Face/Off: “Deepfake” Face Swaps and Privacy Laws
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Artificial Intelligence in Health Care: the Hope, the Hype, the Promise, the Peril
Artificial Intelligence in Health Care: The Hope, the Hype, the Promise, the Peril Michael Matheny, Sonoo Thadaney Israni, Mahnoor Ahmed, and Danielle Whicher, Editors WASHINGTON, DC NAM.EDU PREPUBLICATION COPY - Uncorrected Proofs NATIONAL ACADEMY OF MEDICINE • 500 Fifth Street, NW • WASHINGTON, DC 20001 NOTICE: This publication has undergone peer review according to procedures established by the National Academy of Medicine (NAM). Publication by the NAM worthy of public attention, but does not constitute endorsement of conclusions and recommendationssignifies that it is the by productthe NAM. of The a carefully views presented considered in processthis publication and is a contributionare those of individual contributors and do not represent formal consensus positions of the authors’ organizations; the NAM; or the National Academies of Sciences, Engineering, and Medicine. Library of Congress Cataloging-in-Publication Data to Come Copyright 2019 by the National Academy of Sciences. All rights reserved. Printed in the United States of America. Suggested citation: Matheny, M., S. Thadaney Israni, M. Ahmed, and D. Whicher, Editors. 2019. Artificial Intelligence in Health Care: The Hope, the Hype, the Promise, the Peril. NAM Special Publication. Washington, DC: National Academy of Medicine. PREPUBLICATION COPY - Uncorrected Proofs “Knowing is not enough; we must apply. Willing is not enough; we must do.” --GOETHE PREPUBLICATION COPY - Uncorrected Proofs ABOUT THE NATIONAL ACADEMY OF MEDICINE The National Academy of Medicine is one of three Academies constituting the Nation- al Academies of Sciences, Engineering, and Medicine (the National Academies). The Na- tional Academies provide independent, objective analysis and advice to the nation and conduct other activities to solve complex problems and inform public policy decisions. -
Indiana Jane’ Pens Another Florence Book / Indianapolis Star
‘Indiana Jane’ pens another Florence Book / Indianapolis Star 'Indiana Jane' pens another Florence book It would be difficult to find someone who loves Florence, Italy, more than Indianapolis philanthropist Jane Fortune . Fortune lives there with her partner, Bob Hesse , about four months out of the year. As evidence of her soft spot for the city, she has done everything from penning “To Florence con Amore,” a 2007 book about the art-filled city’s best behind- the-scenes places, to starting a foundation aimed at restoring and preserving Florentine female artists’ work — pieces Fortune says have been largely overlooked. She added to that list last month when her latest book came out: “Invisible Women: Forgotten Artists of Florence,” an art history book that spotlights the work of women. Fortune is known as “Indiana Jane” for her efforts in the Italian city, where her culture column shows up in “The Florentine,” a biweekly publication for English speakers. In Indianapolis, she is better known for her recent support of the inaugural performance of the Indianapolis City Ballet. “Invisible Women,” ($28, The Florentine Press) made its debut in Indianapolis this month. To celebrate, Fortune’s local friends (and Hesse, to whom the book is dedicated) gathered for a book- signing party Dec. 8 at the Indianapolis Museum of Art. Fortune explained that the proceeds of both books are channeled to her foundation, which helps advance Italian female artists. The IMA gift store carries both of Fortune’s books, and they can also be ordered online at shop.imamuseum.org. Equal access to holiday fun The Italianate home of Ken Ramsay and Joe Everhart on the Old Northside created some holiday magic for Indiana Equality this month. -
False Light, Disclosure, and Outrage Torts
_ False Light, Disclosure, and Outrage Torts Eric E. Johnson ericejohnson.com Konomark Most rights sharable Some general notes about false light, disclosure, and outrage: • They are available for natural, living persons only – not for corporations • Much of defamation doctrine applies – Identification of plaintiff – Fact vs. opinion – Substantial truth (but not for disclosure) • The First Amendment can substantially limit any of these torts – State action hurdle overcome a la NYT v. Sullivan 1 _ False Light The Elements: 1. A public statement 2. Made with actual malice 3. Placing the plaintiff in a false light 4. That is highly offensive to the reasonable person False Light Defenses: • Essentially the same as for defamation • So, for example: – A public figure will have to prove actual malice.* – A private figure, if a matter of public concern, must prove actual malice or negligence + special damages.* *That is, if actual malice is not required as a prima facie element, which it generally, but not always, is. 2 _ Disclosure The Elements: 1. A public disclosure 2. Of private facts 3. That is highly offensive to the reasonable person Disclosure Defenses: • Legitimate public interest or concern – a/k/a “newsworthiness privilege” – First Amendment requires this, even if common law in a jurisdiction would not 3 _ Outrage (a/k/a Intentional Infliction of Emotional Distress) The Elements: 1. Intentional or reckless conduct that is 2. Extreme and outrageous 3. Causing severe emotional distress Review Intrusion The Elements: 1. Physical or other intrusion 2. Into a zone in which the plaintiff has a reasonable expectation of privacy 3. -
Real Vs Fake Faces: Deepfakes and Face Morphing
Graduate Theses, Dissertations, and Problem Reports 2021 Real vs Fake Faces: DeepFakes and Face Morphing Jacob L. Dameron WVU, [email protected] Follow this and additional works at: https://researchrepository.wvu.edu/etd Part of the Signal Processing Commons Recommended Citation Dameron, Jacob L., "Real vs Fake Faces: DeepFakes and Face Morphing" (2021). Graduate Theses, Dissertations, and Problem Reports. 8059. https://researchrepository.wvu.edu/etd/8059 This Thesis is protected by copyright and/or related rights. It has been brought to you by the The Research Repository @ WVU with permission from the rights-holder(s). You are free to use this Thesis in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you must obtain permission from the rights-holder(s) directly, unless additional rights are indicated by a Creative Commons license in the record and/ or on the work itself. This Thesis has been accepted for inclusion in WVU Graduate Theses, Dissertations, and Problem Reports collection by an authorized administrator of The Research Repository @ WVU. For more information, please contact [email protected]. Real vs Fake Faces: DeepFakes and Face Morphing Jacob Dameron Thesis submitted to the Benjamin M. Statler College of Engineering and Mineral Resources at West Virginia University in partial fulfillment of the requirements for the degree of Master of Science in Electrical Engineering Xin Li, Ph.D., Chair Natalia Schmid, Ph.D. Matthew Valenti, Ph.D. Lane Department of Computer Science and Electrical Engineering Morgantown, West Virginia 2021 Keywords: DeepFakes, Face Morphing, Face Recognition, Facial Action Units, Generative Adversarial Networks, Image Processing, Classification. -
Synthetic Video Generation
Synthetic Video Generation Why seeing should not always be believing! Alex Adam Image source https://www.pocket-lint.com/apps/news/adobe/140252-30-famous-photoshopped-and-doctored-images-from-across-the-ages Image source https://www.pocket-lint.com/apps/news/adobe/140252-30-famous-photoshopped-and-doctored-images-from-across-the-ages Image source https://www.pocket-lint.com/apps/news/adobe/140252-30-famous-photoshopped-and-doctored-images-from-across-the-ages Image source https://www.pocket-lint.com/apps/news/adobe/140252-30-famous-photoshopped-and-doctored-images-from-across-the-ages Image Tampering Historically, manipulated Off the shelf software (e.g imagery has deceived Photoshop) exists to do people this now Has become standard in Public have become tabloids/social media somewhat numb to it - it’s no longer as impactful/shocking How does machine learning fit in? Advent of machine learning Video manipulation is now has made image also tractable with enough manipulation even easier data and compute Can make good synthetic Public are largely unaware of videos using a gaming this and the danger it poses! computer in a bedroom Part I: Faceswap ● In 2017, reddit (/u/deepfakes) posted Python code that uses machine learning to swap faces in images/video ● ‘Deepfake’ content flooded reddit, YouTube and adult websites ● Reddit since banned this content (but variants of the code are open source https://github.com/deepfakes/faceswap) ● Autoencoder concepts underlie most ‘Deepfake’ methods Faceswap Algorithm Image source https://medium.com/@jonathan_hui/how-deep-learning-fakes-videos-deepfakes-and-how-to-detect-it-c0b50fbf7cb9 Inference Image source https://medium.com/@jonathan_hui/how-deep-learning-fakes-videos-deepfakes-and-how-to-detect-it-c0b50fbf7cb9 ● Faceswap model is an autoencoder. -
Semifinalists to Face Off for Beef Loving Texans' Best Butcher in Texas
FOR IMMEDIATE RELEASE Contact: Sarah Flores, Hahn Public for Texas Beef Council 512-344-2045 [email protected] SEMIFINALISTS TO FACE OFF FOR BEEF LOVING TEXANS’ BEST BUTCHER IN TEXAS Texas Beef Council Selects Competitors to Battle for Coveted Finalist Spots AUSTIN, Texas – Feb. 23, 2017 –Texas Beef Council announces the top Semifinalists who will move on to compete in the Beef Loving Texans’ Best Butcher in Texas regional competition. The challenge, which pits butchers from across Texas against each other for the chance to win cash prizes and the esteemed title of Beef Loving Texans’ Best Butcher in Texas, has brought some of the state’s most talented butchers together – representing an art form that has been important to Texas’ cultural heritage. Regional semifinal rounds will be held throughout the state in Houston on March 4, Dallas on March 18 and San Antonio on April 1. In each city, Semifinalists will partake in a three-part challenge, which tests competitors on cut identification, along with their skills to cut to order and cut beef for retail merchandising. Each competitor will be equipped with Victorinox Swiss Army boning knives, a breaking knife, a cut resistant glove, a steel and a knife roll, to ensure everyone starts on an even playing field. Competitors will receive top marks based on their technique, creativity, presentation and consumer interaction. With culinary influencer/personality Jess Pryles emceeing, top industry professionals and culinary experts will weigh in in each region to determine the top three competitors who will move on to the final round at the Austin Food + Wine Festival on April 29. -
Exposing Deepfake Videos by Detecting Face Warping Artifacts
Exposing DeepFake Videos By Detecting Face Warping Artifacts Yuezun Li, Siwei Lyu Computer Science Department University at Albany, State University of New York, USA Abstract sibility to large-volume training data and high-throughput computing power, but more to the growth of machine learn- In this work, we describe a new deep learning based ing and computer vision techniques that eliminate the need method that can effectively distinguish AI-generated fake for manual editing steps. videos (referred to as DeepFake videos hereafter) from real In particular, a new vein of AI-based fake video gen- videos. Our method is based on the observations that cur- eration methods known as DeepFake has attracted a lot rent DeepFake algorithm can only generate images of lim- of attention recently. It takes as input a video of a spe- ited resolutions, which need to be further warped to match cific individual (’target’), and outputs another video with the original faces in the source video. Such transforms leave the target’s faces replaced with those of another individ- distinctive artifacts in the resulting DeepFake videos, and ual (’source’). The backbone of DeepFake are deep neu- we show that they can be effectively captured by convo- ral networks trained on face images to automatically map lutional neural networks (CNNs). Compared to previous the facial expressions of the source to the target. With methods which use a large amount of real and DeepFake proper post-processing, the resulting videos can achieve a generated images to train CNN classifier, our method does high level of realism. not need DeepFake generated images as negative training In this paper, we describe a new deep learning based examples since we target the artifacts in affine face warp- method that can effectively distinguish DeepFake videos ing as the distinctive feature to distinguish real and fake from the real ones. -
Behavioral Plasticity Through the Modulation of Switch Neurons
Behavioral Plasticity Through the Modulation of Switch Neurons Vassilis Vassiliades, Chris Christodoulou Department of Computer Science, University of Cyprus, 1678 Nicosia, Cyprus Abstract A central question in artificial intelligence is how to design agents ca- pable of switching between different behaviors in response to environmental changes. Taking inspiration from neuroscience, we address this problem by utilizing artificial neural networks (NNs) as agent controllers, and mecha- nisms such as neuromodulation and synaptic gating. The novel aspect of this work is the introduction of a type of artificial neuron we call \switch neuron". A switch neuron regulates the flow of information in NNs by se- lectively gating all but one of its incoming synaptic connections, effectively allowing only one signal to propagate forward. The allowed connection is determined by the switch neuron's level of modulatory activation which is affected by modulatory signals, such as signals that encode some informa- tion about the reward received by the agent. An important aspect of the switch neuron is that it can be used in appropriate \switch modules" in or- der to modulate other switch neurons. As we show, the introduction of the switch modules enables the creation of sequences of gating events. This is achieved through the design of a modulatory pathway capable of exploring in a principled manner all permutations of the connections arriving on the switch neurons. We test the model by presenting appropriate architectures in nonstationary binary association problems and T-maze tasks. The results show that for all tasks, the switch neuron architectures generate optimal adaptive behaviors, providing evidence that the switch neuron model could be a valuable tool in simulations where behavioral plasticity is required. -
State Tournament Pairings Announced for Boys Basketball Indiana Pacers, Indiana Fever Are Presenting Sponsors of State Tournament
February 21, 2016 State Tournament Pairings Announced for Boys Basketball Indiana Pacers, Indiana Fever Are Presenting Sponsors of State Tournament Three hundred ninety‐eight (398) teams were drawn today and placed into brackets for the 106th Annual IHSAA Boys Basketball State Tournament presented by the Indiana Pacers and Indiana Fever. This is the third year that both of the state’s professional basketball franchises have been presenting sponsors of the boys and girls high school state tournaments. Sectional games are scheduled to begin Tuesday, March 1, and run through Saturday, March 5, with the regional round slated for March 12 and semi‐ states on March 19. The four state championship games that make up the state finals will be played at Bankers Life Fieldhouse in Indianapolis on Saturday, March 26. New Albany is the top‐ranked team in both the Associated Press Class 4A poll and the single class poll from the Indiana Basketball Coaches Association. The Bulldogs will begin their quest for a third consecutive sectional championship after earning a first round bye. They’ll await the Seymour‐New Albany winner in Sectional 15 at Seymour. Class 3A’s No. 1 team Griffith, last year’s state runner‐up, will host Hammond Clark in Sectional 17. In Class 2A, top‐ranked Lapel will play host to Muncie Burris in the six‐team Sectional 42 opener. Liberty Christian, the No. 1 team in the Class A poll, will begin its charge toward a fifth consecutive sectional title when it meets tournament newcomer Anderson Preparatory Academy in Sectional 55 at Wes‐Del. -
The Fading of False Light Invasion of Privacy
\\server05\productn\N\NYS\66-1\NYS112.txt unknown Seq: 1 12-MAY-10 8:28 TWILIGHT: THE FADING OF FALSE LIGHT INVASION OF PRIVACY ANDREW OSORIO* INTRODUCTION One hundred twenty years ago Samuel Warren and Lewis Bran- deis sowed the first seeds of America’s distinct privacy law in their groundbreaking treatise The Right to Privacy.1 Through their work, the pair argued that the common law could, and should, “protect those persons with whose affairs the community has no legitimate concern, from being dragged into an undesirable and undesired publicity and to protect all persons, whatsoever[ ] their position or station, from having matters that they may properly prefer to keep private, made public against their will.”2 Seventy years later William Prosser penned his article Privacy, wherein he differentiated and cataloged what he deemed to be the various limbs of the legal sap- ling planted by Warren and Brandeis.3 In so doing, Prosser suc- ceeded in grafting onto the law a new branch, which he termed “False Light in the Public Eye.”4 Dimly conceived, Prosser claimed that this “form of invasion of privacy . consists of publicity that places the plaintiff in a false light in the public eye.”5 Adopted by the American Law Institute (ALI) in the Restatement of Torts as “Publicity Placing Person in False Light,”6 this tort has faced near constant assault from scholars since its formal recognition.7 Just * New York University School of Law, J.D. Candidate, 2010; Pomona College, B.A., 2003. I would first like to thank Dr. -
The Question of Algorithmic Personhood and Being
Article The Question of Algorithmic Personhood and Being (Or: On the Tenuous Nature of Human Status and Humanity Tests in Virtual Spaces—Why All Souls Are ‘Necessarily’ Equal When Considered as Energy) Tyler Lance Jaynes Alden March Bioethics Institute, Albany Medical College, Albany, NY 12208, USA; [email protected] Abstract: What separates the unique nature of human consciousness and that of an entity that can only perceive the world via strict logic-based structures? Rather than assume that there is some potential way in which logic-only existence is non-feasible, our species would be better served by assuming that such sentient existence is feasible. Under this assumption, artificial intelligence systems (AIS), which are creations that run solely upon logic to process data, even with self-learning architectures, should therefore not face the opposition they have to gaining some legal duties and protections insofar as they are sophisticated enough to display consciousness akin to humans. Should our species enable AIS to gain a digital body to inhabit (if we have not already done so), it is more pressing than ever that solid arguments be made as to how humanity can accept AIS as being cognizant of the same degree as we ourselves claim to be. By accepting the notion that AIS can and will be able to fool our senses into believing in their claim to possessing a will or ego, we may yet Citation: Jaynes, T.L. The Question have a chance to address them as equals before some unforgivable travesty occurs betwixt ourselves of Algorithmic Personhood and Being and these super-computing beings. -
Model Sign Ordinance a Comprehensive, Content-Neutral Approach to Local Sign Control
Prepared by the Montgomery County Planning Commission A comprehensive, content-neutral approach to local sign control NewModel town Sign mixed Ordinance use district Montgomery County Commissioners Josh Shapiro, Chair Leslie S. Richards, Vice Chair Bruce L. Castor, Jr. MMontgomeryontgomery County Planning Commission Board Marc D. Jonas, Esq., Chair Dulcie F. Flaharty, Vice Chair Robert E. Blue, Jr. Jill Blumhardt Scott Exley Roy Rodriguez, Jr. Charles J. Tornetta Pastor John West Rachel Yoka Jody L. Holton, AICP, Executive Director Model Sign Ordinance A comprehensive, content-neutral approach to local sign control Prepared by the Montgomery County Planning Commission 2014 introduction ii model sign ordinance Table of Contents Introduction ........................................................................................................................................ vii Hot Topics in Signage Reference Guide ................................................................................ix Part 1: Purpose of Signs ............................................................................................................. 1 Part 2: Community Impact of Signs Safety Issues .......................................................................................................................... 9 Public Welfare and Aesthetics Issues .................................................................................. 10 Environmental Issues ..........................................................................................................