#Digital Note Fake Detector Machines | Explore Tumblr posts and blogs

another-dr-another · 4 years ago

Note

insert those coins babey! no point in holding onto them if they aren't used !

You Now Own:

001 - Mineral Water (x2)

Drawn from the ocean depths and rigorously purified. Ideal for a modern on-the-go public unsatisfied with tap water.

002 - Sea Salt

A basic seasoning produced from the evaporation of seawater. It also sees use as a preservative.

003 - Ration

A set of canned and vacuum-sealed foodstuffs. The taste isn't bad, and a certain snake that wants to play hide-and-go-seek is just crazy abou- wait, what?

005 - Ramune

A sweet, lemon-flavored carbonated drink. A marble plugs the opening of the uniquely designed bottle. The bottle can also be reused if you bring it to the ramune store.

010 - Ship In a Bottle

A intricate creation, a model ship within a bottle. Made with time, love and care.

012 - Envy-Enducing Envy CD

A CD of songs by Japanese band Envy. Full of unreleased tracks/first recordings.

014 - Children's Book

A children's book about family and forgiveness! The plot is about a sister who can't get along with her younger brother, but they find common ground and bond over causing trouble for their parents.

016 - Sour Soda (x2)

No flavor is listed on the bottle other than sour, which may just be a flavor in and of itself. It's a near black shade of blue... I think.

017 - Gentleman's Guide

A book that's meant to help shape you into the perfect gentleman. However, it's rather demeaning towards the reader, which doesn't feel very gentleman-ly.

018 - Masculine Cologne

Very masculine, can only be used by masculine people. No weaklings allowed, or people with the common sense to smell it before purchasing, and realize it smells really bad.

019 - Fancy Sword

True to it’s name, it looks very fancy, and very intimidating. However, it's only for show, and rather blunt- perhaps inexpensive?

022 - ??? Alcohol

It's something alcoholic. This is a school, it should be confiscated, and you won't be receiving any more information.

024 - Hair Cutting Scissors

Snip snip snip, meant for hair-cutting at home, as these aren’t professional grade. Still though, try and make it even, okay?

025 - Purple Hair Dye

Pretty purple hair dye guaranteed to not come out of hair for weeks! More of a pinkish-purple than the box advertised, but still pretty.

028 - Constellation Skirt

With patterns matching actual real constellations. Despite matching the night sky, it almost seems sun-rise themed, with its pink background and pale gold stars.

030 - Bottled Tea

When heated up, it's meant to help soothe upset stomachs, and muscle aches. Popular among student athletes.

031 - Alarm Clock

It's a digital alarm clock. One of the few normal and functional things here, and it's the thing that screams at you to wake up every morning.

032 - Broken Stopwatch (x2)

It won't stop running, no pausing or restarting. You can however make it record different laps.

034 - Baseball Cap

Perfect for keeping the sun out of your face! This one is all black though, so it'll retain a lot of heat.

038 - Card Game (x3)

One easy to play, and popular among kids. The front side of the package shows a family of four playing.

039 - Reminder Booklet

A small pamphlet that gives reminders for daily things, such as eating, drinking, taking meds, etc. Also has room for you to add in unique personalized reminders.

041 - Tiovita

A Japanese energy drink sold at most convenience stores. Pretty inexpensive, and with a nice fruity flavor- but hey, only one per day!

044 - Lie Detector (x2)

Fun for the whole family! Though not incredibly accurate... wait, how do you know that?

045 - Evidence Encylopedia

A book focusing on evidence found in crime scenes. From most overlooked to most common, this book talks about it all.

049 - Track Award

A award from a middle school track and field award. The recipient of the trophy seems to have come in second in two events, and first in one.

050 - Plane Tickets (x2)

Anywhere, anytime, round trip tickets. Probably given as some sort of thank you for volunteering to get off of a accidentally over-booked flight.

051 - Therapy Advertisement (x2)

Some therapist endorsing themselves. Upon looking at the services they offer, I don't feel very inspired to go there.

056 - Soulmate Sweatshirt

A sweatshirt that supposedly brings the most comfort not when you wear it, but when holding someone wearing it. Currently smells strongly of... lavender?

057 - Scrap Metal x3

Seems to be broken bits and pieces of some sort of engine. Could be repurposed, or simply a cool trinket.

059 - Old Journal

It seems to be from the late 80s, and kept being written in up to the early 90s. There's a entry on the last page, synopsizing the birth of the owners son, and how proud the owner is of his now five year old.

060 - Paper Boat

A piece of paper that's been folded into a boat. Apparently you can fold and tear it as you tell a story to provide a visual aid for the story, but no one here knows how.

061 - Calendar

It's got pictures of internationally famous towns on it! This particular one has been written on with a note on almost every day.

064 - Face-paint Kit.

A professional face-painting kit. However, it’s missing it’s red, yellow, blue and white paint- those colors have been all used up.

065 - Life Quote Sign (x3)

A sign with some stereotypical life quote written on it in flowery lettering. Most likely to be seen hanging in a kitchen.

066 - Throwing Rings (x2)

Meant for fair games. If you have good enough aim, maybe you'll win a prize!

067 - Pleasant Savior

Seemingly a CD filled with various performances by the same person. I haven't played the CD, so I don't know what kind of performances he does though, and the name is off-putting.

069 - “Fresh” Bouquet (x3)

Somehow still smells sweet with flowers that look flawless. It's comprised of roses that have been dyed rainbow, all of them.

070 - Hair Ribbons (x3)

They come in a variety of colors, but the Monomono Machine only dispenses yellow. Guaranteed to make the wearer feel a certain sense of self-satisfaction.

071 - Girls Profile

A student profile from a all-girls academy. The paper is water-stained and some of the ink has run, so it's hard to make out what's on the paper.

073 - Baby Doll

It seems to be from around the 90s and... not quite well-loved, but well-played-with. Doesn't come with the original clothes... or hair.

075 - Dream Catcher

Made by a past SHSL. It's actually been pretty effective, and is part of the reason they got scouted.

080 - Retro Game

It's handheld, old, and extremely broken. The screen has been shattered so it displays wrong, all cracked and distorted.

081 - Blackout Curtains (x2)

Completely block out any and all light. Strong enough to plunge a room into darkness.

084 - Noise-cancelling Headphones

They completely block out all sound! Also come with the ability to adjust the size of the band, and will stay on your ears even if you pull the band down to your neck.

086 - Wall Decals

Stickers you can put on your wall. They do a decent job of covering up holes in said walls.

087 - Antique Stuffed Animal (x2)

It seems to be bunny themed, and dressed in clothes you'd see on babies in the 1930s. It's in pretty good shape, other than a few tears where the lace trim at the end has had it’s stitches removed.

088 - Embroidery Kit

Or rather, a needle and thread to be used for embroidery. There's only one needle, and one spool of thread, but hey, it’s something.

090 - Scented Markers (x2)

A full rainbow set, all with their own unique smell! Be careful though - it's hard to get these out of clothes.

092 - Fake Christmas Tree (x2)

Too plastic to be a real tree. It's also incredibly small, but real trees can be small too, so that doesn’t really mess with the realism.

093 - Hair Gel

Top of the line hair gel, and completely unopened! Helps you style your hair and keep it in place, but doesn’t give it the nicest texture.

095 - Instant Noodles

Just add water to get something hot, salty, and/or spicy! A nice meal if you're looking for something that's quick and easy, you can dress it up some too.

097 - Drink Mix

A powder used for ??? warm drink, made with milk, tastes like... something? You try it and tell me, but it smells good at the least.

099 - The DSM-I

Self-explanatory, it's the original version of the DSM, from 1952. Index cards have been slipped in-between most of the pages, talking about what happened with the information listed there.

100 - Collection Of Old Ads

Dating back to the 1920s. A magazine full of ads from a different time, it’s somewhat of a miracle the paper held up while the ideas in it didn’t.

101 - Wooden Ruler

It's a wooden ruler. Used for measuring things, nothing else- why do you ask?

102 - Building Blocks (x3)

Stacking and stacking, and sending it all crumbling down. And then you rinse and repeat.

104 - Cutesy Hair Clips

Snap clips in pastel colors and covered in designs. Oddly enough, there isn't any non-pastels, unless you count the few white clips.

106 - Newspaper Collage

Seems to be a collection of snippets from newspaper articles. There must be hundreds in here... it's a big collage.

107 - Cropped Sweatshirt

Cropped specifically due to a parent saying not to. The sweatshirt seems to be related to some organization, with the big fancy emblem on it.

109 - Pins And Patches

A mix-and-match bag full of enamel pins, buttons, and iron-on patches. Good luck finding something to do with them all.

110 - Origami Paper (x2)

Simple origami paper, in a variety of colors and patterns! Comes easy to tear out of a book, which includes instructions on basic origami types.

112 - Colorful Band-Aids.

They come in many colors, designs, even different sizes. Some seem to be made to cover up paper cuts, others meant to help skinned knees and scraped elbows.

Thank you for visiting the Monomono Machine!

~*~

Maeda, narrating - And I thought the coins were kinda heavy...

Maeda - What now?

[Free Time Event - Uehara]

{Head to Your Room}

#dra #au #fangan #fanganronpa #yuki maeda #chapter 2 #daily life

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jsgroupcompany-blog · 5 years ago

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endenogatai · 5 years ago

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Sentinel loads up with $1.35M in the deepfake detection arms race

Estonia-based Sentinel, which is developing a detection platform for identifying synthesized media (aka deepfakes), has closed a $1.35 million seed round from some seasoned angle investors — including Jaan Tallinn (Skype), Taavet Hinrikus (Transferwise), Ragnar Sass & Martin Henk (Pipedrive) — and Baltics early stage VC firm, United Angels VC.

The challenge of building tools to detect deepfakes has been likened to an arms race — most recently by tech giant Microsoft, which earlier this month launched a detector tool in the hopes of helping pick up disinformation aimed at November’s US election. “The fact that [deepfakes are] generated by AI that can continue to learn makes it inevitable that they will beat conventional detection technology,” it warned, before suggesting there’s still short term value in trying to debunk malicious fakes with “advanced detection technologies”.

Sentinel co-founder and CEO, Johannes Tammekänd, agrees on the arms race point — which is why its approach to this ‘goal-post-shifting’ problem entails offering multiple layers of defence, following a cyber security-style template. He says rival tools — mentioning Microsoft’s detector and another rival, Deeptrace, aka Sensity — are, by contrast, only relying on “one fancy neural network that tries to detect defects”, as he puts it.

“Our approach is we think it’s impossible to detect all deepfakes with only one detection method,” he tells TechCrunch. “We have multiple layers of defence that if one layer gets breached then there’s a high probability that the adversary will get detected in the next layer.”

Tammekänd says Sentinel’s platform offers four layers of deepfake defence at this stage: An initial layer based on hashing known examples of in-the-wild deepfakes to check against (and which he says is scalable to “social media platform” level); a second layer comprised of a machine learning model that parses metadata for manipulation; a third that checks for audio changes, looking for synthesized voices etc; and lastly a technology that analyzes faces “frame by frame�� to look for signs of visual manipulation.

“We take input from all of those detection layers and then we finalize the output together [as an overall score] to have the highest degree of certainty,” he says.

“We already reached the point where somebody can’t say with 100% certainty if a video is a deepfake or not. Unless the video is somehow ‘cryptographically’ verifiable… or unless somebody has the original video from multiple angles and so forth,” he adds.

Tammekänd also emphasizes the importance of data in the deepfake arms race — over and above any specific technique. Sentinel’s boast on this front is that it’s amassed the “largest” database of in-the-wild deepfakes to train its algorithms on.

It has an in-house verification team working on data acquisition by applying its own detection system to suspect media, with three human verification specialists who “all have to agree” in order for it to verify the most sophisticated organic deepfakes.

“Every day we’re downloading deepfakes from all the major social platforms — YouTube, Facebook, Instagram, TikTok, then there’s Asian ones, Russian ones, also porn sites as well,” he says.

“If you train a deepfake model based on let’s say Facebook data-sets then it doesn’t really generalize — it can detect deepfakes like itself but it doesn’t generalize well with deepfakes in the wild. So that’s why the detection is really 80% the data engine.”

Not that Sentinel can always be sure. Tammekänd gives the example of a short video clip released by Chinese state media of a poet who it was thought has been killed by the military — in which he appeared to say he was alive and well and told people not to worry.

“Although our algorithms show that, with a very high degree of certainty, it is not manipulated — and most likely the person was just brainwashed — we can’t say with 100% certainty that the video is not a deepfake,” he says.

Sentinel’s founders, who are ex NATO, Monese and the UK Royal Navy, actually started working on a very different startup idea back in 2018 — called Sidekik — building a Black Mirror-esque tech which ingested comms data to create a ‘digital clone’ of a person in the form of a tonally similar chatbot (or audiobot).

The idea was that people could use this virtual double to hand off basic admin-style tasks. But Tammekänd says they became concerned about the potential for misuse — hence pivoting to deepfake detection.

They’re targeting their technology at governments, international media outlets and defence agencies — with early clients, after the launch of their subscription service in Q2 this year, including the European Union External Action Service and the Estonian Government.

Their stated aim is to help to protect democracies from disinformation campaigns and other malicious information ops. So that means they’re being very careful about who gets access to their tech. “We have a very heavy vetting process,” he notes. “For example we work only with NATO allies.”

“We have had requests from Saudi Arabia and China but obviously that is a no-go from our side,” Tammekänd adds.

A recent study the startup conducted suggests exponential growth of deepfakes in the wild (i.e. found anywhere online) — with more than 145,000 examples identified so far in 2020, indicating a ninefold year-on-year growth.

Tools to create deepfakes are certainly getting more accessible. And while plenty are, at face value, designed to offer harmless fun/entertainment — such as the likes of selfie-shifting app Reface — it’s clear that without thoughtful controls (including deepfake detection systems) the synthesized content they enable could be misappropriated to manipulate unsuspecting viewers.

Scaling up deepfake detection technology to the level of media swapping going on on social media platforms today is one major challenge Tammekänd mentions.

“Facebook or Google could scale up [their own deepfake detection] but it would cost so much today that they would have to put in a lot of resources and their revenue would obviously fall drastically — so it’s fundamentally a triple standard; what are the business incentives?” he suggests.

There is also the risk posed by very sophisticated, very well funded adversaries — creating what he describes as “deepfake zero day” targeted attacks (perhaps state actors, presumably pursuing a very high value target).

“Fundamentally it is the same thing in cyber security,” he says. “Basically you can mitigate [the vast majority] of the deepfakes if the business incentives are right. You can do that. But there will always be those deepfakes which can be developed as zero days by sophisticated adversaries. And nobody today has a very good method or let’s say approach of how to detect those.

“The only known method is the layered defence — and hope that one of those defence layers will pick it up.”

Sentinel co-founders, Kaspar Peterson (left) & Johannes Tammekänd (right). Photo Credit: Sentinel

It’s certainly getting cheaper and easier for any Internet user to make and distribute plausible fakes. And as the risks posed by deepfakes rise up political and corporate agendas — the European Union is readying a Democracy Action Plan to respond to disinformation threats, for example — Sentinel is positioning itself to sell not only deekfake detection but bespoke consultancy services, powered by learnings extracted from its deepfake data-set.

“We have a whole product — meaning we just don’t offer a ‘black box’ but also provide prediction explainability, training data statistics in order to mitigate bias, matching against already known deepfakes and threat modelling for our clients through consulting,” the startup tells us. “Those key factors have made us the choice of clients so far.”

Asked what he sees as the biggest risks that deepfakes pose to Western society, Tammekänd says, in the short term, the major worry is election interference.

“One probability is that during the election — or a day or two days before — imagine Joe Biden saying ‘I have a cancer, don’t vote for me’. That video goes viral,” he suggests, sketching one near term risk.

“The technology’s already there,” he adds noting that he had a recent call with a data scientist from one of the consumer deepfake apps who told him they’d been contacted by different security organizations concerned about just such a risk.

“From a technical perspective it could definitely be pulled off… and once it goes viral for people seeing is believing,” he adds. “If you look at the ‘cheap fakes’ that have already had a massive impact, a deepfake doesn’t have to be perfect, actually, it just has to be believable in a good context — so there’s a large number of voters who can fall for that.”

Longer term, he argues the risk is really massive: People could lose trust in digital media, period.

“It’s not only about videos, it can be images, it can be voice. And actually we’re already seeing the convergence of them,” he says. “So what you can actually simulate are full events… that I could watch on social media and all the different channels.

“So we will only trust digital media that is verified, basically — that has some method of verification behind that.”

Another even more dystopian AI -warped future is that people will no longer care what’s real or not online — they’ll just believe whatever manipulated media panders to their existing prejudices. (And given how many people have fallen down bizarre conspiracy rabbit holes seeded by a few textual suggestions posted online, that seems all too possible.)

“Eventually people don’t care. Which is a very risky premise,” he suggests. “There’s a lot of talk about where are the ‘nuclear bombs’ of deepfakes? Let’s say it’s just a matter of time when a deepfake of a politician comes out that will do massive damage but… I don’t think that’s the biggest systematic risk here.

“The biggest systematic risk is, if you look from the perspective of history, what has happened is information production has become cheaper and easier and sharing has become quicker. So everything from Gutenberg’s printing press, TV, radio, social media, Internet. What’s happening now is the information that we consume on the Internet doesn’t have to be produced by another human — and thanks to algorithms you can on a binary time-scale do it on a mass scale and in a hyper-personalized way. So that’s the biggest systematic risk. We will not fundamentally understand what is reality anymore online. What is human and what is not human.”

The potential consequences of such a scenario are myriad — from social division on steroids; so even more confusion and chaos engendering rising anarchy and violent individualism to, perhaps, a mass switching off, if large swathes of the mainstream simply decide to stop listening to the Internet because so much online contents is nonsense.

From there things could even go full circle — back to people “reading more trusted sources again”, as Tammekänd suggests. But with so much at shapeshifting stake, one thing looks like a safe bet: Smart, data-driven tools that help people navigate an ever more chameleonic and questionable media landscape will be in demand.

TechCrunch’s Steve O’Hear contributed to this report

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freshleadprovider · 5 years ago

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Why Nearly 20% of Young Consumers Didn’t Read Local Business Reviews in 2019

Every year, BrightLocal produces its flagship report on online reviews, the Local Consumer Review Survey, and in last year’s results we noticed some alarming trends that we felt worth further analysis and discussion. Of particular note are the stories the data tells us about the feelings and behaviors of those in the 18-34 age group (who, for the sake of simplicity, I’ll be referring to as ‘young people’ from here on in – especially as it makes a 37-year-old like me feel a little more youthful). Not only did young people’s responses to the survey often differ wildly from the average responses of other age groups, but at times they contradicted their own responses to earlier questions. Let’s take a deeper dive into the findings of the survey to see what insights we can glean, and what you can use to inspire your marketing strategies for young people. How Often Do Young People Search for Local Businesses Online? With web use steadily growing from year to year, and the prevalence of smartphones significantly contributing to it, one would expect that the frequency of searches for IRL businesses would trend higher as we become more reliant on it as a discovery tool in all parts of life. Not so with young people! In 2019, the percentage of young people who said they never used the internet to find local businesses rose to an astonishing 15% (from just 3% the previous year). As you can see above, decreases were seen across mid-range frequencies (e.g. at least once a week, once a month, 6-10 times per year) but these responses didn’t move over to the left side of the chart, as you’d expect with an increasing reliance on the web. Instead, there’s been a migration of responses from across the board to the far more apathetic response of ‘Never’. I should highlight that this question has been asked the same way, of the same representative group, year on year, and this is the first time we’ve seen a shift like this. (As a comparison, it’s worth noting that although the overall percentage of respondents (all age groups) who said they ‘never’ use the internet to find local businesses crept up from 8% in 2018 to 10% in 2019, the percentage that said they looked daily also leapt from 27% to 33%.) This statistic alone has left us with a lot of questions… Are young people finding other ways to seek out local businesses? Are recommendations from social media taking the place of original research and online reviews? Are young people even actively ‘seeking out’ local businesses? Have the prevalence of influencer marketing and targeted advertising simply turned social media into a brand recommendation delivery machine, to the point that young people don’t, for example, choose to go to a restaurant and look for one, and instead have one in mind from the offset? As they’re less likely to need infrequent services (like plumbing, lawyers, doctors), is the idea of a ‘local business’ reduced to shopping, entertainment and eating out? Having been raised on the internet, are they more likely to instinctively purchase from businesses online rather than shopping locally, as research from Invesp suggests? Or is it simply a question of semantics? Have the meanings of ‘use the internet’ and ‘local businesses’ slightly changed for young people? Do they now perceive ‘use the internet’ to mean using a web browser (rather than an app like Yelp or Google Maps) and ‘local businesses’ to mean ‘businesses in the town where I live’? Has this change in meaning led to this change in results? There’s a lot here to chew on, I’m sure you’ll agree. We’d be fascinated to hear your thoughts and theories in the comments below. Do Young People Read and Trust Online Reviews? Now that we’ve taken a look at young people’s use of the internet to discover local businesses (and come away somewhat shattered and disillusioned), let’s dive into the meat of the survey: their use of, and attitude towards, online reviews. This time around, many of those who had previously read online reviews ‘regularly’ or ‘occasionally’ are instead ‘never’ reading online reviews. In itself, this is a huge surprise (especially considering what you’ll learn a little later in this piece) but put in the context of the previous chart, it makes perfect sense: a big increase in young people not looking for local businesses online must naturally lead to a similar increase in those not reading reviews for local businesses. Still, that’s a bit of an increase in young people who ‘always’ read online reviews for local businesses (50% up to 52%), so the cause isn’t by any means lost. We’re perhaps merely seeing the emergence of two separate groups in this demographic of digital natives: those who have grown up to trust the online world and those who have learnt to be far more wary and distrusting of it. The emergence of discourse around the negative effects of social media and wider online practices on younger generations certainly bears this theory out. Taking this theory further, let’s see what happened when we asked those young people who did say they read online reviews to tell us how much they trusted them. And it’s the same story… a few more young people becoming entirely trusting of online reviews (from 39% to 41%), but a huge jump of 13% saying they ‘don’t trust online reviews at all’. Quite aside from making me scream, red-faced, into the void, “Then why are you reading them?!” this reaffirms my belief that we might be seeing the two diametric experiences that can come from a youth growing up with the web. It’s a broad thing to hypothesize considering the limited data in front of me, but I can’t help but think that, for young people at least, the movement of responses in the above charts from the middle ground to the left and right is indicative of a society in which the gray area is disappearing and online experiences and discourse are trending towards black/white extremes. Sadly, it looks like fake reviews aren’t helping the problem. Here we can see more young people than ever before being confident in spotting fake reviews online and seeing them regularly. One could certainly make the argument that the lack of trust expressed in the previous chart strongly suggests that this is just a very distrusting generation, perhaps battle-hardened by scrapes on social media, and that the number of fake reviews online hasn’t grown significantly, but by pretty much all accounts, fake reviews are indeed a growing problem. My hunch is, instead, that young people today are just more web-savvy and aware of the kinds of dodgy online tactics businesses are willing to employ to get your custom. How Much Are Young People Engaging with Online Reviews? As you’ve seen, there’s a consistent trend across many of our survey questions in which responses from young people are starting to skew towards the negative and distrusting. This might paint a picture of apathy, of a youth spent viewing the online world through an increasingly skeptical lens, but interestingly this hasn’t led to a disengagement with online reviews. Rather, the reverse is true. As our survey shows, young people who read online reviews for local businesses read 30% more reviews than the average consumer before trusting a local business, and spend 35% more time reading them than the average consumer. This fits in with the narrative of a digital native with a discerning eye, their bullcrap-detector permanently on. It seems that although some young people take their distrust of online reviews and choose never to engage with them, some go the opposite way and spend more time than any other age group combing through the reams of reviews left by other consumers. How Can Local Businesses React? Whichever way you look at the data above, you can’t deny we’re seeing some fascinating swings in the attitudes of young people. Whether 2019 was just a bad year for trust in, and use of, online reviews (or whether it’s a quirk in the data that will rectify itself next year) remains to be seen, but I for one will certainly be monitoring the perception of this key marketing tactic as the year goes on. But if your local business primarily deals with young people, what can you do to ensure you’re still attracting them in 2020? Try to get a presence on top lists in your local area, as these can dominate the organic results of local-intent searches. Get in the lists of top things to do, the top restaurants, etc. And if you can, make sure the lists are on websites where young people are a prime audience. Get more active on social media, and consider social media advertising on Instagram in particular. Facebook’s trending towards older age groups these days, and Twitter has never been a great place for local businesses to advertise, but Instagram is your holy grail if you can easily sell your service or products on a good image or short video. Use in-store tactics to get your audience to share their visit with you. Encourage the use of a hashtag, run a social media competition, or create an attractive and quirky ‘Instagram wall’ in your store or shop to make it a can’t-miss selfie opportunity. It looks like 2020 might be a bumpy year for young people and reviews, but I hope some of the ideas and theories above help you to weather the storm! The post Why Nearly 20% of Young Consumers Didn’t Read Local Business Reviews in 2019 appeared first on BrightLocal. https://probdm.com/site/NzUxMA

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ruthahansentx · 6 years ago

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Better fact-checking for fake news

The 21st century has opened up a boundless mass of headlines, articles, and stories. This information influx, however, is partially contaminated: Alongside factual, truthful content is fallacious, deliberately manipulated material from dubious sources. According to research by the European Research Council, one in four Americans visited at least one fake news article during the 2016 presidential campaign.

This problem has recently been exacerbated by something called “automatic text generators.” Advanced artificial intelligence software, like OpenAI's GPT-2 language model, is now being used for things like auto-completion, writing assistance, summarization, and more, and it can also be used to produce large amounts of false information — fast.

To mitigate this risk, researchers have recently developed automatic detectors that can identify this machine-generated text.

However, a team from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) found that this approach was incomplete.

To prove this, the researchers developed attacks that they showed could fool state-of-the-art fake-news detectors. Since the detector thinks that the human-written text is real, the attacker cleverly (and automatically) impersonates such text. In addition, because the detector thinks the machine-generated text is fake, it might be forced to also falsely condemn totally legitimate uses of automatic text generation.

But how can the attackers automatically produce “fake human-written text”? If it’s human-written, how can it be automatically produced?

The team came up with the following strategy: Instead of generating the text from scratch, they used the abundance of existing human-written text, but automatically corrupted it to alter its meaning. To maintain coherence, they used a GPT-2 language model when performing the edits, demonstrating that its potential malicious uses are not limited to generating text.

“There’s a growing concern about machine-generated fake text, and for a good reason,” says CSAIL PhD student Tal Schuster, lead author on a new paper on their findings. “I had an inkling that something was lacking in the current approaches to identifying fake information by detecting auto-generated text — is auto-generated text always fake? Is human-generated text always real?”

In one experiment, the team simulated attackers that use auto-completion writing assistance tools similar to legitimate sources. The legitimate source verifies that the auto-completed sentences are correct, whereas the attackers verify that they’re incorrect.

For example, the team used an article about NASA scientists describing the collection of new data on coronal mass ejections. They prompted a generator to produce information on how this data is useful. The AI gave an informative and fully correct explanation, describing how the data will help scientists to study the Earth’s magnetic fields. Nevertheless, it was identified as “fake news.” The fake news detector could not differentiate fake from real text if they were both machine-generated.

“We need to have the mindset that the most intrinsic ‘fake news’ characteristic is factual falseness, not whether or not the text was generated by machines,” says Schuster. “Text generators don’t have a specific agenda — it’s up to the user to decide how to use this technology.”

The team notes that, since the quality of text generators is likely to keep improving, the legitimate use of such tools will most likely increase — another reason why we shouldn’t “discriminate” against auto-generated text.

“This finding of ours calls into question the credibility of current classifiers in being used to help detect misinformation in other news sources,” says MIT Professor Regina Barzilay.

Schuster and Barzilay wrote the paper alongside Roei Schuster from Cornell Tech and Tel Aviv University, as well as CSAIL PhD student Darsh Shah.

Bias in AI is nothing new — our stereotypes, prejudices, and partialities are known to affect the information that our algorithms hinge on. A sample bias could ruin a self-driving car if there’s not enough nighttime data, and a prejudice bias could unconsciously reflect personal stereotypes. If these predictive models learn based on the data they’re given, they’ll undoubtedly fail to understand what’s true or false.

With that in mind, in a second paper, the same team from MIT CSAIL used the world’s largest fact-checking dataset, Fact Extraction and VERification (FEVER), to develop systems to detect false statements.

FEVER has been used by machine learning researchers as a repository of true and false statements, matched with evidence from Wikipedia articles. However, the team’s analysis showed staggering bias in the dataset — bias that could cause errors in models it was trained on it.

“Many of the statements created by human annotators contain giveaway phrases,” says Schuster. “For example, phrases like ‘did not’ and ‘yet to’ appear mostly in false statements.”

One bad outcome is that models trained on FEVER viewed negated sentences as more likely to be false, regardless of whether they were actually true.

“Adam Lambert does not publicly hide his homosexuality,” for instance, would likely be declared false by fact-checking AI, even though the statement is true, and can be inferred from the data the AI is given. The problem is that the model focuses on the language of the claim, and doesn’t take external evidence into account.

Another problem of classifying a claim without considering any evidence is that the exact same statement could be true today, but be considered false in the future. For example, until 2019 it was true to say that actress Olivia Colman had never won an Oscar. Today, this statement could be easily refuted by checking her IMDB profile.

With that in mind, the team created a dataset that corrects some of this through de-biasing FEVER. Surprisingly, they found that the models performed poorly on their unbiased evaluation sets, with results dropping from 86 percent to 58 percent.

“Unfortunately, the models seem to overly rely on the bias that they were exposed to, instead of validating the statements in the context of given evidence,” says Schuster.

Armed with the debiased dataset, the team developed a new algorithm that outperforms previous ones across all metrics.

“The algorithm down-weights the importance of cases with phrases that were specifically common with a corresponding class, and up-weights cases with phrases that are rare for that class,” says Shah. “For example, true claims with the phrase ‘did not’ would be upweighted, so that in the newly weighted dataset, that phrase would no longer be correlated with the ‘false’ class.”

The team hopes that, in the future, combining fact-checking into existing defenses will make models more robust against attacks. They aim to further improve existing models by developing new algorithms and constructing datasets that cover more types of misinformation.

“It's exciting to see research on detection of synthetic media, which will be an increasingly key building block of ensuring online security going forward as AI matures,” says Miles Brundage, a research scientist at OpenAI who was not involved in the project. “This research opens up AI's potential role in helping to address the problem of digital information, by teasing apart the roles of factual accuracy and provenance in detection.”

A paper on the team’s contribution to fact-checking, based on debiasing, will be presented at the Conference on Empirical Methods in Natural Language Processing in Hong Kong in October. Schuster wrote the paper alongside Shah, Barzilay, Serene Yeo from DSO National Laboratories, MIT undergraduate Daniel Filizzola, and MIT postdoc Enrico Santus.

This research is supported by Facebook AI Research, who granted the team the Online Safety Benchmark Award.

from Technology News And Updates http://news.mit.edu/2019/better-fact-checking-fake-news-1017

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mathematicianadda · 6 years ago

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A Few Thoughts about Deep Fakes

Someone from the House Permanent Select Committee on Intelligence recently contacted me about a hearing they’re having on the subject of deep fakes. I can’t attend the hearing, but the conversation got me thinking about the subject of deep fakes, and I made a few quick notes….

What You See May Not Be What Happened

The idea of modifying images is as old as photography. At first, it had to be done by hand (sometimes with airbrushing). By the 1990s, it was routinely being done with image manipulation software such as Photoshop. But it’s something of an art to get a convincing result, say for a person inserted into a scene. And if, for example, the lighting or shadows don’t agree, it’s easy to tell that what one has isn’t real.

What about videos? If one does motion capture, and spends enough effort, it’s perfectly possible to get quite convincing results—say for animating aliens, or for putting dead actors into movies. The way this works, at least in a first approximation, is for example to painstakingly pick out the keypoints on one face, and map them onto another.

What’s new in the past couple of years is that this process can basically be automated using machine learning. And, for example, there are now neural nets that are simply trained to do “face swapping”:

In essence, what these neural nets do is to fit an internal model to one face, and then apply it to the other. The parameters of the model are in effect learned from looking at lots of real-world scenes, and seeing what’s needed to reproduce them. The current approaches typically use generative adversarial networks (GANs), in which there’s iteration between two networks: one trying to generate a result, and one trying to discriminate that result from a real one.

Today’s examples are far from perfect, and it’s not too hard for a human to tell that something isn’t right. But even just as a result of engineering tweaks and faster computers, there’s been progressive improvement, and there’s no reason to think that within a modest amount of time it won’t be possible to routinely produce human-indistinguishable results.

Can Machine Learning Police Itself?

OK, so maybe a human won’t immediately be able to tell what’s real and what’s not. But why not have a machine do it? Surely there’s some signature of something being “machine generated”. Surely there’s something about a machine-generated image that’s statistically implausible for a real image.

Well, not naturally. Because, in fact, the whole way the machine images are generated is by having models that as faithfully as possible reproduce the “statistics” of real images. Indeed, inside a GAN there’s explicitly a “fake or not” discriminator. And the whole point of the GAN is to iterate until the discriminator can’t tell the difference between what’s being generated, and something real.

Could one find some other feature of an image that the GAN isn’t paying attention to—like whether a face is symmetric enough, or whether writing in the background is readable? Sure. But at this level it’s just an arms race: having identified a feature, one puts it into the model the neural net is using, and then one can’t use that feature to discriminate any more.

There are limitations to this, however. Because there’s a limit to what a typical neural net can learn. Generally, neural nets do well at tasks like image recognition that humans do without thinking. But it’s a different story if one tries to get neural nets to do math, and for example factor numbers.

Imagine that in modifying a video one has to fill in a background that’s showing some elaborate computation—say a mathematical one. Well, then a standard neural net basically doesn’t stand a chance.

Will it be easy to tell that it’s getting it wrong? It could be. If one’s dealing with public-key cryptography, or digital signatures, one can certainly imagine setting things up so that it’s very hard to generate something that is correct, but easy to check whether it is.

But will this kind of thing show up in real images or videos? My own scientific work has actually shown that irreducibly complex computation can be quite ubiquitous even in systems with very simple rules—and presumably in many systems in nature. Watch a splash in water. It takes a complex computation to figure out the details of what’s going to happen. And while a neural net might be able to get something that basically looks like a splash, it’d be vastly harder for it to get the details of a particular splash right.

But even though in the abstract computational irreducibility may be common, we humans, in our evolution and the environments we set up for ourselves, tend to end up doing our best to avoid it. We have shapes with smooth curves. We build things with simple geometries. We try to make things evolvable or understandable. And it’s this avoidance of computational irreducibility that makes it feasible for neural nets to successfully model things like the visual scenes in which we typically find ourselves.

One can disrupt this, of course. Just put in the picture a display that’s showing some sophisticated computation (even, for example, a cellular automaton). If someone tries to fake some aspect of this with a neural net, it won’t (at least on its own) feasibly be able to get the details right.

I suspect that in the future of human technology—as we mine deeper in the computational universe—irreducible computation will be much more common in what we build. But as of now, it’s still rare in typical human-related situations. And as a result, we can expect that neural nets will successfully be able to model what’s going on well enough to at least fool other neural nets.

How to Know What’s Real

So if there’s no way to analyze the bits in an image to tell if it’s a real photograph, does that mean we just can’t tell? No. Because we can also think about metadata associated with the image—and about the provenance of the image. When was the image created? By whom? And so on.

So let’s say we create an image. How can we set things up so that we can prove when we did it? Well, in modern times it’s actually very easy. We take the image, and compute a cryptographic hash from it (effectively by applying a mathematical operation that derives a number from the bits in the image). Then we take this hash and put it on a blockchain.

The blockchain acts as a permanent ledger. Once we’ve put data on it, it can never be changed, and we can always go back and see what the data was, and when it was added to the blockchain.

This setup lets us prove that the image was created no later than a certain time. If we want to prove that the image wasn’t created earlier, then when we create the hash for the image, we can throw in a hash from the latest block on our favorite blockchain.

OK, but what about knowing who created the image? It takes a bit of cryptographic infrastructure—very similar to what’s done in proving the authenticity of websites. But if one can trust some “certificate authority” then one can associate a digital signature to the image that validates who created it.

But how about knowing where the image was taken? Assuming one has a certain level of access to the device or the software, GPS can be spoofed. If one records enough about the environment when the image was taken, then it gets harder and harder to spoof. What were the nearby Wi-Fi networks? The Bluetooth pings? The temperature? The barometric pressure? The sound level? The accelerometer readings? If one has enough information collected, then it becomes easier to tell if something doesn’t fit.

There are several ways one could do this. Perhaps one could just detect anomalies using machine learning. Or perhaps one could use actual models of how the world works (the path implied by the accelerometer isn’t consistent with the equations of mechanics, etc.). Or one could somehow tie the information to some public computational fact. Was the weather really like that in the place the photo was said to be taken? Why isn’t there a shadow from such-and-such a plane going overhead? Why is what’s playing on the television not what it should be? Etc.

But, OK, even if one just restricts oneself to creation time and creator ID, how can one in practice validate them?

The best scheme seems to be something like how modern browsers handle website security. The browser tries to check the cryptographic signature of the website. If it matches, the browser shows something to say the website is secure; if not, it shows some kind of warning.

So let’s say an image comes with data on its creation time and creator ID. The data could be metadata (say EXIF data), or it could be a watermark imprinted on the detailed bits in the image. Then the image viewer (say in the browser) can check whether the hash on a blockchain agrees with what the data provided by the image implies. If it does, fine. And the image viewer can make the creation time and creator ID available. If not, the image viewer should warn the user that something seems to be wrong.

Exactly the same kind of thing can be done with videos. It just requires video players computing hashes on the video, and comparing to what’s on a blockchain. And by doing this, one can guarantee, for example, that one’s seeing a whole video that was made at a certain time.

How would this work in practice? Probably people often wouldn’t want to see all the raw video taken at some event. But a news organization, for example, could let people click through to it if they wanted. And one can easily imagine digital signature mechanisms that could be used to guarantee that an edited video, for example, contained no content not in certain source videos, and involved, say, specified contiguous chunks from these source videos.

The Path Forward

So, where does this leave us with deep fakes? Machine learning on its own won’t save us. There’s not going to be a pure “fake or not” detector that can run on any image or video. Yes, there’ll be ways to protect oneself against being “faked” by doing things like wearing a live cellular automaton tie. But the real way to combat deep fakes, I think, is to use blockchain technology—and to store on a public ledger cryptographic hashes of both images and sensor data from the environment where the images were acquired. The very presence of a hash can guarantee when an image was acquired; “triangulating” from sensor and other data can give confidence that what one is seeing was something that actually happened in the real world.

Of course, there are lots of technical details to work out. But in time I’d expect image and video viewers could routinely check against blockchains (and “data triangulation computations”), a bit like how web browsers now check security certificates. And today’s “pics or it didn’t happen” will turn into “if it’s not on the blockchain it didn’t happen”.

from Stephen Wolfram Blog http://bit.ly/2X3fQzD from Blogger http://bit.ly/2IEJ0f1

#Blogger

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