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Dogs vs. Cats
Classification
Animal
|...
License: Custom

Overview

Dogs vs. Cats is a competition on Kaggle, which needs to write
an algorithm to classify whether images contain either a dog or a cat. The training archive
contains 25,000 images of dogs and cats.

The Asirra data set

Web services are often protected with a challenge
that's supposed to be easy for people to solve, but difficult for computers. Such a challenge
is often called a CAPTCHA (Completely Automated Public Turing test
to tell Computers and Humans Apart) or HIP (Human Interactive Proof). HIPs are used for many
purposes, such as to reduce email and blog spam and prevent brute-force attacks on web site
passwords.

Asirra (Animal
Species Image Recognition for Restricting Access) is a HIP that works by asking users to identify
photographs of cats and dogs. This task is difficult for computers, but studies have shown
that people can accomplish it quickly and accurately. Many even think it's fun!

Asirra is
unique because of its partnership with Petfinder.com, the world's
largest site devoted to finding homes for homeless pets. They've provided Microsoft Research
with over three million images of cats and dogs, manually classified by people at thousands
of animal shelters across the United States. Kaggle is fortunate to offer a subset of this
data for fun and research.

Image recognition attacks

While random guessing is the easiest form of attack, various
forms of image recognition can allow an attacker to make guesses that are better than random.
There is enormous diversity in the photo database (a wide variety of backgrounds, angles,
poses, lighting, etc.), making accurate automatic classification difficult. In an informal
poll conducted many years ago, computer vision experts posited that a classifier with better
than 60% accuracy would be difficult without a major advance in the state of the art. For
reference, a 60% classifier improves the guessing probability of a 12-image HIP from 1/4096
to 1/459.

State of the art

The current literature suggests machine classifiers can score above 80% accuracy
on this task [1]. Therefore, Asirra
is no longer considered safe from attack. This contest aims to benchmark the latest computer
vision and deep learning approaches to this problem.

License

Custom

Data Summary
Type
Image,
Amount
25K
Size
813.56MB
Provided by
Kaggle
Kaggle is the world's largest data science community with powerful tools and resources to help you achieve your data science goals.
| Amount 25K | Size 813.56MB
Dogs vs. Cats
Classification
Animal
License: Custom

Overview

Dogs vs. Cats is a competition on Kaggle, which needs to write
an algorithm to classify whether images contain either a dog or a cat. The training archive
contains 25,000 images of dogs and cats.

The Asirra data set

Web services are often protected with a challenge
that's supposed to be easy for people to solve, but difficult for computers. Such a challenge
is often called a CAPTCHA (Completely Automated Public Turing test
to tell Computers and Humans Apart) or HIP (Human Interactive Proof). HIPs are used for many
purposes, such as to reduce email and blog spam and prevent brute-force attacks on web site
passwords.

Asirra (Animal
Species Image Recognition for Restricting Access) is a HIP that works by asking users to identify
photographs of cats and dogs. This task is difficult for computers, but studies have shown
that people can accomplish it quickly and accurately. Many even think it's fun!

Asirra is
unique because of its partnership with Petfinder.com, the world's
largest site devoted to finding homes for homeless pets. They've provided Microsoft Research
with over three million images of cats and dogs, manually classified by people at thousands
of animal shelters across the United States. Kaggle is fortunate to offer a subset of this
data for fun and research.

Image recognition attacks

While random guessing is the easiest form of attack, various
forms of image recognition can allow an attacker to make guesses that are better than random.
There is enormous diversity in the photo database (a wide variety of backgrounds, angles,
poses, lighting, etc.), making accurate automatic classification difficult. In an informal
poll conducted many years ago, computer vision experts posited that a classifier with better
than 60% accuracy would be difficult without a major advance in the state of the art. For
reference, a 60% classifier improves the guessing probability of a 12-image HIP from 1/4096
to 1/459.

State of the art

The current literature suggests machine classifiers can score above 80% accuracy
on this task [1]. Therefore, Asirra
is no longer considered safe from attack. This contest aims to benchmark the latest computer
vision and deep learning approaches to this problem.

License

Custom

0
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