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Falsified Satellite Images in Deepfake Geography Seen as Security Threat

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Scientists who have identified a potential national security threat from deepfake geography, such as in false satellite images, are studying ways to identify them and take countermeasures. (Credit: Getty Images)

By John P. Desmond, AI Trends Editor

Deepfake is a portmanteau of “deep learning” and “fake”, and refers to a synthetic media usually in which a person in an existing image or video is replaced with someone else’s likeness. Deepfakes use techniques from machine learning and AI to manipulate visual and audio content with a high potential to deceive.

Deepfakes applied to geography have the potential to falsify satellite image data, which could pose a national security threat. Scientists at the University of Washington (UW) are studying this, in the hopes of finding ways to detect fake satellite images and warn of its dangers.

Bo Zhao, Assistant Professor of Geography, University of Washington

“This isn’t just Photoshopping things. It’s making data look uncannily realistic,” stated Bo Zhao, assistant professor of geography at the UW and lead author of the study, in a news release from the University of Washington. The study was published on April 21 in the journal Cartography and Geographic Information Science. “The techniques are already there. We’re just trying to expose the possibility of using the same techniques, and of the need to develop a coping strategy for it,” Zhao stated.

Fake locations and other inaccuracies have been part of mapmaking since ancient times, due to the nature of translating real-life locations to map form. But some inaccuracies in maps are created by the mapmakers to prevent copyright infringement.

National Geospatial Intelligence Agency Director Sounds Alarm

Now with the prevalence of geographic information systems, Google Earth and other satellite imaging systems, the spoofing involves great sophistication and carries more risks. The director of the federal agency in charge of geospatial intelligence, the National Geospatial Intelligence Agency (NGA), sounded the alarm at an industry conference in 2019.

“We’re currently faced with a security environment that is more complex, inter­connected, and volatile than we’ve experienced in recent memory—one which will require us to do things differently if we’re to navigate ourselves through it successfully,” stated NGA Director Vice Adm. Robert Sharp, according to an account from SpaceNews.

To study how satellite images can be faked, Zhao and his team at WU used an AI framework that has been used to manipulate other types of digital files. When applied to the field of mapping, the algorithm essentially learns the characteristics of satellite images from an urban area, then generates a deepfake image by feeding the characteristics of the learned satellite image characteristics onto a different base map. The researchers employed a generative adversarial network machine learning framework to achieve this.

The researchers combined maps and satellite images from three cities—Tacoma, Seattle and Beijing—to compare features and create new images of one city, drawn from the characteristics of the other two. The untrained eye may have difficulty detecting the differences between real and fake, the researchers noted. The researchers studied color histograms and frequency, texture, contrast, and spatial domains, to try to identify the fakes.

Simulated satellite imagery can serve a legitimate purpose when used to represent how an area is affected by climate change over time, for example. If there are no images for a certain period, filling in the gaps to provide perspective can provide perspective. The simulations need to be labeled as such.

The researchers hope to learn how to detect fake images, to help geographers develop data literacy tools, similar to fact-checking services. As technology continues to evolve, this study aims to encourage more holistic understanding of geographic data and information, so that we can demystify the question of absolute reliability of satellite images or other geospatial data, Zhao stated. “We also want to develop more future-oriented thinking in order to take countermeasures such as fact-checking when necessary,” he said.

In an interview with The Verge, Zhao stated the aim of his study “is to demystify the function of absolute reliability of satellite images and to raise public awareness of the potential influence of deep fake geography.” He stated that although deepfakes are widely discussed in other fields, his paper is likely the first to touch upon the topic in geography.

“While many GIS [geographic information system] practitioners have been celebrating the technical merits of deep learning and other types of AI for geographical problem-solving, few have publicly recognized or criticized the potential threats of deep fake to the field of geography or beyond,” stated the authors.

US Army Researchers Also Working on Deepfake Detection

Professor C.-C. Jay Kuo, Professor of Electrical and Computer Engineering, University of Southern California

US Army researchers are also working on a deepfake detection method. Researchers at the US Army Combat Capabilities Development Command, known as DEVCOM, Army Research Laboratory, in collaboration with Professor C.C. Jay Kuo’s research group at the University of Southern California, are examining the threat that deepfakes pose to our society and national security, according to a release from the US Army Research Laboratory (ARL).

Their work is featured in the paper titled “DefakeHop: A light-weight high-performance deepfake detector,” which will be presented at the IEEE International Conference on Multimedia and Expo 2021 in July.

ARL researchers Dr. Suya You and Dr. Shuowen (Sean) Hu noted that most state-of-the-art deepfake video detection and media forensics methods are based upon deep learning, which has inherent weaknesses in robustness, scalability, and portability.

“Due to the progression of generative neural networks, AI-driven deepfakes have advanced so rapidly that there is a scarcity of reliable techniques to detect and defend against them,” You stated. “We have an urgent need for an alternative paradigm that can understand the mechanism behind the startling performance of deepfakes, and to develop effective defense solutions with solid theoretical support.”

Relying on their experience with machine learning, signal analysis, and computer vision, the researchers developed a new theory and mathematical framework they call the Successive Subspace Learning, or SSL, as an innovative neural network architecture. SSL is the key innovation of DefakeHop, the researchers stated.

“SSL is an entirely new mathematical framework for neural network architecture developed from signal transform theory,” Kuo stated. “It is radically different from the traditional approach. It is very suitable for high-dimensional data that have short-, mid- and long-range covariance structures. SSL is a complete data-driven unsupervised framework, offering a brand-new tool for image processing and understanding tasks such as face biometrics.”

Read the source articles and information in a news release from the University of Washington, in the journal Cartography and Geographic Information Science,  an account from SpaceNews,a release from the US Army Research Laboratory, and in the paper titled “DefakeHop: A light-weight high-performance deepfake detector.”

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Source: https://www.aitrends.com/ai-in-science/falsified-satellite-images-in-deepfake-geography-seen-as-security-threat/

AI

OceanDAO Launches 7th Round of Grants, valued at $224K, for Data Science, Developer, AI Research Projects

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OceanDAO, a distributed autonomous organization supporting the Ocean Protocol, reveals that the 7th round is now open for submissions. More than $200,000 is being offered for Data Science, Developer, and AI Research projects according to a release shared with Crowdfund Insider.

During its first six months, OceanDAO has “made 49 grants to community projects,” the announcement noted while adding that more than 15M OCEAN tokens used were to vote in the funding initiative, “painting a promising picture of an autonomous future for the Ocean Protocol community.”

The announcement also mentioned that OceanDAO presents opportunities for public financing that’s open to data science and AI practitioners “interested in building and creating streams to sell and curate data.”

The release also noted:

“OceanDAO’s seventh round is now open for submissions with 400,000 OCEAN (valued at $224K USD) available and up to 32,000 OCEAN per project. Proposals are due by July 6th. The community voting period begins on July 8th. Interested parties can pitch project ideas and form teams on the OceanDAO Discord. More information on the submission process can be found on OceanDAO’s website. OceanDAO is the community funding initiative of Ocean Protocol, the data exchange protocol.”

The update pointed out that OceanDAO’s funding has managed to reach almost ½ million OCEAN tokens during the first six rounds since its launch. OceanDAO, the grants DAO to assist with funding Ocean Protocol community-curated initiatives, has reportedly made 49 allocations since December of last year, with its 7th round now taking submissions.

OceanDAO intends to expand the fast-evolving Ocean ecosystem, as “a key component in the Ocean’s near-term growth and long-term sustainability,” the release noted while adding that OceanDAO remains focused on making strategic investments in certain areas that can assist with expanding the Ocean Protocol ecosystem including: “building and improving applications or integrations to Ocean, community outreach, making data available on an Ocean-powered marketplace, building and improving Ocean core software, and improvements to the OceanDAO.”

Alex Napheys, OceanDAO Community & Growth Lead, stated:

“Our main goal is to support the long-term growth of the Ocean Protocol. The OceanDAO community is evolving monthly including some of the brightest and enthusiastic builders in the new data economy sector. The DAO aims to continually grow the [number] of projects it supports by onboarding the next wave to the OceanDAO community.”

As mentioned in the release, the community behind OceanDAO includes talented data scientists, engineers, builders, educators, and more. OceanDAO holds monthly rounds, during which teams are invited to apply for grants.

OceanDAO community regularly casts its votes for initiatives that aim to provide the best chance for growth and sustainability “based on the following criteria: return on investment towards growth and alignment with Ocean’s mission.”

Town Hall meetings are “held every week and are open to the public to discuss the status of projects and the future of the DAO,” the announcement confirmed.

OceanDAO backs initiatives across “all aforementioned categories with financial resources to meet their objectives.”

OceanDAO investments reportedly include:

  • DataUnion.app, the project “creates a two-sided market and economy for crowdsourced data to enable long and short-term benefits of AI for everyone.”
  • Rugpullindex.com, helping data scientists “to make better decisions when buying data online.”
  • Opsci Bay, an open science bay “for self-sovereign data flows from Lab to Market that is GDPR-compliant.”
  • Data Whale, a user-friendly “one-stop” solution that “helps data economy participants to understand the ecosystem and make smart staking decisions.”
  • ResilientML, will bring a vast collection of data sets “curated by experts in NLP for utilization directly in machine learning methods and sentiment models running in the Ocean environment and available through the Ocean marketplace.”

As noted in the release:

“As the projects drive traction in the Ocean ecosystem, it grows network fees and improves fundamentals for OCEAN, which in turn increases funds to OceanDAO available for future investments. This “snowball effect” is a core mechanism of the Web3 Sustainability Loop developed by Ocean Protocol Founder Trent McConaghy, in which both Network Revenue and Network Rewards are directed to work that is used for growth.”

Network Rewards help “to kickstart the project and to ensure funding. Network Revenue can help to push growth further once the Web3 project achieves traction at scale,” the announcement noted.

You may access the list of initiatives supported since OceanDAO’s launch here. OceanDAO has reportedly seen more than 60 proposals since December of last year, and all project proposals are publicly available to view online.

As previously reported, Ocean Protocol’s mission is to support a new Data Economy that “reaches the world, giving power back to data owners and enabling people to capture value from data to better our world.”

According to Ocean Protocol developers, data is like “a new asset class; Ocean Protocol unlocks its value.” Data owners and consumers use the Ocean Market app “to publish, discover, and consume data assets in a secure, privacy-preserving fashion.”

Ocean datatokens “turn data into data assets” and this enables data wallets, data exchanges, and data co-ops by “leveraging crypto wallets, exchanges, and other DeFi tools.” Projects use Ocean libraries and OCEAN in their own apps “to help drive the new Data Economy.”

The OCEAN token is used “to stake on data, govern Ocean Protocol’s community funding, and buy & sell data,” the announcement explained while confirming that its supply is “disbursed over time to drive near-term growth and long-term sustainability.” OCEAN has been designed “to increase with a rise in usage volume.”

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Source: https://www.crowdfundinsider.com/2021/06/176846-oceandao-launches-7th-round-of-grants-valued-at-224k-for-data-science-developer-ai-research-projects/

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AI Fraud Protection Firm Servicing Digital Goods nSure.ai Raises $6.8 Million Seed Round

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Israel-based nSure.ai has raised a $6.8 million Seed round led by DisruptiveAI, Phoenix Insurance, Kamet (an AXA backed VC), Moneta Seeds and other individual investors.

nSure.ai is a “predictive AI fraud protection company” that services digital goods such as gift cards, prepaid debit cards, software and game keys, digital wallet transfers, international money transfers, tickets, and more. The company explains that sellers of physical goods have processing times that allow them to double-check charges and can withhold a shipment if needed. Digital sellers lack this buffer, so even if fraud is detected minutes later, the assailant may be untraceable. nSure.ai is bringing anti-fraud technological and chargeback guarantees to the digital goods sector.

“We are thrilled that our investors have placed their trust in our leadership and confidence in nSure.ai,” says Alex Zeltcer, co-founder and CEO. “This investment enables us to register thousands of new merchants, who can feel confident selling higher-risk digital goods, without accepting fraud as a part of business.”

The founders of nSure.ai, Zeltcer and Ziv Isaiah say they experienced first-hand the unique challenges faced by retailers of digital assets. During the first week of operating their online gift card business, 40% of sales were fraudulent, resulting in chargebacks. nSure.ai’s 98% approval rate offers a more accurate fraud-detection strategy, allowing retailers to recapture nearly $100 billion a year in revenue lost by declining legitimate customers, according to Zeltcer.

Gadi Tirosh, Venture Partner at Disruptive AI, says they believe fraud, especially in the field of digital goods, can only be fought with top-of-the-line AI technologies.

“nSure.ai has both the technology and industry understanding to win this market.”

The funding is expected to be used to further develop nSure.ai’s predictive AI and machine learning algorithms. nSure.ai solution currently monitors and manages millions of transactions every month, and has approved close to $1B in volume since going live.

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Source: https://www.crowdfundinsider.com/2021/06/176867-ai-fraud-protection-firm-servicing-digital-goods-nsure-ai-raises-6-8-million-seed-round/

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AI enhanced Insurtech Tractable Acquires $60M via Series D Round led by Insight Partners, Georgian

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Tractable, the AI firm assisting insurers with accident and disaster recovery, recently revealed that it has acquired $60 million through a Series D round that was led by Insight Partners and Georgian.

Tractable’s latest investment round has now doubled the total raised by the firm from $55 million to $115 million and values the business at $1 billion – making it “the world’s first computer vision ‘unicorn’ for financial services,” according to a release.

As explained in the announcement:

“When drivers get into an accident, they (or their repairer) can submit photos of the damage to their insurer, which Tractable’s AI analyzes in real time to accelerate decisions that can otherwise take days, such as predicting whether the car is repairable, or assessing what repairs should take place. Over 20 of the global top 100 auto insurers use Tractable today to help their customers get back to normal faster after an accident.”

The proceeds from the investment round will serve to “double down” on accident recovery, which is the firm’s primary business. It will also fund new artificial intelligence solutions for accurately assessing the condition of an automobile, enabling users to understand car damage down to individual parts – “to enable transparent sale and purchase decisions.”

The release further noted that LKQ North America, the provider of alternative automobile parts and the largest automotive recycler in the world, currently uses Tractable’s AI “to optimize the recycling of end-of-life vehicles in North America.” The update also mentioned that automotive firms and auto leasing financial institutions will now be able “to benefit from the technology.”

Additionally, the round will be funding the application of Tractable’s tech to assess different homes and properties. As stated in the release, working cooperatively with a global insurer based in Japan, Tractable will help homeowners “recover faster from a typhoon by allowing them to submit photos and obtain an AI-accelerated claim payout.”

Tractable reports more than 600% revenue growth during the last 2 years, “in part through attracting new customers such as GEICO, the second-largest auto insurer in the US.” Other clients include Tokio Marine Nichido, Mitsui Sumitomo, Aioi Nissay Dowa and Sompo Japan, the four largest P&C insurers in Japan; Covéa, the largest auto insurer in France; Admiral Seguros, the Spanish entity of UK leader Admiral Group; and Ageas, a top UK insurer.

Alex Dalyac, CEO and founder of Tractable, stated:

“Six years ago we founded Tractable to bring the AI breakthrough in image classification to the real world. We cracked how to assess cars, helping over a million people recover from accidents, and helping recycle cars that couldn’t be repaired. We’ve turned $55M raised until now into $1B of valuation. And yet, there are other image recognition tasks out there, and more AI breakthroughs to come. Next up for us is homes.”

Lonne Jaffe, MD at Insight Partners and Tractable Board member, remarked:

“Tractable’s accelerating growth at scale is a testament to the power and differentiation of their applied machine learning system, which continues to improve as more businesses adopt it. We’re excited to double down on our partnership with Tractable as they work to help the world recover faster from accidents and disasters that affect hundreds of millions of lives.”

Emily Walsh, Partner at Georgian Partners, added:

“Tractable’s industry-leading computer vision capabilities are continuing to fuel incredible customer ROI and growth for the firm. We’re excited to continue to partner with Tractable as they apply their artificial intelligence capabilities to new, multi-billion dollar market opportunities in the used vehicle and natural disaster recovery industries.”

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Source: https://www.crowdfundinsider.com/2021/06/176840-ai-enhanced-insurtech-tractable-acquires-60m-via-series-d-round-led-by-insight-partners-georgian/

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Each of These Microscopic Glass Beads Stores an Image Encoded on a Strand of DNA

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Increasingly, civilization’s information is stored digitally, and that storage is abundant and growing. We don’t bother deleting those seven high-definition videos of the ceiling or 20 blurry photos of a table corner taken by our kid. There’s plenty of room on a smartphone or in the cloud, and we count on both increasing every year.

As we fluidly copy information from device to device, this situation seems durable. But that’s not necessarily true.

The amount of data we create is increasing rapidly. And if we (apocalyptically) lost the ability to produce digital storage devices—hard drives or magnetic tape, for example—our civilization’s collective digital record would begin to sprout holes within years. In decades, it’d become all but unreadable. Digital storage isn’t like books or stone tablets. It has a shorter expiration date. And, although we take storage for granted, it’s still expensive and energy hungry.

Which is why researchers are looking for new ways to archive information. And DNA, life’s very own “hard drive,” may be one solution. DNA offers incredibly dense data storage, and under the right conditions, it can keep information intact for millennia.

In recent years, scientists have advanced DNA data storage. They’ve shown how we can encode individual books, photographs, and even GIFs in DNA and then retrieve them. But there hasn’t been a scalable way to organize and retrieve large collections of DNA files. Until now, that is.

In a new Nature Materials paper, a team from MIT and Harvard’s Broad Institute describe a DNA-based storage system that allows them to search for and pull individual files—in this case images encoded in DNA. It’s a bit like thumbing through your file cabinet, reading the paper tabs to identify a folder, and then pulling the deed to your car from it. Only, obviously, the details are bit more complicated.

“We need new solutions for storing these massive amounts of data that the world is accumulating, especially the archival data,” said Mark Bathe, an MIT professor of biological engineering and senior author of the paper. “DNA is a thousandfold denser than even flash memory, and another property that’s interesting is that once you make the DNA polymer, it doesn’t consume any energy. You can write the DNA and then store it forever.”

How to Organize a DNA Storage System

How does one encode an image in a strand of DNA, anyway? It’s a fairly simple matter of translation.

Each pixel of a digital image is encoded in bits. These bits are represented by 1s and 0s. To convert it into DNA, scientists assign each of these bits to the DNA’s four base molecules, or nucleotides, adenine, cytosine, guanine, and thymine—usually referred to in shorthand by the letters A, C, G, and T. The DNA bases A and G, for example, could represent 1, and C and T could represent 0.

Next, researchers string together (or synthesize) a chain of DNA bases representing each and every bit of information in the original file. To retrieve the image, researchers reverse the process, reading the sequence of DNA bases (or sequencing it) and translating the data back into bits.

The standard retrieval process has a few drawbacks, however.

Researchers use a technique called a polymerase chain reaction (PCR) to pull files. Each strand of DNA includes an identifying sequence that matches a short sequence of nucleotides called a PCR primer. When the primer is added to the DNA solution, it bonds with matching DNA strands—the ones we want to read—and only those sequences are amplified (that is, copied for sequencing). The problem? Primers can interact with off-target sequences. Worse, the process uses enzymes that chew up all the DNA.

“You’re kind of burning the haystack to find the needle, because all the other DNA is not getting amplified and you’re basically throwing it away,” said Bathe.

MIT DNA data storage image microscopic glass beads
The microscopic glass spheres pictured here are DNA “files.” Each contains an image, encoded in DNA, and is coated in DNA tags describing the image within. Image Credit: Courtesy of the researchers (via MIT News)

To get around this, the Broad Institute team encapsulated the DNA strands in microscopic (6-micron) glass beads. They affixed short, single-stranded DNA labels to the surface of each bead. Like file names, the labels describe the bead’s contents. A tiger image might be labeled “orange,” “cat,” “wild.” A house cat might be labeled “orange,” “cat,” “domestic.” With just four labels per bead, you could uniquely label 1020 DNA files.

The team can retrieve specific files by adding complementary nucleotide sequences, or primers, corresponding to an individual file’s label. The primers contain fluorescent molecules, and when they link up with a complementary strand—that is, the searched-for label—they form a double helix and glow. Machines separate out the glowing beads, which are opened and the DNA inside sequenced. The rest of the DNA files remain untouched, left in peace to guard their information.

The best part of the method is its scalability. You could, in theory, have a huge DNA library stored in a test tube—Bathe notes a coffee mug of DNA could store all the world’s data—but without an easy way to search and retrieve the exact file you’re looking for, it’s worthless. With this method, everything can be retrieved.

George Church, a Harvard professor of genetics and well-known figure in the field of synthetic biology, called it a “giant leap” for the field.

“The rapid progress in writing, copying, reading, and low-energy archival data storage in DNA form has left poorly explored opportunities for precise retrieval of data files from huge…databases,” he said. “The new study spectacularly addresses this using a completely independent outer layer of DNA and leveraging different properties of DNA (hybridization rather than sequencing), and moreover, using existing instruments and chemistries.”

This Isn’t Coming For Your Computer

To be clear, all DNA data storage, including the work outlined in this study, remains firmly in the research phase. Don’t expect DNA hard drives for your laptop anytime soon.

Synthesizing DNA is still extremely expensive. It’d cost something like $1 trillion dollars to write a petabyte of data in DNA. To match magnetic tape, a common method of archival data storage, Bathe estimates synthesis costs would have to fall six orders of magnitude. Also, this isn’t the speediest technique (to put it mildly).

The cost of DNA synthesis will fall—the technology is being advanced in other areas as well—and with more work, the speed will improve. But the latter may be beside the point. That is, if we’re mainly concerned with backing up essential data for the long term with minimal energy requirements and no need to regularly access it, then speed is less important than fidelity, data density, and durability.

DNA already stores the living world’s information, now, it seems, it can do the same for all things digital too.

Image Credit: Courtesy of the researchers (via MIT News).

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Source: https://singularityhub.com/2021/06/20/each-of-these-microscopic-glass-beads-stores-an-image-encoded-on-a-strand-of-dna/

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