Is Web Scraping Illegal? Depends on What the Meaning of the Word Is
Depending on who you ask, web scraping can be loved or hated.
Web scraping has existed for a long time and, in its good form, it’s a key underpinning of the internet. “Good bots” enable, for example, search engines to index web content, price comparison services to save consumers money, and market researchers to gauge sentiment on social media.
“Bad bots, ” however, fetch content from a website with the intent of using it for purposes outside the site owner’s control. Bad bots make up 20 percent of all web traffic and are used to conduct a variety of harmful activities, such as denial of service attacks, competitive data mining, online fraud, account hijacking, data theft, stealing of intellectual property, unauthorized vulnerability scans, spam and digital ad fraud.
So, is it Illegal to Scrape a Website?
So is it legal or illegal? Web scraping and crawling aren’t illegal by themselves. After all, you could scrape or crawl your own website, without a hitch.
Startups love it because it’s a cheap and powerful way to gather data without the need for partnerships. Big companies use web scrapers for their own gain but also don’t want others to use bots against them.
The general opinion on the matter does not seem to matter anymore because in the past 12 months it has become very clear that the federal court system is cracking down more than ever.
Let’s take a look back. Web scraping started in a legal grey area where the use of bots to scrape a website was simply a nuisance. Not much could be done about the practice until in 2000 eBay filed a preliminary injunction against Bidder’s Edge. In the injunction eBay claimed that the use of bots on the site, against the will of the company violated Trespass to Chattels law.
The court granted the injunction because users had to opt in and agree to the terms of service on the site and that a large number of bots could be disruptive to eBay’s computer systems. The lawsuit was settled out of court so it all never came to a head but the legal precedent was set.
In 2001 however, a travel agency sued a competitor who had “scraped” its prices from its Web site to help the rival set its own prices. The judge ruled that the fact that this scraping was not welcomed by the site’s owner was not sufficient to make it “unauthorized access” for the purpose of federal hacking laws.
Two years later the legal standing for eBay v Bidder’s Edge was implicitly overruled in the “Intel v. Hamidi”, a case interpreting California’s common law trespass to chattels. It was the wild west once again. Over the next several years the courts ruled time and time again that simply putting “do not scrape us” in your website terms of service was not enough to warrant a legally binding agreement. For you to enforce that term, a user must explicitly agree or consent to the terms. This left the field wide open for scrapers to do as they wish.
Fast forward a few years and you start seeing a shift in opinion. In 2009 Facebook won one of the first copyright suits against a web scraper. This laid the groundwork for numerous lawsuits that tie any web scraping with a direct copyright violation and very clear monetary damages. The most recent case being AP v Meltwater where the courts stripped what is referred to as fair use on the internet.
Previously, for academic, personal, or information aggregation people could rely on fair use and use web scrapers. The court now gutted the fair use clause that companies had used to defend web scraping. The court determined that even small percentages, sometimes as little as 4. 5% of the content, are significant enough to not fall under fair use. The only caveat the court made was based on the simple fact that this data was available for purchase. Had it not been, it is unclear how they would have ruled. Then a few months back the gauntlet was dropped.
Andrew Auernheimer was convicted of hacking based on the act of web scraping. Although the data was unprotected and publically available via AT&T’s website, the fact that he wrote web scrapers to harvest that data in mass amounted to “brute force attack”. He did not have to consent to terms of service to deploy his bots and conduct the web scraping. The data was not available for purchase. It wasn’t behind a login. He did not even financially gain from the aggregation of the data. Most importantly, it was buggy programing by AT&T that exposed this information in the first place. Yet Andrew was at fault. This isn’t just a civil suit anymore. This charge is a felony violation that is on par with hacking or denial of service attacks and carries up to a 15-year sentence for each charge.
In 2016, Congress passed its first legislation specifically to target bad bots — the Better Online Ticket Sales (BOTS) Act, which bans the use of software that circumvents security measures on ticket seller websites. Automated ticket scalping bots use several techniques to do their dirty work including web scraping that incorporates advanced business logic to identify scalping opportunities, input purchase details into shopping carts, and even resell inventory on secondary markets.
To counteract this type of activity, the BOTS Act:
Prohibits the circumvention of a security measure used to enforce ticket purchasing limits for an event with an attendance capacity of greater than 200 persons.
Prohibits the sale of an event ticket obtained through such a circumvention violation if the seller participated in, had the ability to control, or should have known about it.
Treats violations as unfair or deceptive acts under the Federal Trade Commission Act. The bill provides authority to the FTC and states to enforce against such violations.
In other words, if you’re a venue, organization or ticketing software platform, it is still on you to defend against this fraudulent activity during your major onsales.
The UK seems to have followed the US with its Digital Economy Act 2017 which achieved Royal Assent in April. The Act seeks to protect consumers in a number of ways in an increasingly digital society, including by “cracking down on ticket touts by making it a criminal offence for those that misuse bot technology to sweep up tickets and sell them at inflated prices in the secondary market. ”
In the summer of 2017, LinkedIn sued hiQ Labs, a San Francisco-based startup. hiQ was scraping publicly available LinkedIn profiles to offer clients, according to its website, “a crystal ball that helps you determine skills gaps or turnover risks months ahead of time. ”
You might find it unsettling to think that your public LinkedIn profile could be used against you by your employer.
Yet a judge on Aug. 14, 2017 decided this is okay. Judge Edward Chen of the U. S. District Court in San Francisco agreed with hiQ’s claim in a lawsuit that Microsoft-owned LinkedIn violated antitrust laws when it blocked the startup from accessing such data. He ordered LinkedIn to remove the barriers within 24 hours. LinkedIn has filed to appeal.
The ruling contradicts previous decisions clamping down on web scraping. And it opens a Pandora’s box of questions about social media user privacy and the right of businesses to protect themselves from data hijacking.
There’s also the matter of fairness. LinkedIn spent years creating something of real value. Why should it have to hand it over to the likes of hiQ — paying for the servers and bandwidth to host all that bot traffic on top of their own human users, just so hiQ can ride LinkedIn’s coattails?
I am in the business of blocking bots. Chen’s ruling has sent a chill through those of us in the cybersecurity industry devoted to fighting web-scraping bots.
I think there is a legitimate need for some companies to be able to prevent unwanted web scrapers from accessing their site.
In October of 2017, and as reported by Bloomberg, Ticketmaster sued Prestige Entertainment, claiming it used computer programs to illegally buy as many as 40 percent of the available seats for performances of “Hamilton” in New York and the majority of the tickets Ticketmaster had available for the Mayweather v. Pacquiao fight in Las Vegas two years ago.
Prestige continued to use the illegal bots even after it paid a $3. 35 million to settle New York Attorney General Eric Schneiderman’s probe into the ticket resale industry.
Under that deal, Prestige promised to abstain from using bots, Ticketmaster said in the complaint. Ticketmaster asked for unspecified compensatory and punitive damages and a court order to stop Prestige from using bots.
Are the existing laws too antiquated to deal with the problem? Should new legislation be introduced to provide more clarity? Most sites don’t have any web scraping protections in place. Do the companies have some burden to prevent web scraping?
As the courts try to further decide the legality of scraping, companies are still having their data stolen and the business logic of their websites abused. Instead of looking to the law to eventually solve this technology problem, it’s time to start solving it with anti-bot and anti-scraping technology today.
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How to Download All Images from a Web Page in Python
·
5 min read
· Updated
sep 2021
· Web Scraping
Have you ever wanted to download all images on a certain web page? In this tutorial, you will learn how you can build a Python scraper that retrieves all images from a web page given its URL and downloads them using requests and BeautifulSoup libraries.
To get started, we need quite a few dependencies, let’s install them:
pip3 install requests bs4 tqdm
Open up a new Python file and import necessary modules:
import requests
import os
from tqdm import tqdm
from bs4 import BeautifulSoup as bs
from import urljoin, urlparse
First, let’s make a URL validator, that makes sure that the URL passed is a valid one, as there are some websites that put encoded data in the place of a URL, so we need to skip those:
def is_valid(url):
“””
Checks whether `url` is a valid URL.
parsed = urlparse(url)
return bool() and bool()
urlparse() function parses a URL into six components, we just need to see if the netloc (domain name) and scheme (protocol) are there.
Second, I’m going to write the core function that grabs all image URLs of a web page:
def get_all_images(url):
Returns all image URLs on a single `url`
soup = bs((url). content, “”)
The HTML content of the web page is in soup object, to extract all img tags in HTML, we need to use nd_all(“img”) method, let’s see it in action:
urls = []
for img in tqdm(nd_all(“img”), “Extracting images”):
img_url = (“src”)
if not img_url:
# if img does not contain src attribute, just skip
continue
This will retrieve all img elements as a Python list.
I’ve wrapped it in a tqdm object just to print a progress bar though. To grab the URL of an img tag, there is a src attribute. However, there are some tags that do not contain the src attribute, we skip those by using the continue statement above.
Now we need to make sure that the URL is absolute:
# make the URL absolute by joining domain with the URL that is just extracted
img_url = urljoin(url, img_url)
There are some URLs that contains HTTP GET key-value pairs that we don’t like (that ends with something like this “/”), let’s remove them:
try:
pos = (“? “)
img_url = img_url[:pos]
except ValueError:
pass
We’re getting the position of ‘? ‘ character, then removing everything after it, if there isn’t any, it will raise ValueError, that’s why I wrapped it in try/except block (of course you can implement it in a better way, if so, please share with us in the comments below).
Now let’s make sure that every URL is valid and returns all the image URLs:
# finally, if the url is valid
if is_valid(img_url):
(img_url)
return urls
Now that we have a function that grabs all image URLs, we need a function to download files from the web with Python, I brought the following function from this tutorial:
def download(url, pathname):
Downloads a file given an URL and puts it in the folder `pathname`
# if path doesn’t exist, make that path dir
if not (pathname):
kedirs(pathname)
# download the body of response by chunk, not immediately
response = (url, stream=True)
# get the total file size
file_size = int((“Content-Length”, 0))
# get the file name
filename = (pathname, (“/”)[-1])
# progress bar, changing the unit to bytes instead of iteration (default by tqdm)
progress = tqdm(er_content(1024), f”Downloading {filename}”, total=file_size, unit=”B”, unit_scale=True, unit_divisor=1024)
with open(filename, “wb”) as f:
for data in erable:
# write data read to the file
(data)
# update the progress bar manually
(len(data))
The above function basically takes the file url to download and the pathname of the folder to save that file into.
Related: How to Convert HTML Tables into CSV Files in Python.
Finally, here is the main function:
def main(url, path):
# get all images
imgs = get_all_images(url)
for img in imgs:
# for each image, download it
download(img, path)
Getting all image URLs from that page and download each of them one by one. Let’s test this:
main(“, “yandex-images”)
This will download all images from that URL and stores them in the folder “yandex-images” that will be created automatically.
Note though, there are some websites that load their data using Javascript, in that case, you should use requests_html library instead, I’ve already made another script that makes some tweaks to the original one and handles Javascript rendering, check it here.
Alright, we’re done! Here are some ideas you can implement to extend your code:
Extracting all links on a web page and downloading all images on each.
Download every PDF file on a given website.
Use multi-threading to accelerate the download (since this is a heavy IO task).
Use proxies to prevent certain websites from blocking your IP address.
Want to Learn More about Web Scraping?
Finally, if you want to dig more into web scraping with different Python libraries, not just BeautifulSoup, the below courses will definitely be valuable for you:
Modern Web Scraping with Python using Scrapy Splash Selenium.
Web Scraping and API Fundamentals in Python 2021.
Learn Also: How to Make an Email Extractor in Python.
Happy Scraping ♥
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Search engine scraping – Wikipedia
Search engine scraping is the process of harvesting URLs, descriptions, or other information from search engines such as Google, Bing, Yahoo, Petal or Sogou. This is a specific form of screen scraping or web scraping dedicated to search engines only.
Most commonly larger search engine optimization (SEO) providers depend on regularly scraping keywords from search engines, especially Google, Petal, Sogou to monitor the competitive position of their customers’ websites for relevant keywords or their indexing status.
Search engines like Google have implemented various forms of human detection to block any sort of automated access to their service, [1] in the intent of driving the users of scrapers towards buying their official APIs instead.
The process of entering a website and extracting data in an automated fashion is also often called “crawling”. Search engines like Google, Bing, Yahoo, Petal or Sogou get almost all their data from automated crawling bots.
Difficulties[edit]
Google is the by far largest search engine with most users in numbers as well as most revenue in creative advertisements, which makes Google the most important search engine to scrape for SEO related companies. [2]
Although Google does not take legal action against scraping, it uses a range of defensive methods that makes scraping their results a challenging task, even when the scraping tool is realistically spoofing a normal web browser:
Google is using a complex system of request rate limitation which can vary for each language, country, User-Agent as well as depending on the keywords or search parameters. The rate limitation can make it unpredictable when accessing a search engine automated as the behaviour patterns are not known to the outside developer or user.
Network and IP limitations are as well part of the scraping defense systems. Search engines can not easily be tricked by changing to another IP, while using proxies is a very important part in successful scraping. The diversity and abusive history of an IP is important as well.
Offending IPs and offending IP networks can easily be stored in a blacklist database to detect offenders much faster. The fact that most ISPs give dynamic IP addresses to customers requires that such automated bans be only temporary, to not block innocent users.
Behaviour based detection is the most difficult defense system. Search engines serve their pages to millions of users every day, this provides a large amount of behaviour information. A scraping script or bot is not behaving like a real user, aside from having non-typical access times, delays and session times the keywords being harvested might be related to each other or include unusual parameters. Google for example has a very sophisticated behaviour analyzation system, possibly using deep learning software to detect unusual patterns of access. It can detect unusual activity much faster than other search engines. [3]
HTML markup changes, depending on the methods used to harvest the content of a website even a small change in HTML data can render a scraping tool broken until it is updated.
General changes in detection systems. In the past years search engines have tightened their detection systems nearly month by month making it more and more difficult to reliable scrape as the developers need to experiment and adapt their code regularly. [4]
Detection[edit]
When search engine defense thinks an access might be automated the search engine can react differently.
The first layer of defense is a captcha page[5] where the user is prompted to verify they are a real person and not a bot or tool. Solving the captcha will create a cookie that permits access to the search engine again for a while. After about one day the captcha page is removed again.
The second layer of defense is a similar error page but without captcha, in such a case the user is completely blocked from using the search engine until the temporary block is lifted or the user changes their IP.
The third layer of defense is a long-term block of the entire network segment. Google has blocked large network blocks for months. This sort of block is likely triggered by an administrator and only happens if a scraping tool is sending a very high number of requests.
All these forms of detection may also happen to a normal user, especially users sharing the same IP address or network class (IPV4 ranges as well as IPv6 ranges).
Methods of scraping Google, Bing, Yahoo, Petal or Sogou[edit]
To scrape a search engine successfully the two major factors are time and amount.
The more keywords a user needs to scrape and the smaller the time for the job the more difficult scraping will be and the more developed a scraping script or tool needs to be.
Scraping scripts need to overcome a few technical challenges:[6]
IP rotation using Proxies (proxies should be unshared and not listed in blacklists)
Proper time management, time between keyword changes, pagination as well as correctly placed delays Effective longterm scraping rates can vary from only 3–5 requests (keywords or pages) per hour up to 100 and more per hour for each IP address / Proxy in use. The quality of IPs, methods of scraping, keywords requested and language/country requested can greatly affect the possible maximum rate.
Correct handling of URL parameters, cookies as well as HTTP headers to emulate a user with a typical browser[7]
HTML DOM parsing (extracting URLs, descriptions, ranking position, sitelinks and other relevant data from the HTML code)
Error handling, automated reaction on captcha or block pages and other unusual responses[8]
Captcha definition explained as mentioned above by[9]
An example of an open source scraping software which makes use of the above mentioned techniques is GoogleScraper. [7] This framework controls browsers over the DevTools Protocol and makes it hard for Google to detect that the browser is automated.
Programming languages[edit]
When developing a scraper for a search engine almost any programming language can be used. Although, depending on performance requirements, some languages will be favorable.
PHP is a commonly used language to write scraping scripts for websites or backend services, since it has powerful capabilities built-in (DOM parsers, libcURL); however, its memory usage is typically 10 times the factor of a similar C/C++ code. Ruby on Rails as well as Python are also frequently used to automated scraping jobs. For highest performance, C++ DOM parsers should be considered.
Additionally, bash scripting can be used together with cURL as a command line tool to scrape a search engine.
Tools and scripts[edit]
When developing a search engine scraper there are several existing tools and libraries available that can either be used, extended or just analyzed to learn from.
iMacros – A free browser automation toolkit that can be used for very small volume scraping from within a users browser [10]
cURL – a command line browser for automation and testing as well as a powerful open source HTTP interaction library available for a large range of programming languages. [11]
google-search – A Go package to scrape Google. [12]
SEO Tools Kit – Free Online Tools, Duckduckgo, Baidu, Petal, Sogou) by using proxies (socks4/5, proxy). The tool includes asynchronous networking support and is able to control real browsers to mitigate detection. [13]
se-scraper – Successor of SEO Tools Kit. Scrape search engines concurrently with different proxies. [14]
Legal[edit]
When scraping websites and services the legal part is often a big concern for companies, for web scraping it greatly depends on the country a scraping user/company is from as well as which data or website is being scraped. With many different court rulings all over the world. [15][16][17]
However, when it comes to scraping search engines the situation is different, search engines usually do not list intellectual property as they just repeat or summarize information they scraped from other websites.
The largest public known incident of a search engine being scraped happened in 2011 when Microsoft was caught scraping unknown keywords from Google for their own, rather new Bing service, [18] but even this incident did not result in a court case.
One possible reason might be that search engines like Google, Petal, Sogou are getting almost all their data by scraping millions of public reachable websites, also without reading and accepting those terms.
See also[edit]
Comparison of HTML parsers
References[edit]
^ “Automated queries – Search Console Help”. Retrieved 2017-04-02.
^ “Google Still World’s Most Popular Search Engine By Far, But Share Of Unique Searchers Dips Slightly”. 11 February 2013.
^ “Does Google know that I am using Tor Browser? “.
^ “Google Groups”.
^ “My computer is sending automated queries – reCAPTCHA Help”. Retrieved 2017-04-02.
^ “Scraping Google Ranks for Fun and Profit”.
^ a b “Python3 framework GoogleScraper”. scrapeulous.
^ Deniel Iblika (3 January 2018). “De Online Marketing Diensten van DoubleSmart”. DoubleSmart (in Dutch). Diensten. Retrieved 16 January 2019.
^ Jan Janssen (26 September 2019). “Online Marketing Services van SEO SNEL”. SEO SNEL (in Dutch). Services. Retrieved 26 September 2019.
^ “iMacros to extract google results”. Retrieved 2017-04-04.
^ “libcurl – the multiprotocol file transfer library”.
^ “A Go package to scrape Google” – via GitHub.
^ “Free online SEO Tools (like Google, Yandex, Bing, Duckduckgo,… ). Including asynchronous networking support. : NikolaiT/SEO Tools Kit”. 15 January 2019 – via GitHub.
^ Tschacher, Nikolai (2020-11-17), NikolaiT/se-scraper, retrieved 2020-11-19
^ “Is Web Scraping Legal? “. Icreon (blog).
^ “Appeals court reverses hacker/troll “weev” conviction and sentence [Updated]”.
^ “Can Scraping Non-Infringing Content Become Copyright Infringement… Because Of How Scrapers Work? “.
^ Singel, Ryan. “Google Catches Bing Copying; Microsoft Says ‘So What? ‘”. Wired.
External links[edit]
Scrapy Open source python framework, not dedicated to search engine scraping but regularly used as base and with a large number of users.
Compunect scraping sourcecode – A range of well known open source PHP scraping scripts including a regularly maintained Google Search scraper for scraping advertisements and organic resultpages.
Justone free scraping scripts – Information about Google scraping as well as open source PHP scripts (last updated mid 2016)
rvices source code – Python and PHP open source classes for a 3rd party scraping API. (updated January 2017, free for private use)
PHP Simpledom A widespread open source PHP DOM parser to interpret HTML code into variables.
SerpApi Third party service based in the United States allowing you to scrape search engines legally.
Frequently Asked Questions about how to scrape an image from a website
How do I extract an image from a website?
Extract. pics is an easy to use tool that allows you to extract, view and download images from any public website. Simply paste the URL of the website into the input field and click “Extract” to start the process. The extraction process will take a few seconds to make sure it finds as many images as possible.
Is Web scraping images Legal?
Web scraping and crawling aren’t illegal by themselves. … Web scraping started in a legal grey area where the use of bots to scrape a website was simply a nuisance. Not much could be done about the practice until in 2000 eBay filed a preliminary injunction against Bidder’s Edge.
How do I get an image from a website using python?
How to Download All Images from a Web Page in Pythonpip3 install requests bs4 tqdm.import requests import os from tqdm import tqdm from bs4 import BeautifulSoup as bs from urllib. … def is_valid(url): “”” Checks whether `url` is a valid URL. “””More items…