Myah  Conn

Myah Conn

1593086340

Copy Move Forgery Detection using SIFT and DBSCAN Clustering.

This post is to provide you a fundamental idea about the detection of one of the very common forgery techniques i.e., Copy Move Forgery using clustering.

For complete code of the following approach:

_Github: _https://github.com/Himj266/DBSCAN-Copy-Move-Foregry-Detection

_Kaggle: _https://www.kaggle.com/himj26/copy-move-forgery-detection-dbscan-clustering

Copy Move Forgery

Let’s start with brief info about copy-move forgery. Copy Move Forgery is basically cloning/copying a part of the image and then moving it to the other location to hide some details or to produce some fake information.

Images are tampered to generate fake proofs to manipulate the perception of the public and also people are not good at recognizing when an image has been manipulated, even if the change is fairly substantial. This type of forgery is very common today and forged images can be found easily on Facebook, Instagram,📸 and other platforms.

Scale-Invariant Feature Transform (SIFT)

So, Why are we talking about SIFT and what the heck is SIFT?

To detect objects in the images we need features of that object to extract meaningful details of an image. But sometimes, images may get scaled, rotated, illuminated or there is a change in viewpoint.

That means we need an algorithm to extract features of objects in a way that these features should be equal/same or approximately the same even if the object is scaled, rotated, or present at a different place.

The Sift algorithm comes in play here. It can detect features that are scale, rotation, noise invariant. The features extracted with the help of the SIFT algorithm will be able to identify the objects in the image and the features extracted are scale, rotation, noise invariant.

So, how does SIFT work?

The SIFT algorithm will find some “interesting” keypoints(like nose, eyes) in the image by using edge/corner detection techniques and thresholding. Then for each keypoint, it will produce a descriptor i.e a feature vector in a 128 dimension space which is generally a vector with 128 values.

How these keypoints are found and their descriptor is calculated is the beauty of the SIFT algorithm. You can read the complete mechanism of SIFT on this wonderful site.

The circles marked in the above image are “interesting” keypoints detected by the SIFT algorithm (Well we might not find them interesting but SIFT does and its SIFT’s choice😝) and for each circle, we have a descriptor.

#Code For SIFT in python using OpenCV
	def siftDetector(image):
	 sift = cv2.xfeatures2d.SIFT_create()
	 gray= cv2.cvtColor(image,cv2.COLOR_BGR2GRAY) 
	 key_points,descriptors = sift.detectAndCompute(gray, None)
	 return key_points,descriptors

Sift Detection in python


Ok, now we have keypoints and descriptors but what to do with them?

Since a part of the image is copied to another position their feature descriptors must be equal or approximately equal and this is what we will use to detect the forgery. This is the basic idea behind many key-point based copy-move forgery detection(CMFD) techniques.

There are many approaches suggested in many papers to compare and identify similar descriptors. Here a clustering(DBSCAN) approach is presented.

#clustering #python #dbscan #sift #image-processing

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Buddha Community

Copy Move Forgery Detection using SIFT and DBSCAN Clustering.
Myah  Conn

Myah Conn

1593086340

Copy Move Forgery Detection using SIFT and DBSCAN Clustering.

This post is to provide you a fundamental idea about the detection of one of the very common forgery techniques i.e., Copy Move Forgery using clustering.

For complete code of the following approach:

_Github: _https://github.com/Himj266/DBSCAN-Copy-Move-Foregry-Detection

_Kaggle: _https://www.kaggle.com/himj26/copy-move-forgery-detection-dbscan-clustering

Copy Move Forgery

Let’s start with brief info about copy-move forgery. Copy Move Forgery is basically cloning/copying a part of the image and then moving it to the other location to hide some details or to produce some fake information.

Images are tampered to generate fake proofs to manipulate the perception of the public and also people are not good at recognizing when an image has been manipulated, even if the change is fairly substantial. This type of forgery is very common today and forged images can be found easily on Facebook, Instagram,📸 and other platforms.

Scale-Invariant Feature Transform (SIFT)

So, Why are we talking about SIFT and what the heck is SIFT?

To detect objects in the images we need features of that object to extract meaningful details of an image. But sometimes, images may get scaled, rotated, illuminated or there is a change in viewpoint.

That means we need an algorithm to extract features of objects in a way that these features should be equal/same or approximately the same even if the object is scaled, rotated, or present at a different place.

The Sift algorithm comes in play here. It can detect features that are scale, rotation, noise invariant. The features extracted with the help of the SIFT algorithm will be able to identify the objects in the image and the features extracted are scale, rotation, noise invariant.

So, how does SIFT work?

The SIFT algorithm will find some “interesting” keypoints(like nose, eyes) in the image by using edge/corner detection techniques and thresholding. Then for each keypoint, it will produce a descriptor i.e a feature vector in a 128 dimension space which is generally a vector with 128 values.

How these keypoints are found and their descriptor is calculated is the beauty of the SIFT algorithm. You can read the complete mechanism of SIFT on this wonderful site.

The circles marked in the above image are “interesting” keypoints detected by the SIFT algorithm (Well we might not find them interesting but SIFT does and its SIFT’s choice😝) and for each circle, we have a descriptor.

#Code For SIFT in python using OpenCV
	def siftDetector(image):
	 sift = cv2.xfeatures2d.SIFT_create()
	 gray= cv2.cvtColor(image,cv2.COLOR_BGR2GRAY) 
	 key_points,descriptors = sift.detectAndCompute(gray, None)
	 return key_points,descriptors

Sift Detection in python


Ok, now we have keypoints and descriptors but what to do with them?

Since a part of the image is copied to another position their feature descriptors must be equal or approximately equal and this is what we will use to detect the forgery. This is the basic idea behind many key-point based copy-move forgery detection(CMFD) techniques.

There are many approaches suggested in many papers to compare and identify similar descriptors. Here a clustering(DBSCAN) approach is presented.

#clustering #python #dbscan #sift #image-processing

Ian  Robinson

Ian Robinson

1624380180

Big data Clustering: MR-DBSCAN from scratch using Python

Clustering based on basic standards like density, shape, and size is very common. In a similar way, DBSCAN is an extensive method of the density-based clustering algorithm.

For MapReduce check this article (https://medium.com/@rrfd/your-first-map-reduce-using-hadoop-with-python-and-osx-ca3b6f3dfe78)

Algorithm description:

  1. Choose a random point p.
  2. Fetch all points that are density-reachable from p with respect to eps and minPts.
  3. A cluster is formed if p is a core point.
  4. Visit the next point of the dataset, if p is a border point and none of the points is density-reachable from p.
  5. Repeat the above process until all the points have been examined.

#mapreduce #big-data #partitioning #dbscan #clustering-algorithm #big data clustering: mr-dbscan from scratch using python

Why Use WordPress? What Can You Do With WordPress?

Can you use WordPress for anything other than blogging? To your surprise, yes. WordPress is more than just a blogging tool, and it has helped thousands of websites and web applications to thrive. The use of WordPress powers around 40% of online projects, and today in our blog, we would visit some amazing uses of WordPress other than blogging.
What Is The Use Of WordPress?

WordPress is the most popular website platform in the world. It is the first choice of businesses that want to set a feature-rich and dynamic Content Management System. So, if you ask what WordPress is used for, the answer is – everything. It is a super-flexible, feature-rich and secure platform that offers everything to build unique websites and applications. Let’s start knowing them:

1. Multiple Websites Under A Single Installation
WordPress Multisite allows you to develop multiple sites from a single WordPress installation. You can download WordPress and start building websites you want to launch under a single server. Literally speaking, you can handle hundreds of sites from one single dashboard, which now needs applause.
It is a highly efficient platform that allows you to easily run several websites under the same login credentials. One of the best things about WordPress is the themes it has to offer. You can simply download them and plugin for various sites and save space on sites without losing their speed.

2. WordPress Social Network
WordPress can be used for high-end projects such as Social Media Network. If you don’t have the money and patience to hire a coder and invest months in building a feature-rich social media site, go for WordPress. It is one of the most amazing uses of WordPress. Its stunning CMS is unbeatable. And you can build sites as good as Facebook or Reddit etc. It can just make the process a lot easier.
To set up a social media network, you would have to download a WordPress Plugin called BuddyPress. It would allow you to connect a community page with ease and would provide all the necessary features of a community or social media. It has direct messaging, activity stream, user groups, extended profiles, and so much more. You just have to download and configure it.
If BuddyPress doesn’t meet all your needs, don’t give up on your dreams. You can try out WP Symposium or PeepSo. There are also several themes you can use to build a social network.

3. Create A Forum For Your Brand’s Community
Communities are very important for your business. They help you stay in constant connection with your users and consumers. And allow you to turn them into a loyal customer base. Meanwhile, there are many good technologies that can be used for building a community page – the good old WordPress is still the best.
It is the best community development technology. If you want to build your online community, you need to consider all the amazing features you get with WordPress. Plugins such as BB Press is an open-source, template-driven PHP/ MySQL forum software. It is very simple and doesn’t hamper the experience of the website.
Other tools such as wpFoRo and Asgaros Forum are equally good for creating a community blog. They are lightweight tools that are easy to manage and integrate with your WordPress site easily. However, there is only one tiny problem; you need to have some technical knowledge to build a WordPress Community blog page.

4. Shortcodes
Since we gave you a problem in the previous section, we would also give you a perfect solution for it. You might not know to code, but you have shortcodes. Shortcodes help you execute functions without having to code. It is an easy way to build an amazing website, add new features, customize plugins easily. They are short lines of code, and rather than memorizing multiple lines; you can have zero technical knowledge and start building a feature-rich website or application.
There are also plugins like Shortcoder, Shortcodes Ultimate, and the Basics available on WordPress that can be used, and you would not even have to remember the shortcodes.

5. Build Online Stores
If you still think about why to use WordPress, use it to build an online store. You can start selling your goods online and start selling. It is an affordable technology that helps you build a feature-rich eCommerce store with WordPress.
WooCommerce is an extension of WordPress and is one of the most used eCommerce solutions. WooCommerce holds a 28% share of the global market and is one of the best ways to set up an online store. It allows you to build user-friendly and professional online stores and has thousands of free and paid extensions. Moreover as an open-source platform, and you don’t have to pay for the license.
Apart from WooCommerce, there are Easy Digital Downloads, iThemes Exchange, Shopify eCommerce plugin, and so much more available.

6. Security Features
WordPress takes security very seriously. It offers tons of external solutions that help you in safeguarding your WordPress site. While there is no way to ensure 100% security, it provides regular updates with security patches and provides several plugins to help with backups, two-factor authorization, and more.
By choosing hosting providers like WP Engine, you can improve the security of the website. It helps in threat detection, manage patching and updates, and internal security audits for the customers, and so much more.

Read More

#use of wordpress #use wordpress for business website #use wordpress for website #what is use of wordpress #why use wordpress #why use wordpress to build a website

Michael  Hamill

Michael Hamill

1618310820

These Tips Will Help You Step Up Anomaly Detection Using ML

In this article, you will learn a couple of Machine Learning-Based Approaches for Anomaly Detection and then show how to apply one of these approaches to solve a specific use case for anomaly detection (Credit Fraud detection) in part two.

A common need when you analyzing real-world data-sets is determining which data point stand out as being different from all other data points. Such data points are known as anomalies, and the goal of anomaly detection (also known as outlier detection) is to determine all such data points in a data-driven fashion. Anomalies can be caused by errors in the data but sometimes are indicative of a new, previously unknown, underlying process.

#machine-learning #machine-learning-algorithms #anomaly-detection #detecting-data-anomalies #data-anomalies #machine-learning-use-cases #artificial-intelligence #fraud-detection

Understanding DBSCAN Algorithm and Implementation from Scratch

What is DBSCAN
DBSCAN(Density-Based Spatial Clustering of Applications with Noise) is a commonly used unsupervised clustering algorithm proposed in 1996. Unlike the most well known K-mean, DBSCAN does not need to specify the number of clusters. It can automatically detect the number of clusters based on your input data and parameters. More importantly, DBSCAN can find arbitrary shape clusters that k-means are not able to find. For example, a cluster surrounded by a different cluster.
Also, DBSCAN can handle noise and outliers. All the outliers will be identified and marked without been classified into any cluster. Therefore, DBSCAN can also be used for Anomaly Detection (Outlier Detection)
Before we take a look at the preusdecode, we need to first understand some basic concepts and terms. Eps, Minpits, Directly density-reachable, density-reachable, density-connected, core point and border point
First of all, there are two parameters we need to set for DBSCAN, Eps, and MinPts.

#data-mining #outlier-detection #python #clustering #dbscan