System and method for user-specific captchas
The embodiments provide a system and method for improved CAPTCHA challenges that utilize user-specific information. In some embodiments, personalized information about assets currently or previously owned assets, including properties and/or vehicles, are collected. The system then builds a dataset (a “user-specific CAPTCHA dataset”) that is comprised of images including the user-owned assets. The user-specific CAPTCHA dataset can then be used to create personalized, or user-specific, CAPTCHA challenges that include images from the data set. For systems that implement CAPTCHA challenges for multiple different users, each user-specific dataset may be associated to a particular user identifier (such as a username or email address).
1 . A computer implemented method for gathering images for use in a Completely Automate Public Turing Test to Tell Computers and Humans Apart (CAPTCHA) challenge, the computer implemented method comprising the steps of:
receiving information about an asset owned by a user;
submitting a query for an image representative of the asset owned by the user, the query including the information about the asset, and receiving a returned image, wherein submitting the query includes sending the query to an online search engine;
applying a machine learning algorithm to the returned image to detect an object matching the information about the asset;
upon failing to detect the object within the returned image using the machine learning algorithm, causing the returned image to be presented, via a user interface, to a human reviewer for confirmation that the returned image includes an item matching the information about the asset; and
storing the returned image as an asset image, wherein the returned image is configured to be used in a CAPTCHA challenge for the user at a later time.
2 . The computer implemented method according to claim 1 , wherein the submitting the query includes querying a database of images.
3 . The computer implemented method according to claim 1 , wherein the returned image includes metadata, and wherein the metadata includes a tag associated with the information about the asset.
4 . The computer implemented method according to claim 1 , wherein the machine learning algorithm further comprises a neural network.
5 . The computer implemented method according to claim 1 , wherein the asset is a vehicle.
6 . The computer implemented method according to claim 1 , wherein the asset is a real estate property.
7 . A system for gathering images for use in a Completely Automated Public Turing Test to Tell Computers and Humans Apart (CAPTCHA) challenge, the system comprising:
a processor and memory, the memory storing instructions executable by the processor to:
receive information about an asset owned by a user;
submit a query for an image representative of the asset owned by the user, the query including the information about the asset, and receiving a returned image, wherein submitting the query includes sending the query to an online search engine;
apply a machine learning algorithm to the returned image to detect an object matching the information about the asset;
upon failing to detect the object within the returned image using the machine learning algorithm, causing the returned image to be presented, via a user interface, to a human reviewer for confirmation that the returned image includes an item matching the information about the asset; and
store the returned image as an asset image in the memory, wherein the returned image is configured to be used in a CAPTCHA challenge for the user at a later time.
8 . The system according to claim 7 , wherein submitting the query includes querying a database of images.
9 . The system according to claim 7 , wherein the returned image includes metadata, and wherein the metadata includes a tag associated with the information about the asset.
10 . The system according to claim 7 , wherein the machine learning algorithm includes a neural network.
11 . The system according to claim 7 , wherein the asset is a vehicle.
12 . The system according to claim 7 , wherein the asset is a real estate property.
13 . A non-transitory computer-readable medium storing instructions that, when executed by one or more processors, cause the one or more processors to perform operations for gathering images for use in a Completely Automated Public Turing Test to Tell Computers and Humans Apart (CAPTCHA) challenge, the operations comprising:
receiving information about an asset owned by a user;
submitting a query for an image representative of the asset owned by the user, the query including the information about the asset, and receiving a returned image, wherein the submitting the query includes sending the query to an online search engine;
applying a machine learning algorithm to the returned image to detect an object matching the information about the asset;
upon failing to detect the object within the returned image using the machine learning algorithm, causing the returned image to be presented, via a user interface, to a human reviewer for confirmation that the returned image includes an item matching the information about the asset; and
storing the returned image as an asset image, wherein the returned image is configured to be used in a CAPTCHA challenge for the user at a later time.
14 . The non-transitory computer-readable medium according to claim 13 , wherein the submitting the query includes querying a database of images.
15 . The non-transitory computer-readable medium according to claim 13 , wherein the returned image includes metadata, and wherein the metadata includes a tag associated with the information about the asset.
16 . The non-transitory computer-readable medium according to claim 13 , wherein the machine learning algorithm further comprises a neural network.
17 . The non-transitory computer-readable medium according to claim 13 , wherein the asset is selected from a group consisting of a vehicle and a real estate property.