#Website blocker chrome extension free#

This is obviously suboptimal for the website’s reputation and SEO, so webmasters often make exceptions for visitors coming directly from Google.

Imagine someone goes to Google, clicks on the first search result, and then bounces right away because of a paywall. Google ranks articles based on engagement these days.

Cookie Remover is a great extension to do exactly that. You can usually bypass those paywalls by removing those cookies. Websites with metered paywalls use cookies to keep track of how many articles you open. You can read a few articles but then need to subscribe to see more content. Some websites like Medium have metered paywalls. To open an incognito window with a shortcut, press Shift + Command + N. And thus, you can often just open a paywalled article in the incognito mode to remove the paywall. In the incognito mode, cookies are cleared and disabled. Here are a few additional methods to try. Now, here come the extensions to bypass a paywall.īeyond browser extensions, there are other ways to bypass a paywall. Soft paywall extensions take down the curtain or extract the content from the article’s HTML, whereas hard paywalls pretend to be a robot. So these extensions use different approaches to bypass paywalls. Websites with a hard paywall, on the other hand, don’t load any content until verifying that you are a subscriber. A website with a soft paywall loads the entire article into the browser and then puts a curtain over the content by visually abstracting it with a popup or an overlay. Paywalls come in two types - soft and hard. To learn more about paywalls and how they work, check out our complete guide to bypassing paywalls.īefore we dive into the extensions themselves, here is a drill down on paywalls. If a paywall can be bypassed, these extensions will help you do it. In this article, you will learn about the best Chrome extensions to bypass a paywall.

#Website blocker chrome extension free#

The internet is no longer as free as you once thought. This project is based on the Tensorflow Image Recognition Chrome Extension, which allowed me to focus on the aspects of machine learning, image blocking, and URL reporting.So you’ve been surfing the web, exploring the vastness of the internet. If the image is not publically available, we wil not have access to it. We do not store any personal data or the actual image itself.

To do so, right click on the image and select 'This shouldn't be blocked - send Image URL'.Īll that we will receive is the URL of the image in question. If the image is classified incorrectly - say you are on a site where you know it is not porn, explicit, or racy, you have the option to report that image to help re-train the classifier so that these types of images are not detected as porn in the future. When an image is classified as porn, it will be replaced in the website as a red X. The images are then sent through the Mobile NSFW classifier using TensorflowJS. How it worksĪs you are browsing the web, the extension intercepts all image calls. The only way to know is to gather a dataset of the false positive images, which is what this chrome extension strives to do. I hypothesize that there are significant other types of images that result in false positives. A reasonable observer can surmise why that's the case. For instance, the NSFW Mobile classifier currently classifies some images of rockets and bananas as porn. The problem is that if the control images are not choosen well, the features could actually be too broad and classify images as porn that aren't even closely related. In the case of porn, the assumption would be that particular body parts would be the features that determine whether an image is porn or not. It is easy to scrape a website for porn images and train a classifier to correctly identify those images as porn, with a sample of control images used to make sure the classifier finds particular features in the images. This can be attributed to the datasets used for training. Nowadays best of breed porn detection is very good at correctly identifying porn, but also recognizes many images as false positives. Porn detection with computer vision is a difficult problem with varying levels of success. This project is meant to help build a dataset of control images to use in machine learning for porn detection. Uses the free MobileNsfwNet neural network to detect racy and pornographic images. All images are scanned on your device and removed from the website if porn is detected.