Persuasive cued click-points: Design, implementation, and evaluation of a knowledge-based “Graphical password authentication using cued click points. Request PDF on ResearchGate | Graphical Password Authentication Using Cued Click Points | We propose and examine the usability and. Cued Click Points Password Authentication using Picture Grids. Article (PDF . new click-based graphical password scheme called Cued.

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We suspect that PCCP participants had more difficulty initially learning their password because they were selecting click-points that were less obvious than those chosen by PassPoints and CCP participants.

The results show the graph of the tolerance value against security success rate see figure 7 and the graph of tolerance value against success rate see figure 8.

If they are unable or unwilling to select a point in the current view port, they may press the Shuffle button to randomly reposition the view port.

It is the value which indicates the degree of closeness to the actual click graphicql.

During password creation, most of the image is dimmed authenticatiob for a small view port area that is randomly positioned on the image. We summarize the main issues below. Parts of this paper appeared earlier in publications [1], [2], [3], [4], [5],[16],[17],[18]. Given that PCCP passwords are essentially indistinguishable from random for click-point distributions along the x- and y-axes, angles, slopes, and shapes see technical usihg such pattern-based attacks would be ineffective against PCCP passwords.

We obtained favorable results both for usability and security. Access to computer systems is most often based on the use of alphanumeric passwords. User interface manipulations such as reducing the size of the mouse cursor or dimming the image may offer some protection, but have not been tested. The classification of attacks on knowledge-based authentication into two general categories: The primary security problem is hotspots: We authenticatioj consider how these could be leveraged in guessing attacks.

A password authentication system should encourage dued and less predictable passwords while maintaining memorability and security. Previous models have shown that hotspots are a problem in click-based graphical passwords, leading to a reduced effective password space that facilitates more successful dictionary attacks. Success rates within three attempts indicate that fewer than three mistakes.


This design decision had the effect authenticatlon deemphasizing the edges of the image, slightly favoring the central area.

The process flow starts from registering user id and tolerance value. The viewport is positioned randomly rather than specifically to avoid known hotspots, since such information could be used by attackers to improve guesses and could also lead to the formation of new hotspots. We believe that users can be aurhentication to select stronger passwords through better user interface design.

In picture selection phase user select any image as passwords and consist of a sequence of five click-points on a given image. CCP also provides implicit feedback claimed to be useful only to legitimate users. The size of the fovea limits foveal vision to an angle of approximately 1 degree within the direct line to the target of interest.

Once the first participant has logged out, the other participants are asked to enter the same password which they have observed of the first participant. Although attackers must perform proportionally more work to exploit hotspots, results showed that hotspots remained a problem [2]. Those who shuffled a lot felt that the viewport hindered their ability to select the most obvious click-point on an image and that they had to shuffle repeatedly in order to reach this desired point.

In recognition based,a user is presented with a set of images and the user passes the authentication by recognizing and identifying the images he selected during the registration stage.

The best attack would seem to involve building a guessing dictionary whose entries are constructed from the authenticaation hotspots on random combinations of images. We use persuasion to influence user choice is used in click-based graphical passwords, encouraging users to select more random, and hence more difficult to guess, click-points. Users may select any pixels in the image passwod click-points for their password.

Initially when the tolerance limit was large i. A considerably more complicated alternative is to make user input invisible to cameras, for example, by using eye tracking as an input mechanism. Similarly the participant select a click point each of the images.

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Our results show that our Persuasive Cued Click Points scheme is effective at reducing the number of hotspots areas of the image where users are more likely to select click points while still maintaining usability.

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In this login procedure see figure 6first user enters the unique user ID as same as entered during registration.

Each participant has a password which includes clicking on 5 click points in 5 different images. Morgan Kaufmann Publishers, As a result, the system also has the advantage of minimizing the formation of hotspots across users since click points are more randomly distributed.

Graphical Password Authentication Using Cued Click Points

During each trial, participants answered Likert-scale questions correspond to passwod reported in the previously cited studies A Likert scale is a psychometric scale commonly involved in research that employs questionnaires.

However PCCP participants were ultimately able to remember their passwords with a little additional effort. According to user opinion during lab study, The PCCP graphical password authentication system will take more time to execute the program compare to text password and pass point. Users must select a click-point within the view port. Related article at PubmedScholar Google.


This project is based on recall based Technique. We interviewed participants to learn about their shuffling strategy. To log in, they repeat the sequence of clicks in the correct order, within a authenticatiob tolerance square of the original click-points.

Specifically, when users created a password, the images were slightly shaded except for a randomly positioned viewport see Figure authenticatoon. When logging on, seeing an image they do not recognize alerts users that their previous click-point was incorrect and users may restart password entry.

The area around an original click point accepted as authenitcation since it is unrealistic to expect user to accurately target an exact pixel. Offline dictionary attacks become even less tractable.

A user who is determined to reach a certain click-point may still shuffle until the view port moves to the specific location, but this is a time consuming and more tedious process.