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Some anti-phishing filters have even used IWR (intelligent word recognition), which is not meant to completely replace OCR, but these filters can even detect cursive, hand-written, rotated (including upside-down text), or distorted (such as made wavy, stretched vertically or laterally, or in different directions) text, as well as text on colored backgrounds.

An example of a phishing email, disguised as an official email from a (fictional) bank.

The sender is attempting to trick the recipient into revealing confidential information by "confirming" it at the phisher's website.

Several phishing attacks have been directed specifically at senior executives and other high-profile targets within businesses, and the term whaling has been coined for these kinds of attacks.

In the case of whaling, the masquerading web page/email will take a more serious executive-level form.

Phishing is the attempt to obtain sensitive information such as usernames, passwords, and credit card details (and money), often for malicious reasons, by disguising as a trustworthy entity in an electronic communication.

The word is a neologism created as a homophone of fishing due to the similarity of using a bait in an attempt to catch a victim.

According to the 2013 Microsoft Computing Safety Index, released in February 2014, the annual worldwide impact of phishing could be as high as US$5 billion.

and it often directs users to enter personal information at a fake website, the look and feel of which are identical to the legitimate one and the only difference is the URL of the website in concern.

However, this has led to the evolution of more sophisticated anti-phishing filters that are able to recover hidden text in images.

These filters use OCR (optical character recognition) to optically scan the image and filter it.

Attackers may gather personal information about their target to increase their probability of success.