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NEW YORK, March 24, 2020 /PRNewswire/ -- The global natural language processing (NLP) market was valued at USD 10.93 billion in 2019, and it is expected to be worth USD 34.80 billion by 2025, registering a CAGR of 21.5% during 2020-2025. Over the past few years, deep learning architectures and algorithms have made impressive advances in the fields of text analytics. Most of the marketing agencies are adopting text analytics solutions to enhance their marketing programs. The growing trend for mobile marketing is developing space for the studied segment to expand over the forecast period. Product innovation is still a significant trend in the text analytics market, which is mainly helping the market vendors to expand the customer base. For instance, Rosoka Software launched the analyst's notebook, Rosoka Text Analytics, which can be used to analyze unstructured documents in over 200 languages. This is expected to boost the company's audience reach, thereby fueling the financials.

- The shifting trend from product-centric to customer-centric experience drives the market. The usage of the internet and an ever-expanding means of communication, consumption, and interaction has empowered consumers. Companies have been forced to rethink their branding and business models.

Key Market Trends

- Speech analytics solutions are gaining popularity among enterprises across the world since the conventional text-based analytics solutions adopted by enterprises is no longer enough to handle complex business issues. Many organizations are deploying speech analytics through a combination of internally recorded data, social media data, and external syndicated data, mainly to have a better understanding of their customer requirements.

Asia-Pacific to Witness the Highest Market Growth

- The Asia-Pacific region is one of the most potential markets for the NLP industry. The region is also witnessing an increasing rate of adoption of the AI and ML technologies, especially among SMEs in the region. The voice assistance market is also booming in Asia, and the region is home to many consumer electronics manufacturers, including smart speakers and smartphones.

Competitive Landscape

Reasons to Purchase this report:
- The market estimate (ME) sheet in Excel format
- 3 months of analyst support

[Source: This article was published in oleantimesherald.com By Reportlinker - Uploaded by the Association Member: Edna Thomas]

Categorized in Market Research

Private.sh is a new private search engine that uses cryptography to ensure that your search history cannot be tracked by anyone – even us. Private.sh comes from the same privacy committed makers of Private Internet Access in partnership with GigaBlast – one of the few companies to have their own index of the internet available for search.

This first truly private search engine is possible thanks to the partnership, with each partner playing a key part of the puzzle to provide a new standard for cryptographically secured privacy for search engines.

Chances are, your current search engine is not private

One of the core functions and business models of a search engine is tracking searches and who makes them. The vast majority of search engines are taking personally identifiable information like your IP address and browser fingerprints to create or add to a profile. Besides keeping a log of what you’ve searched for, non-private search engines also keep a log of what result you clicked on, how long it took, whether you were feeling lucky, etc.

With non-private search engines, being able to identify you – the searcher – and tie your search terms to your user profile while targeting advertising at you is all an essential part of the business model.

That is to say, with normal search engines, your search’s privacy is ignored and trampled on by design. With Private.sh, your privacy is protected by design.

With Private.sh, your search privacy is protected with both encryption and anonymity.

When you enter a search term into Private.sh, the search term gets encrypted on the client side (on your computer or device) using GigaBlast’s public key, which only they can decrypt. In effect, this ensures that Private.sh never sees the search term.

After the search term is encrypted, it is passed to the GigaBlast search engine through a Private.sh proxy so GigaBlast doesn’t see your IP address, browser fingerprints, or anything that would allow for your privacy to be broken or a user profile to be created. This means that neither Private.sh or GigaBlast is able to build a user profile on you or store your search history.

Finally, the search results are encrypted by GigaBlast using your temporary public key and are returned to you through the Private.sh proxy. The results then get decrypted and rendered locally on your device using Javascript with a temporary private key that only exists on your device. This client-side keypair is changed for every search request.

With this multi-pronged approach, Private.sh is the perfect option when you need to search something privately.

Private Search is finally here, and improving

Another benefit is that since Private.sh and GigaBlast are both unable to build a user profile on you, you’ll be able to get unbiased and private search results every time. You may notice that these results aren’t as accurate or algorithmically tailored to you based on your search history. This is by design and makes Private.sh a perfect complement to your favorite search engine, for when you want to make a search that is truly private.

We will constantly be working to better your search experience without compromising your security. In the future, Private.sh will be working with GigaBlast to expand their index of the internet to bring more results. Private.sh is determined to be your private search engine of choice.

Try private search out and add it to your privacy toolkit today.

 [Source: This article was published in privateinternetaccess.com - Uploaded by the Association Member: Joshua Simon]

Categorized in Search Engine

Today startup Kipwise, the creator of a new ‘search engine’ knowledge management tool for companies, has raised €400K from Icebreaker.vc, Techstars London and angel investors from Germany.

Founded in 2018 by Kwun-Lok Ng (based in Tallin, Estonia), Charlie Mak and Amanda Wong, Kipwise aims to build a ‘Google search engine’ to organise the internal knowledge of companies. Ensuring good knowledge sharing is vital to the success of a company – it impacts the speed of onboarding, team productivity and prevents knowledge leaks when employees leave. However, when mentioning ‘knowledge management’, words that one often associates the phrase with are – boring, tedious, time-consuming and the list goes on. 

“It sounds boring because the process is tedious. That’s why we’re redefining knowledge management,” says CEO and co-founder Kwun-Lok Ng on why he and his co-founders started Kipwise. “We spoke to more than 200 companies and found that over 90% are unhappy with the knowledge management tool that they’re using. The tools are clumsy to use, detached from your daily workflow, require lots of manual effort to save knowledge and have limited  search capabilities.”  

The way teams communicate has changed a lot in recent years. Nowadays, teams often rely on multiple tools to get their job done and it’s time-consuming and distracting to document important knowledge from the fragmented communication. 

Kipwise simplifies the whole flow by integrating with tools that companies use every day, such as Slack, Google Drive, Chrome and Trello. Kipwise serves as a layer on top of all your tools, so you can save new knowledge and access saved knowledge directly within your daily workflow, with just a few clicks. This way, even the busiest person in your team can contribute to knowledge management with minimal effort. 

Another trend that makes better knowledge management so important is the rise of remote work, especially now during Coronavirus mitigation measures. Kipwise truly understands these challenges because they are a 100% remote team with teammates in Estonia, Brazil, Croatia, Taiwan and Hong Kong.  “Even us three co-founders have been working remotely since the beginning,” says Kwun. “Although remote working has now become common, many people are still skeptical about remote co-founders. Prior to starting Kipwise, we’ve experienced working together for 4+ years building a startup from scratch, so we are very familiar with each other’s working style and that has made our collaboration much easier.” 

Currently, Kipwise has customers from over 53 different countries. Around 83% of managers and seniors engage with their tool weekly, and distributed/remote teams account for 66% of their customer base.

Just like its team composition, the journey behind Kipwise is global. With the team’s strong experience in growing B2B SaaS innovations internationally, Kipwise has attracted investments from Techstars London, and now more recently Icebreaker.vc and two German angel investors. It aims to use the fresh funds to expand its product to teams all over the world, remote and in-office, and help them work more efficiently.

[Source: This article was published in eu-startups.com By Charlotte Tucker - Uploaded by the Association Member: Carol R. Venuti]

Categorized in Search Engine

Google published a new Search Console training video all about how to use the index coverage report.

Google’s Daniel Waisberg explains how to use Search Console to learn which pages have been crawled and indexed by Google, and how to deal with any problems found during that process.

First, the video gives an overview of the different components of the index coverage report and how to read the data included in them.

What’s Contained in the Index Coverage Report?

Search Console’s index coverage report provides a detailed look at all pages of a website that Google has either indexed or tried to index. The report also logs all errors Googlebot encountered when crawling a page.

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The index coverage report is made up of the following components:

  • Errors: These are critical issues that prevent pages from being indexed. Errors could include pages with the ‘noindex’ directive, pages with a server error, or pages with a 404 error.
  • Valid with warnings: This section includes pages that may or may not be shown in search results depending on the issue. An example is an indexed page that’s blocked by robots.txt.
  • Valid: These are indexed pages that are eligible to be served in search results.
  • Excluded: These are pages that are intentionally not indexed and won’t be included in search results.

On the summary page of the index coverage report you will also see a checkbox you can click to show impressions for indexed pages in Google search.

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How Should I Use The Index Coverage Report?

It’s recommended that site owners start by checking the chart on the summary page to learn if the valid pages trend is somewhat steady. Some amount of fluctuation is normal here. If you’re aware of content being published or removed you will see that reflected in the report.

Next, move onto reviewing the various error sections. You can quickly identity the most pressing issues because they’re sorted by severity. Start at the top of the list and work your way down.

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Once you know what needs to be fixed you can either fix the issues yourself, if you feel comfortable doing so, or share the details with your developer who can make code changes to your website.

After an issue has been fixed you can click on “Validate Fix” and Google will validate the changes.

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How Often Should I Check the Index Coverage Report?

It’s not necessary to check the index coverage report every day, Google says, because emails will be sent out whenever Search Console detects a new indexing error.

However, if an existing error gets worse, Google will not send out an email notification. So it’s necessary to check on the report at least once in a while to make sure nothing is going from bad to worse.

Those are the basics of the Search Console index coverage report. See the full video below:

[Source: This article was published in searchenginejournal.com By Matt Southern - Uploaded by the Association Member: Robert Hensonw]

Categorized in Search Engine

Over the past several years, a wealth of scientific information has become available courtesy of the efforts from scientists studying the microbiome. However, we have always come across one major issue, which is how to harness the plethora of information the research provides in an efficient manner. Now, an international team of investigators, led by researchers at the Chinese Academy of Sciences (CAS) in Beijing, has put forth a new microbiome search-based method via Microbiome Search Engine (MSE) to analyze the wealth of available health data to detect and diagnose human diseases. Findings from the new study were published recently in mSystems through an article entitled “Multiple-Disease Detection and Classification across Cohorts via Microbiome Search.

“Microbiome-based disease classification depends on well-validated, disease-specific models or markers,” explained lead study investigator Xiaoquan Su, PhD, a research scientist at the Single-Cell Center within the Qingdao Institute of Bioenergy and Bioprocess Technology (QIBEBT) of CAS. “However, current models lack that information for many diseases.”

Multiple diseases can share the same biomarkers—the microorganisms that indicate something out of the ordinary, such as a mutated protein found in cancer cells, making it harder for researchers to classify each one correctly.

To combat these issues for disease detection and classification, the researchers developed a new search approach based on the whole microbial community a human body contains—the microbiome.

“We present an alternative, search-based strategy for disease detection and classification, which detects diseased samples via their outlier novelty versus a database of samples from healthy subjects and then compare these to databases of samples from patients,” the authors wrote. Our strategy’s precision, sensitivity, and speed outperform model-based approaches. In addition, it is more robust to platform heterogeneity and to contamination in 16S rRNA gene amplicon data sets. This search-based strategy shows promise as an essential first step in microbiome big-data-based diagnosis.”

Traditional models compare samples from healthy subjects to those from people known to have specific diseases. With the new method, by searching based on the particular outlier, rather than known biomarkers that can code for several diseases, the researchers can identify the microbiome state associated with the disease across different cohorts or sequencing platforms.

In this new approach, the research team employs a two-step process to identify disease. First, they search a baseline database of healthy individuals to detect any specific microbiome outlier novelty—or any known anomaly that differentiates the microbiome from a healthy state. They then search for that outlier in a database of disease-specific examples.

“Our strategy’s precision, sensitivity, and speed outperform model-based approaches,” SU said.

The results of the search can provide quick predictions to help clinicians diagnose and treat diseases.

“This search-based strategy shows promise as an important first step in microbiome big data-based diagnosis,” according to Rob Knight, PhD, Director of the Center for Microbiome Innovation at UCSD and who recently addressed the GEN audience in a Keynote Webinar on the Dynamic Microbiome. “In light of the general shift of microbiome-sequencing focus from healthy to diseased hosts, the findings here advocate for adding more baseline samples from across different geographic locations.”

The team is working towards encouraging their colleagues to join a coordinated effort to continue expanding the microbiome database, to include every population and every ecosystem on the globe.

“With Microbiome Search Engine, performing a search can become as standard and enabling for new microbiome studies as performing a BLAST against your new DNA sequence is today” concluded XU Jian, Director of Single-Cell Center, QIBEBT.

 [Source: This article was published in genengnews.com - Uploaded by the Association Member: Anna K. Sasaki]

Categorized in Search Engine

DENVER, March 24, 2020 (GLOBE NEWSWIRE) -- TruKno, a Denver-based startup focused on improving the way cybersecurity professionals find and leverage critical information and experts, today announced the launch of the first search platform built from the ground up for the cybersecurity industry.

TruKno combines access to niche experts with the latest attack vectors, breach data, mitigation practices, innovative solutions and associated vendors to equip cybersecurity professionals with the necessary information to contend with the constantly changing threat landscape. The robust TruKno search platform currently includes more than 20,000 items and more content is added daily. 

“Cybersecurity has become a never-ending game of cat-and-mouse between hackers seeking to exploit vulnerabilities and cybersecurity professionals working to mitigate known and unknown risks to networks,” said Manish Kapoor, founder and CEO of TruKno. “In cybersecurity, finding the right information at the right time is crucial, but the fragmented nature of the industry makes being able to actually pinpoint and utilize that information an enormous challenge — with devastating consequences for failure. Our search platform consolidates all consequential components to give users the most thorough understanding of any given threat.”

“Previously, there was no platform that truly integrated all relevant cyber information in one place,” said James Carder, chief security officer at LogRhythm. “TruKno is delivering real value to the cyber community by consolidating and curating vital threat intelligence and aligning it to specific solutions, solution providers and niche experts.”

Developed by Kapoor, a seasoned technology industry professional with more than 20 years of experience in field sales, business development and product management, TruKno was created to provide context surrounding top cybersecurity threats. Kapoor’s background includes more than 10 years at Cisco Systems, helping various global service providers launch new, managed/cloud/hosted cybersecurity services. He earned an electrical engineering degree from the University of Colorado Boulder and a master’s in business management from Harvard University.

“Current search engine results are too general and have become skewed by SEO manipulation, paid advertising and changing algorithms. As such, traditional information-sourcing methods are inefficient,” Kapoor continued. “That is why we believe the future of search is curated. Curated search both excludes low-value content and brings specific content to light that might not have otherwise shown up in generic search results.”

TruKno will be hosting daily informational webinars over the next several days. To register, or for more information about TruKno’s curated search platform for cybersecurity, please visit www.TruKno.com.

About TruKno
TruKno is the first curated search platform built from the ground up for the cybersecurity industry. Based in Denver, TruKno provides a better, faster way for industry professionals to identify and comprehend top cyber threats, consolidating all related information, including the latest breaches, mitigation practices, innovative solutions, associated vendors and access to niche experts. TruKno empowers cybersecurity experts to share knowledge and highlight their personal experience, strengthening the cybersecurity community. For more information, visit www.TruKno.com.

[Source: This article was published in markets.businessinsider.com - Uploaded by the Association Member: Deborah Tannen]

Categorized in Search Engine

Lizzi Harvey from Google created a single page to follow the major updates made to the Google Search Developer documentation. So now you can just scan this page over here and see what updates she and her teammates made to the Google Search Developer documentations online.

Lizzi announced this on Twitter saying "Do you often wish there was 1 page that you could check and see what's new in the search dev docs? Well, here it is, backdated to include things that happened this month."

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Will there be a way to subscribe to these updates? RSS probably is not going to happen.

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[Source: This article was published in seroundtable.com By CBarry Schwartz - Uploaded by the Association Member: David J. Redcliff]

Categorized in Search Engine
  • Space companies, from Elon Musk’s SpaceX to start-up OneWeb, are racing to launch satellites into space with the aim of creating global internet coverage on Earth.
  • But there’s one big problem, experts say — the creation of so-called “space junk.”
  • Debris in space can be a threat to future manned missions to space as well as satellites currently in orbit.

Space companies, from Elon Musk’s SpaceX to start-up OneWeb, are racing to launch satellites into space with the aim of creating global internet coverage on Earth. But there’s one big problem, experts say — the creation and threat from so-called “space junk.”

This debris floating in space could interfere with future space missions and satellite launches — and even send objects hurtling back to Earth.

The latest episode of CNBC’s “Beyond the Valley” podcast looks at London-based start-up OneWeb’s mission to launch satellites into space and the issues surrounding space junk and regulation.

What is space junk?

There have been over 5,000 launches into space since the late 1950s, according to the European Space Agency (ESA) with nearly 9,000 satellites put up there. About 5,000 are still in space but under 2,000 are actually functioning.

These human-made objects, which can be an entire satellite or even bits of rockets, are dubbed as space junk.

The ESA said there are 22,300 pieces of debris that are traceable but there could be hundreds of thousands more than can’t be tracked.

Space junk has gotten worse for a number of reasons. When rockets are launched, certain “stages” of rockets detach from the main body of the vessel. These explode, splintering into lots of pieces. That’s one cause of the growing amount of junk.

One particular major event happened in 2009, when two satellites collided with each other, resulting in 2,300 trackable fragments being generated, the ESA said.

The other big problem is the countries launching anti-satellite missiles. For example, in 2007, China blew up one of its own missiles, increasing the amount of trackable debris size by 25% in that one incident. And in 2009, India carried out a similar missile launch on one of its own satellites.

As space junk increases, there could be a snowball effect. If more debris is traveling at thousands of miles per hour in space and it hits another object, that can result in more splintering and more junk.

“Imagine how dangerous sailing the high seas would be if all the ships ever lost in history were still drifting on top of the water,” ESA Director General Jan Worner said in a statement last year.

What’s the issue?

The biggest concern right now is the plans for thousands of satellites from various companies being launched into space.

SpaceX and OneWeb are among the companies in this race. The aim is to create so-called mega-constellations that are able to provide internet access to anywhere in the world, even the remotest parts of Earth. Both SpaceX and OneWeb have already begun launching satellites.

There are a number of risks associated with space junk. The first is that this debris could hit spacecraft carrying humans or even the International Space Station.

Another risk is satellites hitting each other. And finally, the ESA warns that large space debris that “reenter into the atmosphere in an uncontrolled way can reach the ground and create a risk to the population on the ground.”

“The space environment is a very delicate one,” Christopher Newman, professor of space law and policy at Northumbria University in the U.K., told CNBC’s “Beyond the Valley” podcast.

“And for many, many years there was the prevalence of what we call ‘big sky theory’ — space is big, we don’t need to worry about it. But actually the amount of operational space we are using is really quite small and especially now, with the constellations looking to occupy large areas of low Earth orbit, it’s becoming even more crowded.”

What is being done?

Projects have been authorized with the aim of removing the floating space rubbish.

Last year, ESA commissioned a consortium led by Swiss start-up Clear Space, to lead a mission to remove a specific item of debris from space.

A video on ClearSpace’s website shows how its technology would work. A spacecraft would be sent up toward the junk and an arm would extend out to grab the item. This mission is slated for 2025.

The Japan Aerospace Exploration Agency (JAXA) has commissioned another start-up Astroscale to remove space debris, and the mission is slated to begin in 2022.

“Active debris removal is going to become an area where I think we’re going to have to pay increased attention to,” Newman said.

Adrian Steckel, CEO of OneWeb, explained how he’s trying to make his company’s launches sustainable.

“We are making sure that what we are putting up in space … what really matters is you take this stuff down, when we take it down our satellites will disintegrate … upon re-entry (into Earth),” Steckel said during an interview for CNBC’s “Beyond the Valley” podcast.

SpaceX did not respond to a request for comment when contacted by CNBC.

[Source: This article was published in cnbc.com By ARJUNKHARPAL - Uploaded by the Association Member: Alex Gray]

Categorized in Science & Tech

Reverse image search is one of the most well-known and easiest digital investigative techniques, with two-click functionality of choosing “Search Google for image” in many web browsers. This method has also seen widespread use in popular culture, perhaps most notably in the MTV show Catfish, which exposes people in online relationships who use stolen photographs on their social media.

However, if you only use Google for reverse image searching, you will be disappointed more often than not. Limiting your search process to uploading a photograph in its original form to just images.google.com may give you useful results for the most obviously stolen or popular images, but for most any sophisticated research project, you need additional sites at your disposal — along with a lot of creativity.

This guide will walk through detailed strategies to use reverse image search in digital investigations, with an eye towards identifying people and locations, along with determining an image’s progeny. After detailing the core differences between the search engines, Yandex, Bing, and Google are tested on five test images showing different objects and from various regions of the world.

Beyond Google

The first and most important piece of advice on this topic cannot be stressed enough: Google reverse image search isn’t very good.

As of this guide’s publication date, the undisputed leader of reverse image search is the Russian site Yandex. After Yandex, the runners-up are Microsoft’s Bing and Google. A fourth service that could also be used in investigations is TinEye, but this site specializes in intellectual property violations and looks for exact duplicates of images.

Yandex

Yandex is by far the best reverse image search engine, with a scary-powerful ability to recognize faces, landscapes, and objects. This Russian site draws heavily upon user-generated content, such as tourist review sites (e.g. FourSquare and TripAdvisor) and social networks (e.g. dating sites), for remarkably accurate results with facial and landscape recognition queries.

Its strengths lie in photographs taken in a European or former-Soviet context. While photographs from North America, Africa, and other places may still return useful results on Yandex, you may find yourself frustrated by scrolling through results mostly from Russia, Ukraine, and eastern Europe rather than the country of your target images.

To use Yandex, go to images.yandex.com, then choose the camera icon on the right.

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From there, you can either upload a saved image or type in the URL of one hosted online.

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If you get stuck with the Russian user interface, look out for Выберите файл (Choose file), Введите адрес картинки (Enter image address), and Найти (Search). After searching, look out for Похожие картинки (Similar images), and Ещё похожие (More similar).

The facial recognition algorithms used by Yandex are shockingly good. Not only will Yandex look for photographs that look similar to the one that has a face in it, but it will also look for other photographs of the same person (determined through matching facial similarities) with completely different lighting, background colors, and positions. While Google and Bing may just look for other photographs showing a person with similar clothes and general facial features, Yandex will search for those matches, and also other photographs of a facial match. Below, you can see how the three services searched the face of Sergey Dubinsky, a Russian suspect in the downing of MH17. Yandex found numerous photographs of Dubinsky from various sources (only two of the top results had unrelated people), with the result differing from the original image but showing the same person. Google had no luck at all, while Bing had a single result (fifth image, second row) that also showed Dubinsky.

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Yandex is, obviously, a Russian service, and there are worries and suspicions of its ties (or potential future ties) to the Kremlin. While we at Bellingcat constantly use Yandex for its search capabilities, you may be a bit more paranoid than us. Use Yandex at your own risk, especially if you are also worried about using VK and other Russian services. If you aren’t particularly paranoid, try searching an un-indexed photograph of yourself or someone you know in Yandex, and see if it can find yourself or your doppelganger online.

Bing

Over the past few years, Bing has caught up to Google in its reverse image search capabilities, but is still limited. Bing’s “Visual Search”, found at images.bing.com, is very easy to use, and offers a few interesting features not found elsewhere.

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Within an image search, Bing allows you to crop a photograph (button below the source image) to focus on a specific element in said photograph, as seen below. The results with the cropped image will exclude the extraneous elements, focusing on the user-defined box. However, if the selected portion of the image is small, it is worth it to manually crop the photograph yourself and increase the resolution — low-resolution images (below 200×200) bring back poor results.

Below, a Google Street View image of a man walking a couple of pugs was cropped to focus on just the pooches, leading to Bing to suggest the breed of dog visible in the photograph (the “Looks like” feature), along with visually similar results. These results mostly included pairs of dogs being walked, matching the source image, but did not always only include pugs, as French bulldogs, English bulldogs, mastiffs, and others are mixed in.

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Google

By far the most popular reverse image search engine, at images.google.com, Google is fine for most rudimentary reverse image searches. Some of these relatively simple queries include identifying well-known people in photographs, finding the source of images that have been shared quite a bit online, determining the name and creator of a piece of art, and so on. However, if you want to locate images that are not close to an exact copy of the one you are researching, you may be disappointed.

For example, when searching for the face of a man who tried to attack a BBC journalist at a Trump rally, Google can find the source of the cropped image, but cannot find any additional images of him, or even someone who bears a passing resemblance to him.

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While Google was not very strong in finding other instances of this man’s face or similar-looking people, it still found the original, un-cropped version of the photograph the screenshot was taken from, showing some utility.

Five Test Cases

For testing out different reverse image search techniques and engines, a handful of images representing different types of investigations are used, including both original photographs (not previously uploaded online) and recycled ones. Due to the fact that these photographs are included in this guide, it is likely that these test cases will not work as intended in the future, as search engines will index these photographs and integrate them into their results. Thus, screenshots of the results as they appeared when this guide was being written are included.

These test photographs include a number of different geographic regions to test the strength of search engines for source material in western Europe, eastern Europe, South America, southeast Asia, and the United States. With each of these photographs, I have also highlighted discrete objects within the image to test out the strengths and weaknesses for each search engine.

Feel free to download these photographs (every image in this guide is hyperlinked directly to a JPEG file) and run them through search engines yourself to test out your skills.

Olisov Palace In Nizhny Novgord, Russia (Original, not previously uploaded online)

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Isolated: White SUV in Nizhny Novgorod

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Isolated: Trailer in Nizhny Novgorod

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Cityscape In Cebu, Philippines (Original, not previously uploaded online)

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Isolated: Condominium complex, “The Padgett Place

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Isolated: “Waterfront Hotel

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Students From Bloomberg 2020 Ad (Screenshot from video)

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Isolated: Student

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Av. do Café In São Paulo, Brazil (Screenshot Google Street View)

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Isolated: Toca do Açaí

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Isolated: Estacionamento (Parking)

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Amsterdam Canal (Original, not previously uploaded online)

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Isolated: Grey Heron

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Isolated: Dutch Flag (also rotated 90 degrees clockwise)

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Results

Each of these photographs were chosen in order to demonstrate the capabilities and limitations of the three search engines. While Yandex in particular may seem like it is working digital black magic at times, it is far from infallible and can struggle with some types of searches. For some ways to possibly overcome these limitations, I’ve detailed some creative search strategies at the end of this guide.

Novgorod’s Olisov Palace

Predictably, Yandex had no trouble identifying this Russian building. Along with photographs from a similar angle to our source photograph, Yandex also found images from other perspectives, including 90 degrees counter-clockwise (see the first two images in the third row) from the vantage point of the source image.

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Yandex also had no trouble identifying the white SUV in the foreground of the photograph as a Nissan Juke.

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Lastly, in the most challenging isolated search for this image, Yandex was unsuccessful in identifying the non-descript grey trailer in front of the building. A number of the results look like the one from the source image, but none are an actual match.

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Bing had no success in identifying this structure. Nearly all of its results were from the United States and western Europe, showing houses with white/grey masonry or siding and brown roofs.

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Likewise, Bing could not determine that the white SUV was a Nissan Juke, instead focusing on an array of other white SUVs and cars.

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Lastly, Bing failed in identifying the grey trailer, focusing more on RVs and larger, grey campers.

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Google‘s results for the full photograph are comically bad, looking to the House television show and images with very little visual similarity.

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Google successfully identified the white SUV as a Nissan Juke, even noting it in the text field search. As seen with Yandex, feeding the search engine an image from a similar perspective as popular reference materials — a side view of a car that resembles that of most advertisements — will best allow reverse image algorithms to work their magic.

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Lastly, Google recognized what the grey trailer was (travel trailer / camper), but its “visually similar images” were far from it.

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Scorecard: Yandex 2/3; Bing 0/3; Google 1/3

Cebu

Yandex was technically able to identify the cityscape as that of Cebu in the Philippines, but perhaps only by accident. The fourth result in the first row and the fourth result in the second row are of Cebu, but only the second photograph shows any of the same buildings as in the source image. Many of the results were also from southeast Asia (especially Thailand, which is a popular destination for Russian tourists), noting similar architectural styles, but none are from the same perspective as the source.

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Of the two buildings isolated from the search (the Padgett Palace and Waterfront Hotel), Yandex was able to identify the latter, but not the former. The Padgett Palace building is a relatively unremarkable high-rise building filled with condos, while the Waterfront Hotel also has a casino inside, leading to an array of tourist photographs showing its more distinct architecture.

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Bing did not have any results that were even in southeast Asia when searching for the Cebu cityscape, showing a severe geographic limitation to its indexed results.

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Like Yandex, Bing was unable to identify the building on the left part of the source image.

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Bing was unable to find the Waterfront Hotel, both when using Bing’s cropping function (bringing back only low-resolution photographs) and manually cropping and increasing the resolution of the building from the source image. It is worth noting that the results from these two versions of the image, which were identical outside of the resolution, brought back dramatically different results.

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As with Yandex, Google brought back a photograph of Cebu in its results, but without a strong resemblance to the source image. While Cebu was not in the thumbnails for the initial results, following through to “Visually similar images” will fetch an image of Cebu’s skyline as the eleventh result (third image in the second row below).

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As with Yandex and Bing, Google was unable to identify the high-rise condo building on the left part of the source image. Google also had no success with the Waterfront Hotel image.

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Scorecard: Yandex 4/6; Bing 0/6; Google 2/6

Bloomberg 2020 Student

Yandex found the source image from this Bloomberg campaign advertisement — a Getty Images stock photo. Along with this, Yandex also found versions of the photograph with filters applied (second result, first row) and additional photographs from the same stock photo series. Also, for some reason, porn, as seen in the blurred results below.

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When isolating just the face of the stock photo model, Yandex brought back a handful of other shots of the same guy (see last image in first row), plus images of the same stock photo set in the classroom (see the fourth image in the first row).

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Bing had an interesting search result: it found the exact match of the stock photograph, and then brought back “Similar images” of other men in blue shirts. The “Pages with this” tab of the result provides a handy list of duplicate versions of this same image across the web.

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Focusing on just the face of the stock photo model does not bring back any useful results, or provide the source image that it was taken from.

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Google recognizes that the image used by the Bloomberg campaign is a stock photo, bringing back an exact result. Google will also provide other stock photos of people in blue shirts in class.

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In isolating the student, Google will again return the source of the stock photo, but its visually similar images do not show the stock photo model, rather an array of other men with similar facial hair. We’ll count this as a half-win in finding the original image, but not showing any information on the specific model, as Yandex did.

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Scorecard: Yandex 6/8; Bing 1/8; Google 3.5/8

Brazilian Street View

Yandex could not figure out that this image was snapped in Brazil, instead focusing on urban landscapes in Russia.

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For the parking sign [Estacionamento], Yandex did not even come close.

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Bing did not know that this street view image was taken in Brazil.

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…nor did Bing recognize the parking sign

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…or the Toca do Açaí logo.

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Despite the fact that the image was directly taken from Google’s Street View, Google reverse image search did not recognize a photograph uploaded onto its own service.

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Just as Bing and Yandex, Google could not recognize the Portuguese parking sign.

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Lastly, Google did not come close to identifying the Toca do Açaí logo, instead focusing on various types of wooden panels, showing how it focused on the backdrop of the image rather than the logo and words.

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Scorecard: Yandex 7/11; Bing 1/11; Google 3.5/11

Amsterdam Canal

Yandex knew exactly where this photograph was taken in Amsterdam, finding other photographs taken in central Amsterdam, and even including ones with various types of birds in the frame.

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Yandex correctly identified bird in the foreground of the photograph as a grey heron (серая цапля), also bringing back an array of images of grey herons in a similar position and posture as the source image.

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However, Yandex flunked the test of identifying the Dutch flag hanging in the background of the photograph. When rotating the image 90 degrees clockwise to present the flag in its normal pattern, Yandex was able to figure out that it was a flag, but did not return any Dutch flags in its results.

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Bing only recognized that this image shows an urban landscape with water, with no results from Amsterdam.

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Though Bing struggled with identifying an urban landscape, it correctly identified the bird as a grey heron, including a specialized “Looks like” result going to a page describing the bird.

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However, like with Yandex, the Dutch flag was too confusing for Bing, both in its original and rotated forms.

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Google noted that there was a reflection in the canal of the image, but went no further than this, focusing on various paved paths in cities and nothing from Amsterdam.

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Google was close in the bird identification exercise, but just barely missed it — it is a grey, not great blue, heron.

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Google was also unable to identify the Dutch flag. Though Yandex seemed to recognize that the image is a flag, Google’s algorithm focused on the windowsill framing the image and misidentified the flag as curtains.

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Final Scorecard: Yandex 9/14; Bing 2/14; Google 3.5/14

Creative Searching

Even with the shortcomings described in this guide, there are a handful of methods to maximize your search process and game the search algorithms.

Specialized Sites

For one, you could use some other, more specialized search engines outside of the three detailed in this guide. The Cornell Lab’s Merlin Bird ID app, for example, is extremely accurate in identifying the type of birds in a photograph, or giving possible options. Additionally, though it isn’t an app and doesn’t let you reverse search a photograph, FlagID.org will let you manually enter information about a flag to figure out where it comes from. For example, with the Dutch flag that even Yandex struggled with, FlagID has no problem. After choosing a horizontal tricolor flag, we put in the colors visible in the image, then receive a series of options that include the Netherlands (along with other, similar-looking flags, such as the flag of Luxembourg).

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Language Recognition

If you are looking at a foreign language with an orthography you don’t recognize, try using some OCR or Google Translate to make your life easier. You can use Google Translate’s handwriting tool to detect the language* of a letter that you hand-write, or choose a language (if you know it already) and then write it out yourself for the word. Below, the name of a cafe (“Hedgehog in the Fog“) is written out with Google Translate’s handwriting tool, giving the typed-out version of the word (Ёжик) that can be searched.

*Be warned that Google Translate is not very good at recognizing letters if you do not already know the language, though if you scroll through enough results, you can find your handwritten letter eventually.

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Pixelation And Blurring

As detailed in a brief Twitter thread, you can pixelate or blur elements of a photograph in order to trick the search engine to focus squarely on the background. In this photograph of Rudy Giuliani’s spokeswoman, uploading the exact image will not bring back results showing where it was taken.

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However, if we blur out/pixelate the woman in the middle of the image, it will allow Yandex (and other search engines) to work their magic in matching up all of the other elements of the image: the chairs, paintings, chandelier, rug and wall patterns, and so on.

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After this pixelation is carried out, Yandex knows exactly where the image was taken: a popular hotel in Vienna.

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Conclusion

Reverse image search engines have progressed dramatically over the past decade, with no end in sight. Along with the ever-growing amount of indexed material, a number of search giants have enticed their users to sign up for image hosting services, such as Google Photos, giving these search algorithms an endless amount of material for machine learning. On top of this, facial recognition AI is entering the consumer space with products like FindClone and may already be used in some search algorithms, namely with Yandex. There are no publicly available facial recognition programs that use any Western social network, such as Facebook or Instagram, but perhaps it is only a matter of time until something like this emerges, dealing a major blow to online privacy while also (at that great cost) increasing digital research functionality.

If you skipped most of the article and are just looking for the bottom line, here are some easy-to-digest tips for reverse image searching:

  • Use Yandex first, second, and third, and then try Bing and Google if you still can’t find your desired result.
  • If you are working with source imagery that is not from a Western or former Soviet country, then you may not have much luck. These search engines are hyper-focused on these areas, and struggle for photographs taken in South America, Central America/Caribbean, Africa, and much of Asia.
  • Increase the resolution of your source image, even if it just means doubling or tripling the resolution until it’s a pixelated mess. None of these search engines can do much with an image that is under 200×200.
  • Try cropping out elements of the image, or pixelating them if it trips up your results. Most of these search engines will focus on people and their faces like a heat-seeking missile, so pixelate them to focus on the background elements.
  • If all else fails, get really creative: mirror your image horizontally, add some color filters, or use the clone tool on your image editor to fill in elements on your image that are disrupting searches.

[Source: This article was published in bellingcat.com By Aric Toler - Uploaded by the Association Member: Issac Avila] 

Categorized in Investigative Research

Google to offer users the option to auto-delete location history and web search data that it harvests

Google is to give users the choice of being able to automatically delete their search and location history after three months.

It announced the auto-delete tools for location history data, as well as web browsing and app activity, which will be rolled out in the coming weeks.

Last November Google was accused of misleading about location tracking after consumer groups from seven European nations asked their privacy regulators to take action against the search engine giant.

google logo mountainview 011

Location tracking

Consumer groups from the Netherlands, Poland, Czech Republic, Greece, Norway, Slovenia and Sweden, all filed GDPR complaints against Google’s location tracking.

They alleged that Google is tracking the movements of millions of users in breach of the European Union’s privacy laws.

Google, of course, is already facing a lawsuit in the United States for allegedly tracking phone users regardless of privacy settings.

That lawsuit was filed after an investigation by the Associated Press found that a number of Google services running on Android and Apple devices determine the user’s location and store it, even when Google’s “Location History” setting is switched off.

It should be remembered that Google had already allowed users to manually delete the data it harvests when they use its products such as YouTube, Maps and Search.

But now it trying to give users more control by offering auto-delete tools.

“And when you turn on settings like Location History or Web & App Activity, the data can make Google products more useful for you – like recommending a restaurant that you might enjoy, or helping you pick up where you left off on a previous search,” wrote David Monsees, product manager of search in a blog posting.

Auto-delete

“We work to keep your data private and secure, and we’ve heard your feedback that we need to provide simpler ways for you to manage or delete it,” Monsees added.

“You can already use your Google Account to access simply on/off controls for Location History and Web & App Activity, and if you choose – to delete all or part of that data manually,” he wrote. “In addition to these options, we’re announcing auto-delete controls that make it even easier to manage your data.”

Essentially, the user will give a time limit to choose for how long you want your data to be saved. This could be 3 months or 18 months.

Any data older than that will be automatically deleted from your account on an ongoing basis.

“These controls are coming first to Location History and Web & App Activity and will roll out in the coming weeks,” Monsees wrote. “You should always be able to manage your data in a way that works best for you–and we’re committed to giving you the best controls to make that happen.”

It should be noted that there will be no auto-delete of YouTube watch history or voice commands issued via Home and Assistant.

[Source: This article was published in silicon.co.uk By Tom Jowitt - Uploaded by the Association Member: James Gill]

Categorized in Search Engine
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