikes 28 Recontextualizing 30 Speeding and Slowing 33 Deepfakes Present and Future 35 Virtual Performances 35 Face Swapping 38 Lip-synching and Voice Synthesis 40 Conclusion 47 Acknowledgments Author: Britt Paris, assistant professor of Library and Information Science, Rutgers University; PhD, 2018,Information Studies, University of California, Los Angeles. Author: Joan Donovan, director of the Technology and Social Change Research Project, Harvard Kennedy School; PhD, 2015, Sociology and Science Studies, University of California San Diego. This report is published under Data & Society’s Media Manipulation research initiative; for more information on the initiative, including focus areas, researchers, and funders, please visit https://datasociety.net/research/ media-manipulation DATA & SOCIETY - 2 - EXECUTIVE SUMMARY Do deepfakes signal an information apocalypse? Are they the end of evidence as we know it? The answers to these questions require us to understand what is truly new about contemporary AV manipulation and what is simply an old struggle for power in a new guise. The first widely-known examples of amateur, AI-manipulated, face swap videos appeared in November 2017. Since then, the news media, and therefore the general public, have begun to use the term “deepfakes” to refer to this larger genre of videos—videos that use some form of deep or machine learning to hybridize or generate human bodies and faces. -
A Survey of Adversarial Learning on Graph
A Survey of Adversarial Learning on Graph LIANG CHEN, Sun Yat-sen University of China JINTANG LI, Sun Yat-sen University of China JIAYING PENG, Sun Yat-sen University of China TAO XIE, Sun Yat-sen University of China ZENGXU CAO, Hangzhou Dianzi University of China KUN XU, Sun Yat-sen University of China XIANGNAN HE, University of Science and Technology of China ZIBIN ZHENG∗, Sun Yat-sen University of China Deep learning models on graphs have achieved remarkable performance in various graph analysis tasks, e.g., node classication, link prediction and graph clustering. However, they expose uncertainty and unreliability against the well-designed inputs, i.e., adversarial examples. Accordingly, a line of studies have emerged for both aack and defense addressed in dierent graph analysis tasks, leading to the arms race in graph adversarial learning. Despite the booming works, there still lacks a unied problem denition and a comprehensive review. To bridge this gap, we investigate and summarize the existing works on graph adversarial learning tasks systemically. Specically, we survey and unify the existing works w.r.t. aack and defense in graph analysis tasks, and give appropriate denitions and taxonomies at the same time. Besides, we emphasize the importance of related evaluation metrics, investigate and summarize them comprehensively. Hopefully, our works can provide a comprehensive overview and oer insights for the relevant researchers. More details of our works are available at hps://github.com/gitgiter/Graph-Adversarial-Learning. CCS Concepts: •Computing methodologies ! Semi-supervised learning settings; Neural networks; •Security and privacy ! So ware and application security; •Networks ! Network reliability; Additional Key Words and Phrases: adversarial learning, graph neural networks, adversarial aack and defense, adversarial examples, network robustness ACM Reference format: Liang Chen, Jintang Li, Jiaying Peng, Tao Xie, Zengxu Cao, Kun Xu, Xiangnan He, and Zibin Zheng. -
{PDF} the Facebook Effect: the Real Inside Story of Mark Zuckerberg
THE FACEBOOK EFFECT: THE REAL INSIDE STORY OF MARK ZUCKERBERG AND THE WORLDS FASTEST GROWING COMPANY PDF, EPUB, EBOOK David Kirkpatrick | 384 pages | 01 Feb 2011 | Ebury Publishing | 9780753522752 | English | London, United Kingdom The Facebook Effect: The Real Inside Story of Mark Zuckerberg and the Worlds Fastest Growing Company PDF Book The cover of the plus-page hardcover tome is the silhouette of a face made of mirror-like, reflective paper. Not bad for a Harvard dropout who later became a visionary and technologist of this digital era. Using the kind of computer code otherwise used to rank chess players perhaps it could also have been used for fencers , he invited users to compare two different faces of the same sex and say which one was hotter. View all 12 comments. There was a lot of time for bull sessions, which tended to center on what kind of software should happen next on the Internet. He searched around online and found a hosting company called Manage. Even for those not so keen on geekery and computers, the political wrangling of the company supplies plenty of drama. As Facebook spreads around the globe, it creates surprising effects—even becoming instrumental in political protests from Colombia to Iran. But there are kinks in the storytelling. In little more than half a decade, Facebook has gone from a dorm-room novelty to a company with million users. Sheryl Sandberg, COO: Sandberg is an elegant, slightly hyper, light-spirited forty- year-old with a round face whose bobbed black hair reaches just past her shoulders. -
Citron Research Backing up the Sleigh on Facebook – 2019 S&P
December 26, 2018 Citron Research Backing Up the Sleigh on Facebook – 2019 S&P Stock of the Year Separating the Noise from Reality will take Facebook back to $160 Two and a half years ago, Citron Research said that FB was a long-term short and that engagement levels would eventually top out. At the time, the stock was trading in the $120 range. In the past 30 months FB has more than doubled its quarterly revenue and concerns of engagement have shifted to concerns of addiction, yet the stock is back down in $120 range. Time to back up the sleigh! Yet, 2018 is a year that Facebook shareholders would like to forget, as the stock is down 30% YTD and over 40% from its recent high. Yet, while the media has reported on one scandal after the next and the NYT even tried to promote a #deletefacebook movement. The truth is revenues and more important user base has seen little impact. As you read this article Facebook has 2.2 billion active users and has grown revenues 33% q over q during this controversial 12 months. Considering the user base excludes China, this is a lead that has no second place. Going Forward There are only three talking points on Facebook that matter: 1. Putting Facebook Valuation in Perspective 2. Is Facebook Evil or Investible? 3. Instagram – the Anti CRaP experience © Copyright 2018 | Citron Research | www.citronresearch.com | All Inquiries – [email protected] December 26, 2018 Valuation in Perspective Facebook is growing faster than 95% of the S&P with margins higher than about 90% of the S&P. -
Deepfakes and Cheap Fakes
DEEPFAKES AND CHEAP FAKES THE MANIPULATION OF AUDIO AND VISUAL EVIDENCE Britt Paris Joan Donovan DEEPFAKES AND CHEAP FAKES - 1 - CONTENTS 02 Executive Summary 05 Introduction 10 Cheap Fakes/Deepfakes: A Spectrum 17 The Politics of Evidence 23 Cheap Fakes on Social Media 25 Photoshopping 27 Lookalikes 28 Recontextualizing 30 Speeding and Slowing 33 Deepfakes Present and Future 35 Virtual Performances 35 Face Swapping 38 Lip-synching and Voice Synthesis 40 Conclusion 47 Acknowledgments Author: Britt Paris, assistant professor of Library and Information Science, Rutgers University; PhD, 2018,Information Studies, University of California, Los Angeles. Author: Joan Donovan, director of the Technology and Social Change Research Project, Harvard Kennedy School; PhD, 2015, Sociology and Science Studies, University of California San Diego. This report is published under Data & Society’s Media Manipulation research initiative; for more information on the initiative, including focus areas, researchers, and funders, please visit https://datasociety.net/research/ media-manipulation DATA & SOCIETY - 2 - EXECUTIVE SUMMARY Do deepfakes signal an information apocalypse? Are they the end of evidence as we know it? The answers to these questions require us to understand what is truly new about contemporary AV manipulation and what is simply an old struggle for power in a new guise. The first widely-known examples of amateur, AI-manipulated, face swap videos appeared in November 2017. Since then, the news media, and therefore the general public, have begun to use the term “deepfakes” to refer to this larger genre of videos—videos that use some form of deep or machine learning to hybridize or generate human bodies and faces. -
Social Media Platforms and Democratic Discourse
LCB_23_4_Art_6_Weaver (Do Not Delete) 2/7/2020 2:22 PM SYMPOSIUM SOCIAL MEDIA PLATFORMS AND DEMOCRATIC DISCOURSE by Russell L. Weaver* This Essay explores how social media platforms have been catalysts for social and political change but have created numerous societal problems. The Essay traces the development of speech technologies and shows how these platforms have influenced the world. These changes are evident in the events of the Arab Spring in the Middle East and even in U.S. political elections (including those of President Obama and President Trump). At the same time, the internet and social media present immense challenges to the democratic process. They have enabled individuals to infect the public debate with so-called “fake news,” and have enabled foreign individuals and foreign governments to in- terfere in the U.S. election. In addition, as social media companies endeavor to exercise greater control over the public debate, there is a risk that they will censor or unduly restrict social and political discourse. Introduction .................................................................................................. 1386 I. The Rise and Influence of Social Media Platforms .............................. 1387 II. The Problems Created by Social Media............................................... 1399 A. Fake News ................................................................................... 1399 B. Interference in Electoral Campaigns ............................................... 1400 III. Social Media as the “Gatekeeper” -
Estimating Age and Gender in Instagram Using Face Recognition: Advantages, Bias and Issues. / Diego Couto De Las Casas
ESTIMATING AGE AND GENDER IN INSTAGRAM USING FACE RECOGNITION: ADVANTAGES, BIAS AND ISSUES. DIEGO COUTO DE. LAS CASAS ESTIMATING AGE AND GENDER IN INSTAGRAM USING FACE RECOGNITION: ADVANTAGES, BIAS AND ISSUES. Dissertação apresentada ao Programa de Pós-Graduação em Ciência da Computação do Instituto de Ciências Exatas da Univer- sidade Federal de Minas Gerais – Depar- tamento de Ciência da Computação como requisito parcial para a obtenção do grau de Mestre em Ciência da Computação. Orientador: Virgílio Augusto Fernandes de Almeida Belo Horizonte Fevereiro de 2016 DIEGO COUTO DE. LAS CASAS ESTIMATING AGE AND GENDER IN INSTAGRAM USING FACE RECOGNITION: ADVANTAGES, BIAS AND ISSUES. Dissertation presented to the Graduate Program in Ciência da Computação of the Universidade Federal de Minas Gerais – De- partamento de Ciência da Computação in partial fulfillment of the requirements for the degree of Master in Ciência da Com- putação. Advisor: Virgílio Augusto Fernandes de Almeida Belo Horizonte February 2016 © 2016, Diego Couto de Las Casas. Todos os direitos reservados Ficha catalográfica elaborada pela Biblioteca do ICEx - UFMG Las Casas, Diego Couto de. L337e Estimating age and gender in Instagram using face recognition: advantages, bias and issues. / Diego Couto de Las Casas. – Belo Horizonte, 2016. xx, 80 f. : il.; 29 cm. Dissertação (mestrado) - Universidade Federal de Minas Gerais – Departamento de Ciência da Computação. Orientador: Virgílio Augusto Fernandes de Almeida. 1. Computação - Teses. 2. Redes sociais on-line. 3. Computação social. 4. Instagram. I. Orientador. II. Título. CDU 519.6*04(043) Acknowledgments Gostaria de agradecer a todos que me fizeram chegar até aqui. Àminhafamília,pelosconselhos,pitacoseportodoosuporteaolongodesses anos. Aos meus colegas do CAMPS(-Élysées),pelascolaborações,pelasrisadasepelo companheirismo. -
Graph Search Process in Social Networks and It's Challenges
Preeti Narooka et al | IJCSET(www.ijcset.net) | June 2016 | Vol 6, Issue 6, 228-232 Graph Search Process in Social Networks and it’s Challenges Ms. Preeti Narooka1, Dr. Sunita Chaodhary2 Ph. D Student, Banasthali University, Jaipur, India1 Former Associate Professor, Banasthali University, Jaipur, India 2 Abstract: - Social networking sites are grabbing attention of complex network where huge amount of data can be millions of users and it resulted into creation of humongous distribute and store easily. data on these networking sites. Number of users on any networking sites denotes the complexity and size of the data. Graph algorithm is the efficient tool which defines the These developments have thrown a challenge in front of relationships between all objects on social cloud. In today’s computer scientists to optimize the search operations on these scenario, users are increasing in Social Networks in leaps social networking sites. This paper outlines the Graph theory and bounds. With the increase of users, there is a rapid as a tool for search operation on social networking sites & for increment in number of objects on social graph, results in optimization of search graph operation map reducing exponential growth of the graph size [12]. Accessing useful technology is discussed. In this paper on one side, different information from exponentially growing graph in fast traditional system of social networking and graph algorithm are elaborated. On the other side, various application and manner is a very difficult challenging task. To provide different issues related to the graph algorithm and social solution for the aforesaid problem it is the need to optimize networking are discussed in detail. -
Artificial Intelligence: Risks to Privacy and Democracy
Artificial Intelligence: Risks to Privacy and Democracy Karl Manheim* and Lyric Kaplan** 21 Yale J.L. & Tech. 106 (2019) A “Democracy Index” is published annually by the Economist. For 2017, it reported that half of the world’s countries scored lower than the previous year. This included the United States, which was de- moted from “full democracy” to “flawed democracy.” The princi- pal factor was “erosion of confidence in government and public in- stitutions.” Interference by Russia and voter manipulation by Cam- bridge Analytica in the 2016 presidential election played a large part in that public disaffection. Threats of these kinds will continue, fueled by growing deployment of artificial intelligence (AI) tools to manipulate the preconditions and levers of democracy. Equally destructive is AI’s threat to deci- sional and informational privacy. AI is the engine behind Big Data Analytics and the Internet of Things. While conferring some con- sumer benefit, their principal function at present is to capture per- sonal information, create detailed behavioral profiles and sell us goods and agendas. Privacy, anonymity and autonomy are the main casualties of AI’s ability to manipulate choices in economic and po- litical decisions. The way forward requires greater attention to these risks at the na- tional level, and attendant regulation. In its absence, technology gi- ants, all of whom are heavily investing in and profiting from AI, will dominate not only the public discourse, but also the future of our core values and democratic institutions. * Professor of Law, Loyola Law School, Los Angeles. This article was inspired by a lecture given in April 2018 at Kansai University, Osaka, Japan. -
Unicorn: a System for Searching the Social Graph
Unicorn: A System for Searching the Social Graph Michael Curtiss, Iain Becker, Tudor Bosman, Sergey Doroshenko, Lucian Grijincu, Tom Jackson, Sandhya Kunnatur, Soren Lassen, Philip Pronin, Sriram Sankar, Guanghao Shen, Gintaras Woss, Chao Yang, Ning Zhang Facebook, Inc. ABSTRACT rative of the evolution of Unicorn's architecture, as well as Unicorn is an online, in-memory social graph-aware index- documentation for the major features and components of ing system designed to search trillions of edges between tens the system. of billions of users and entities on thousands of commodity To the best of our knowledge, no other online graph re- servers. Unicorn is based on standard concepts in informa- trieval system has ever been built with the scale of Unicorn tion retrieval, but it includes features to promote results in terms of both data volume and query volume. The sys- with good social proximity. It also supports queries that re- tem serves tens of billions of nodes and trillions of edges quire multiple round-trips to leaves in order to retrieve ob- at scale while accounting for per-edge privacy, and it must jects that are more than one edge away from source nodes. also support realtime updates for all edges and nodes while Unicorn is designed to answer billions of queries per day at serving billions of daily queries at low latencies. latencies in the hundreds of milliseconds, and it serves as an This paper includes three main contributions: infrastructural building block for Facebook's Graph Search • We describe how we applied common information re- product. In this paper, we describe the data model and trieval architectural concepts to the domain of the so- query language supported by Unicorn. -
Graph Generators: State of the Art and Open Challenges 0:3 Across All the Categories of Graph Generators That We Consider
0 Graph Generators: State of the Art and Open Challenges ANGELA BONIFATI, Lyon 1 University, France IRENA HOLUBOVA´ , Charles University, Prague, Czech Republic ARNAU PRAT-PEREZ´ , Sparsity-Technologies, Barcelona, Spain SHERIF SAKR, University of Tartu, Estonia The abundance of interconnected data has fueled the design and implementation of graph generators repro- ducing real-world linking properties, or gauging the effectiveness of graph algorithms, techniques and appli- cations manipulating these data. We consider graph generation across multiple subfields, such as Semantic Web, graph databases, social networks, and community detection, along with general graphs. Despite the disparate requirements of modern graph generators throughout these communities, we analyze them under a common umbrella, reaching out the functionalities, the practical usage, and their supported operations. We argue that this classification is serving the need of providing scientists, researchers and practitioners with the right data generator at hand for their work. This survey provides a comprehensive overview of the state-of-the-art graph generators by focusing on those that are pertinent and suitable for several data-intensive tasks. Finally, we discuss open challenges and missing requirements of current graph generators along with their future extensions to new emerging fields. 1. INTRODUCTION Graphs are ubiquitous data structures that span a wide array of scientific disciplines and are a subject of study in several subfields of computer science. Nowadays, due to the dawn of numerous tools and applications manipulating graphs, they are adopted as a rich data model in many data-intensive use cases involving disparate domain knowl- edge, mathematical foundations and computer science. Interconnected data is often- times used to encode domain-specific use cases, such as recommendation networks, social networks, protein-to-protein interactions, geolocation networks, and fraud de- tection analysis networks, to name a few. -
Towards an Articulation and Assemblage Theory of PIP A
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by University of Minnesota Digital Conservancy The Politics of Personal Information Privacy for the Facebook Age – Towards an Articulation and Assemblage Theory of PIP A Dissertation SUBMITTED TO THE FACULTY OF THE UNIVERSITY OF MINNESOTA BY Lars Weise IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY Gilbert Rodman, Ph.D, adviser May 2014 Copyright Acknowledgement “Culture is ordinary. […] We use the word culture in these two senses: to mean a whole way of life – the common meanings; to mean the arts and learning – the special processes of discovery and creative effort.” Raymond Williams, 1958 A dissertation is ordinary, too. Writing a dissertation is possible only because of the great number of people, who make up our lives. That is why I first thank my partner Hannah. Her willingness to live with me in a foreign country, to never doubt the reasoning behind my plans, her strength and her courage to engage even the most challenging situations, has been a daily inspiration. During the last four years I could always count on my parents's unquestioned support for this project, their patience and worldly wisdom. I own my confidence to them. My gratitude goes to both Hannah's and my family for their love and support, especially once our wonderful daughter Johanna Karlotta made focussing on academic work just a little bit more challenging. Finally, I would like to thank our friends in Minnesota for making our lives abroad enlightening, fun, and enjoyable.