DOCSLIB.ORG

A Simple Generic Attack on Text Captchas

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
A Simple Generic Attack on Text Captchas A Simple Generic Attack on Text Captchas Haichang Gao1*, Jeff Yan2*, Fang Cao1, Zhengya Zhang1, Lei Lei1, Mengyun Tang1, Ping Zhang1, Xin Zhou1, Xuqin Wang1 and Jiawei Li1 1. Institute of Software Engineering, Xidian University, Xi’an, Shaanxi, 710071, P.R. China 2. Security Lancaster & School of Computing and Communications, Lancaster University, UK ∗Corresponding authors: [email protected], [email protected] Abstract—Text-based Captchas have been widely deployed attacked many early Captchas deployed on the Internet [19]. across the Internet to defend against undesirable or malicious Yan and El Ahmad broke most visual schemes provided at bot programs. Many attacks have been proposed; these fine prior Captchaservice.org in 2006 [24], published a segmentation art advanced the scientific understanding of Captcha robustness, attack on Captchas deployed by Microsoft and Yahoo! [25] but most of them have a limited applicability. In this paper, in 2008, and broke the Megaupload scheme with a method we report a simple, low-cost but powerful attack that effectively of identifying and merging character components in 2010 [1]. breaks a wide range of text Captchas with distinct design features, including those deployed by Google, Microsoft, Yahoo!, Amazon In 2011, Bursztein et al. showed that 13 Captchas on pop- and other Internet giants. For all the schemes, our attack achieved ular websites were vulnerable to automated attacks, but they a success rate ranging from 5% to 77%, and achieved an achieved zero success on harder schemes such as reCAPTCHA average speed of solving a puzzle in less than 15 seconds on and Google’s own scheme [5]. In the same year, Yan’s team a standard desktop computer (with a 3.3GHz Intel Core i3 CPU published an effective attack on both of these schemes [2]. At and 2 GB RAM). This is to date the simplest generic attack CCS’13, Gao’s team and Yan jointly published a successful on text Captchas. Our attack is based on Log-Gabor filters; a attack on a family of hollow schemes [13]. The latest attack famed application of Gabor filters in computer security is John on Captchas [4] was published in August 2014. Daugman’s iris recognition algorithm. Our work is the first to apply Gabor filters for breaking Captchas. As a side note, other notable attacks include [14, 17, 20, 23, 27]. But they studied alternative Captcha designs such as I. INTRODUCTION animation, image and audio schemes, rather than text ones. Therefore, we will not look into the details. Captcha allows websites to automatically distinguish com- puters from humans. This technology, in particular text-based These fine prior art advanced the scientific understanding Captchas, has been widely deployed on the Internet to curb of Captcha robustness, but most of them have a limited abuses introduced by automated computer programs mas- applicability. Many of them broke specific schemes, and only querading as human beings. Although many text Captchas a few broke a security mechanism as a whole. We quote the have been broken, the most recent studies, such as one by following from a well-cited paper [25]. a UC Berkeley team [21] and one by Stanford and Google [6], suggest that Captchas are still an effective security tool. The relatively wide applicability of our attack on the MSN scheme is encouraging. However, we Captcha has had many failure modes. Designers typically doubt that there is a universal segmentation attack learn from previous failures to design better schemes. Current that is applicable to all text Captchas, given that Captchas are much more sophisticated than the earliest gener- hundreds of design variations exist. Instead, a more ation designed at Carnegie Mellon. As predicated in [25], this realistically expectation is to create a toolbox (i.e. a technology has been going through a process of evolutionary collection of algorithms and attacks, ideally organ- development, like cryptography, digital watermarking and the ised in a composable way) for evaluating the strength like, with an iterative process in which successful attacks lead of Captchas. to the development of more robust systems. This toolbox approach has been a common practice (with The robustness of text Captchas has been an active field in a few exceptions) in the Captcha research community, as the research communities. Many attacks have been proposed. evidenced by papers published afterwards. Decaptcha [5], was For examples, in 2003, Mori and Malik used sophisticated a well conceived tool for analysing Captcha robustness and object recognition algorithms to break two early designs: EZ- was considered to be a generic attack, but it followed such a Gimpy and GIMPY [18]. In 2005, Chellapilla and Simard toolbox approach, as we will explain in details later. In this paper, we propose a simple but effective attack that Permission to freely reproduce all or part of this paper for noncommercial breaks a wide range of text Captchas. Our attack is based on purposes is granted provided that copies bear this notice and the full citation on the first page. Reproduction for commercial purposes is strictly prohibited Log-Gabor filters, a versatile signal processing technique. A without the prior written consent of the Internet Society, the first-named author key innovation of John Daugman’s iris recognition algorithm (for reproduction of an entire paper only), and the author’s employer if the was to encode iris patterns into binary bits using 2D Gabor paper was prepared within the scope of employment. filters [10]. Our attack uses 2D Log-Gabor, a variant of Gabor NDSS ’16, 21-24 February 2016, San Diego, CA, USA Copyright 2016 Internet Society, ISBN 1-891562-41-X filters. By convolving a Captcha image with Log-Gabor filters http://dx.doi.org/10.14722/ndss.2016.23154 of four different orientations (i.e. directions) respectively, we extract character components along each orientation. Then, we octave. If the bandwidth is larger, a non-zero DC component use a recognition engine to combine adjacent components in will exist. If a wide spectrum is needed, Gabor filters are not different ways to form individual characters. The most likely optimal. combination is output as our recognition result. Proposed by David Field in 1987, Log-Gabor filters [11] We have tested our attack on Captchas deployed by top 20 improve normal Gabor filters in the following sense. Log- most popular websites according to Alexa ranking [3]. These Gabor’s transfer function is a Gaussian on a logarithmic real-world Captchas include Google’s new reCAPTCHA, hol- frequency axis. Normal Gabor filters often over-represent the low schemes, and conventional designs; they represent a wide low frequencies, but it is not the case for the Log-Gabor. Log- range of design features. We have also tested our attack on Gabor filters allow arbitrary bandwidth and the bandwidth can much harder Captchas such as an old version of reCAPTCHA be optimised to produce a filter with minimal spatial extent. and two other designs. Our attack is designed to aim for sim- Field suggested that Log-Gabor filters encode natural images plicity and general applicability, rather than high success rates more efficiently than ordinary Gabor functions, and that the for breaking individual schemes. However, it has successfully former are consistent with measurements of mammalian visual broken all the schemes we tested, judged by both criteria systems which indicate we human beings have cell responses commonly used in the Captcha community [5, 7]. For most that are symmetric on the log frequency scale. of the schemes, it has achieved a good success rate. Mathematically, 2D Log-Gabor filters are constructed in Novelty and significance. Our attack uses a single segmen- the polar coordinate system of frequency domain as follows. tation and recognition strategy, and it is to date the best in terms of simplicity, power and general applicability. Breaking some Captchas is rarely news, but breaking all the Captchas G(f,θ)= G(f) · G(θ) (1) with a single method that is so simple is surprising (even to 2 2 G(f) = exp {−[log (f/f0)] /[log (σ/f0)] } (2) ourselves). Although we have had much experience in breaking 2 2 various Captchas, we did not expect at the beginning that our G(θ) = exp [−(θ − θ0) /2σθ ] (3) method would work so well. f and θ represent the radial and angle coordinate, respec- Our attack might suggest that the current common practice tively. f0 and θ0 represent center frequency and direction of of text Captcha designs is doomed, but it does not pronounce the filter, respectively. σ and σθ represent radial bandwidth and a death sentence to the idea of text Captcha altogether. It’s directional bandwidth of the filter. highly likely that new text Captchas will be invented. We are experimenting some new ideas, for example. G(f) is the radial component that controls the bandwidth of the filter, and G(θ) is the angle component that controls the On the other hand, another important value of our attack choice of filter orientations. G(f,θ) defines a complete 2D is that it can be used as a standard test: any new design that Log-Gabor function. By definition, Log-Gabor filters always cannot pass this test should not be deployed. Moreover, for have no DC component. people working in security economics, this work also suggests the possibility that adversaries can launch concerted automated Gabor filters were used before in the context of computer attacks on Captchas to reduce their cost. security, but mainly in the field of biometrics. The most famous application of Gabor filters in computer security is Daugman’s We organise this paper as follows. Section II briefly intro- iris recognition [10]. Our work is the first application of Gabor duces the essence of Log-Gabor filters.
Load more

Recommended publications
  • Recaptcha: Human-Based Character Recognition Via Web Security
    REPORTS on September 12, 2008 and blogs. For example, CAPTCHAs prevent www.sciencemag.org reCAPTCHA: Human-Based Character ticket scalpers from using computer programs to buy large numbers of concert tickets, only to re Recognition via Web Security Measures sell them at an inflated price. Sites such as Gmail and Yahoo Mail use CAPTCHAs to stop spam Luis von Ahn,* Benjamin Maurer, Colin McMillen, David Abraham, Manuel Blum mers from obtaining millions of free e mail accounts, which they would use to send spam CAPTCHAs (Completely Automated Public Turing test to tell Computers and Humans Apart) are e mail. Downloaded from widespread security measures on the World Wide Web that prevent automated programs from According to our estimates, humans around abusing online services. They do so by asking humans to perform a task that computers cannot yet the world type more than 100 million CAPTCHAs perform, such as deciphering distorted characters. Our research explored whether such human every day (see supporting online text), in each case effort can be channeled into a useful purpose: helping to digitize old printed material by asking spending a few seconds typing the distorted char users to decipher scanned words from books that computerized optical character recognition failed acters. In aggregate, this amounts to hundreds of to recognize. We showed that this method can transcribe text with a word accuracy exceeding 99%, thousands of human hours per day. We report on matching the guarantee of professional human transcribers. Our apparatus is deployed in more an experiment that attempts to make positive use than 40,000 Web sites and has transcribed over 440 million words.
    [Show full text]
  • Google Docs Accessibility (Pdf)
    Google Docs Accessibility (A11y) Building Accessible Google Docs • Heading Styles • Images • Table of Contents • Captioning • Columns and Lists • Tables A11y • Tab Stops • Color Contrast • Paragraph Spacing • Headers and Footers • Meaningful Link Text • Accessibility Checker What is Assistive Technology? Assistive Technology (AT) are “products, equipment, and systems that enhance learning, working, and daily living for persons with disabilities.” Magnification Speech Screen Readers Software Recognition Trackball Mouse Keyboard Zoom Text Braille Computer Keyboard Captions/Subtitles Captioned Telephone Video Relay Services Captioning Videos Per federal and state law, and CSU policy, instructional media (e.g., videos, captured lectures, recorded presentations) must have captions. This includes instructional media used in classrooms, posted on websites or shared in Canvas. • All students who are enrolled in a course must be able to access the content in the course. • Faculty: Funding is available to help faculty generate captions and transcripts for instructional media. Materials should be submitted at least six weeks in advance of their use in instruction. • Staff: For CSUN staff who do not provide classroom material, there is a cost through chargeback. For information on the chargeback, email [email protected]. csun.edu/captioning What are Screen Readers Screen readers are a form of assistive technology (AT) software that enables access to a computer, and all the things a computer does, by attempting to identify and interpret what is being displayed on the computer screen using text-to-speech. Screen readers can only access and process live text (fully editable or selectable text). • Provides access to someone who is visually impaired, mobility or has a learning disability to access text on the screen.
    [Show full text]
  • I Am Not a Robot: an Overview on Google's Captcha
    I AM NOT A ROBOT: - AN OVERVIEW ON GOOGLE’S CAPTCHA A Thesis Presented to the Faculty of California State Polytechnic University, Pomona In Partial Fulfillment Of the Requirements for the Degree Master of Science In Computer Science By Uday Prabhala 2016 SIGNATURE PAGE THESIS: I AM NOT A ROBOT: - AN OVERVIEW ON GOOGLE’S CAPTCHA AUTHOR: Uday Prabhala DATE SUBMITTED: Summer 2016 Computer Science Department. Dr. Gilbert Young ___________________________________________ Thesis Committee Chair Computer Science Dr. Fang D. Tang ___________________________________________ Computer Science Dr. Yu Sun ___________________________________________ Computer Science ii ACKNOWLEDGEMENTS I would like to express my deepest gratitude to my family members, Yashoda, Lucky, and Diskey, as well as my girlfriend Siri, who helped make this endeavor possible. Their limitless support, assistance, and encouragement during the times when I was close to giving up were greatly helpful, and I wouldn’t have been able to overcome the obstacles without them. I would also like to send my appreciation and gratitude to the Professors who were part of my thesis committee. Most notably, I would like to thank Professor Gilbert Young, chair of the committee, for his support, patience, guidance, and sharing of knowledge throughout the program. I would also like to thank Professor Tang and Professor Yusun for reviewing my paper and attending my presentation. The above three Professors not only helped me to complete my program, but also served as an excellent example by exercising professionalism, versatility, and commitment to the developing engineering students at California State Polytechnic University, Pomona. iii ABSTRACT I am not a Robot Overview on Google’s Captcha Uday Kiran Prabhala Computers are one of the greatest inventions done by humans; these devices not only made our work easy, but could also be misused in various ways.
    [Show full text]
  • Modern Password Security for System Designers What to Consider When Building a Password-Based Authentication System
    Modern password security for system designers What to consider when building a password-based authentication system By Ian Maddox and Kyle Moschetto, Google Cloud Solutions Architects This whitepaper describes and models modern password guidance and recommendations for the designers and engineers who create secure online applications. A related whitepaper, Password security ​ for users, offers guidance for end users. This whitepaper covers the wide range of options to consider ​ when building a password-based authentication system. It also establishes a set of user-focused recommendations for password policies and storage, including the balance of password strength and usability. The technology world has been trying to improve on the password since the early days of computing. Shared-knowledge authentication is problematic because information can fall into the wrong hands or be forgotten. The problem is magnified by systems that don't support real-world secure use cases and by the frequent decision of users to take shortcuts. According to a 2019 Yubico/Ponemon study, 69 percent of respondents admit to sharing passwords with ​ ​ their colleagues to access accounts. More than half of respondents (51 percent) reuse an average of five passwords across their business and personal accounts. Furthermore, two-factor authentication is not widely used, even though it adds protection beyond a username and password. Of the respondents, 67 percent don’t use any form of two-factor authentication in their personal life, and 55 percent don’t use it at work. Password systems often allow, or even encourage, users to use insecure passwords. Systems that allow only single-factor credentials and that implement ineffective security policies add to the problem.
    [Show full text]
  • Profiles Research Networking Software Installation Guide
    Profiles Research Networking Software Installation Guide Documentation Version : July 25, 2014 Software Version : ProfilesRNS_2.1.0 Table of Contents Introduction ..................................................................................................................... 2 Hardware and Operating System Requirements ............................................................. 3 Download Options ........................................................................................................... 4 Installing the Database .................................................................................................... 5 Loading Person Data....................................................................................................... 8 Loading Person Data: Part 1 – Importing SSIS Packages into SQL Server msdb Database ..................................................................................................................... 8 Loading Person Data: Part 2 – Importing Demographic Data .................................... 10 Loading Person Data: Part 3 – Geocoding ................................................................ 15 Loading Person Data: Part 4 – Obtaining Publications .............................................. 16 Loading Person Data: Part 5 – Convert data to RDF ................................................. 19 Scheduling Database Jobs ............................................................................................ 21 Installing the Code........................................................................................................
    [Show full text]
  • A Comparison of Natural Language Understanding Platforms for Chatbots in Software Engineering
    1 A Comparison of Natural Language Understanding Platforms for Chatbots in Software Engineering Ahmad Abdellatif, Khaled Badran, Diego Elias Costa, and Emad Shihab, Senior Member, IEEE Abstract—Chatbots are envisioned to dramatically change the future of Software Engineering, allowing practitioners to chat and inquire about their software projects and interact with different services using natural language. At the heart of every chatbot is a Natural Language Understanding (NLU) component that enables the chatbot to understand natural language input. Recently, many NLU platforms were provided to serve as an off-the-shelf NLU component for chatbots, however, selecting the best NLU for Software Engineering chatbots remains an open challenge. Therefore, in this paper, we evaluate four of the most commonly used NLUs, namely IBM Watson, Google Dialogflow, Rasa, and Microsoft LUIS to shed light on which NLU should be used in Software Engineering based chatbots. Specifically, we examine the NLUs’ performance in classifying intents, confidence scores stability, and extracting entities. To evaluate the NLUs, we use two datasets that reflect two common tasks performed by Software Engineering practitioners, 1) the task of chatting with the chatbot to ask questions about software repositories 2) the task of asking development questions on Q&A forums (e.g., Stack Overflow). According to our findings, IBM Watson is the best performing NLU when considering the three aspects (intents classification, confidence scores, and entity extraction). However, the results from each individual aspect show that, in intents classification, IBM Watson performs the best with an F1-measure>84%, but in confidence scores, Rasa comes on top with a median confidence score higher than 0.91.
    [Show full text]
  • Connected Cl As Sroom
    Data in the Cloud he ability to move from one representation monthly temperatures for cities in the United of data to another is one of the key char- States, South America, and Russia. Tacteristics of expert mathematicians and A quick inspection of the graph makes it evi- scientists. Cloud computing will offer more dent that on average it is always colder in Verk- opportunities to create and display multiple hoyansk than in Washington (and much colder representations of data, making this skill in the winter) and that the seasons in the south- even more important in the future. ern hemisphere are reversed. These patterns would have been much more difficult to discern Multiple Representations in a table of numbers alone. We can represent data in a variety of forms— Even young students can explore multiple rep- graphs, charts, tables of numbers, equations. resentations of the same data with software such Mathematicians, scientists, and engineers often as The Graph Club (Tom Snyder Productions). look for patterns in data. Different representations This application allows students to view two of the same data sometimes make it easier to see linked representations of data simultaneously. As a pattern. For example, the pattern in the table children drag icons to form a picture graph or of numbers below is not immediately evident. enter numbers in a simple table, a corresponding bar graph or pie chart takes shape (see Figure 2). Changing the size of a sector in the circle graph or the height of a bar in a bar chart Table 1. changes the pictogram or vice versa.
    [Show full text]
  • Voice.AI Gateway One-Click Dialogflow Integration Guide
    Integration Guide AudioCodes Intuitive Human Communications for Chatbot Services Voice.AI Gateway Google One-Click DialogFlow Integration Integration Guide Contents Table of Contents 1 Introduction ......................................................................................................... 7 1.1 Purpose .................................................................................................................. 7 2 Messages Sent by Voice.AI Gateway ................................................................ 9 2.1 Welcome Message ................................................................................................. 9 3 Messages Sent by Agent .................................................................................. 11 3.1 Basic Activity Syntax ............................................................................................ 11 3.2 hangup activity ..................................................................................................... 11 3.3 Bot Framework Specific Details ............................................................................ 12 3.3.1 Google Dialogflow ....................................................................................................12 Voice Bot Solutions 3 Voice.AI Gateway One-Click Dialogflow List of Tables Table 2-1: Description of Initial Message Sent by Voice.AI Gateway ...................................................... 9 Table 3-1: Properties of JSON Object Activities.....................................................................................11
    [Show full text]
  • Check Point Threat Intelligence Bulletin
    December 31, 2018 – January 6, 2019 VS. CISCO IRON PORT YOUR CHECK POINT THREAT INTELLIGENCE REPORT TOP ATTACKS AND BREACHES Highly-sensitive personal data of more than 100 German politicians, including German Chancellor Angela Merkel, has been leaked in a recent attack. While the identity of the attackers and the method used are still unknown, the leaked data appears to have been collected from their personal smartphones. The popular browser-based game ‘Town of Salem’ has suffered a major data breach, exposing account data of more than 7.6 million players. The breached database contained players’ email addresses, hashed passwords, IP addresses and some payment information. The Ryuk ransomware has hit the cloud hosting provider “Dataresolution.net”, after the attackers used a hacked login account. The Ryuk campaign was studied last August by Check Point’s research team, who associated it with the notorious North Korean APT Lazarus Group. Check Point SandBlast and Anti-Bot blades provide protection against this threat (Trojan-Ransom.Win32.Ryuk) A new campaign targeting Chromecast adapters has been launched in order to promote the popular YouTube channel “PewDiePie”. The hackers utilized the Universal Plug and Play (UPnP) feature in Chromecasts that allows routers to forward public Internet ports to internal adapters and used it to connect to the device and display YouTube content. A data leak has affected over 2.4 million users of Blur, the password manager application. The leak potentially exposed users’ email addresses, password hashes, IP addresses and, in some cases, full names and password hints. The official website of Dublin’s tram system, the Luas, has been hacked and defaced.
    [Show full text]
  • An Object Detection Based Solver for Google's Image Recaptcha V2
    An Object Detection based Solver for Google’s Image reCAPTCHA v2 Md Imran Hossen∗ Yazhou Tu∗ Md Fazle Rabby∗ Md Nazmul Islam∗ Hui Cao† Xiali Hei∗ ∗University of Louisiana at Lafayette †Xi’an Jiaotong University Abstract have emerged as a superior alternative to text ones as they are considered more robust to automated attacks. Previous work showed that reCAPTCHA v2’s image chal- lenges could be solved by automated programs armed with reCAPTCHA v2, a dominant image CAPTCHA service Deep Neural Network (DNN) image classifiers and vision released by Google in 2014, asks users to perform an im- APIs provided by off-the-shelf image recognition services. age recognition task to verify that they are humans and not In response to emerging threats, Google has made signifi- bots. However, in recent years, deep learning (DL) algorithms cant updates to its image reCAPTCHA v2 challenges that have achieved impressive successes in several complex image can render the prior approaches ineffective to a great extent. recognition tasks, often matching or even outperforming the In this paper, we investigate the robustness of the latest ver- cognitive ability of humans [30]. Consequently, successful sion of reCAPTCHA v2 against advanced object detection attacks against reCAPTCHA v2 that leverage Deep Neural based solvers. We propose a fully automated object detection Network (DNN) image classifier and off-the-shelf (OTS) im- based system that breaks the most advanced challenges of age recognition services have been proposed [44, 50]. reCAPTCHA v2 with an online success rate of 83.25%, the The prior work advanced our understanding of the security highest success rate to date, and it takes only 19.93 seconds issues of image CAPTCHAs and led to better CAPTCHA (including network delays) on average to crack a challenge.
    [Show full text]
  • A Low-Resource Defeat of Recaptcha's Audio Challenge
    unCaptcha: A Low-Resource Defeat of reCaptcha’s Audio Challenge Kevin Bock Daven Patel George Hughey Dave Levin University of Maryland Abstract spread of news and information is increasingly driven by user content on sites like Twitter, YouTube, and Reddit, CAPTCHAs are the Internet’s first line of defense against bots that could defeat the captcha system and register a automated account creation and service abuse. Google’s disproportionate number of accounts could theoretically reCaptcha, one of the most popular captcha systems, control the flow of information en masse [29]. It is there- is currently used by hundreds of thousands of web- fore unsurprising that captchas have been the target of sites to protect against automated attackers by testing attack for researchers and attackers for years [27, 2, 25, whether a user is truly human. This paper presents 28, 21, 32, 13, 1, 22]. unCaptcha, an automated system that can solve re- Until recently, captchas have featured distorted text Captcha’s most difficult auditory challenges with high that users must correctly type to pass. Bursztein et success rate. We evaluate unCaptcha using over 450 re- al. [1] showed these text-based captchas to be insecure Captcha challenges from live websites, and show that by demonstrating a system with near-complete (98%) it can solve them with 85.15% accuracy in 5.42 sec- accuracy. As a result, text-based captchas have been onds, on average. unCaptcha combines free, public, on- largely phased out in favor of image captchas (discussed line speech-to-text engines with a novel phonetic map- at greater length in Section 2).
    [Show full text]
  • The Ultimate Guide to Google Sheets Everything You Need to Build Powerful Spreadsheet Workflows in Google Sheets
    The Ultimate Guide to Google Sheets Everything you need to build powerful spreadsheet workflows in Google Sheets. Zapier © 2016 Zapier Inc. Tweet This Book! Please help Zapier by spreading the word about this book on Twitter! The suggested tweet for this book is: Learn everything you need to become a spreadsheet expert with @zapier’s Ultimate Guide to Google Sheets: http://zpr.io/uBw4 It’s easy enough to list your expenses in a spreadsheet, use =sum(A1:A20) to see how much you spent, and add a graph to compare your expenses. It’s also easy to use a spreadsheet to deeply analyze your numbers, assist in research, and automate your work—but it seems a lot more tricky. Google Sheets, the free spreadsheet companion app to Google Docs, is a great tool to start out with spreadsheets. It’s free, easy to use, comes packed with hundreds of functions and the core tools you need, and lets you share spreadsheets and collaborate on them with others. But where do you start if you’ve never used a spreadsheet—or if you’re a spreadsheet professional, where do you dig in to create advanced workflows and build macros to automate your work? Here’s the guide for you. We’ll take you from beginner to expert, show you how to get started with spreadsheets, create advanced spreadsheet-powered dashboard, use spreadsheets for more than numbers, build powerful macros to automate your work, and more. You’ll also find tutorials on Google Sheets’ unique features that are only possible in an online spreadsheet, like built-in forms and survey tools and add-ons that can pull in research from the web or send emails right from your spreadsheet.
    [Show full text]