Genetically optimized face image CAPTCHA EEE DEPT. MACE Guided by BINDU ELIAS Presented by ROMY...

Genetically optimized face image CAPTCHA

EEE DEPT. MACE

Guided by

BINDU ELIAS

Presented by

ROMY GEORGES7 EB, 521

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Test ChartINTRODUCTION

WHAT IS CAPTCHA? Completely Automated Public Turing Test to Tell Computers and Humans Apart

Differentiate between humans and bots.

Give tasks easier for humans but difficult for bots to complete.


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Reduce e-mail spams. Stop automated blog and forum responses. Prevent denial of service (DoS) attacks on web servers. Prevent bots from taking part in online polls, registering for free e-

mail accounts and collecting e-mail addresses.


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Why CAPTCHA?

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1. Text based 2. Generic image based3. Speciality image based4. Knowledge based5. Audio based


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Existing CAPTCHAS!

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Disadvantages of existing CAPTCHA

Language dependent and not suitable for multilingual world wide usage.

Cannot be used effectively in mobile touch platforms.

More vulnerable to attack.

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Generation of computationally challenging face detection CAPTCHA.

Utilization of GENETIC ALGORITHM to optimize CAPTCHA parameters.

Complex background generation

Face and non-face image selection

Distortion selection

Initial chromosome

generation

selection

crossover

mutation

replacements Termination criteria

Face image CAPTCHA

6 Distortion optimization using genetic algorithm

Face image CAPTCHA : fgCAPTCHA

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The generation process can be represented by

Cis a CAPTCHA with distortion applied.When a simulation is used to get results, we have:

F is the fitness value, indicating difference between two likelihoods7

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• a 400x300 pixel background of overlapping rectangles of

different colors and sizes is generated.

• Colored rectangles are scattered across such that 95% of background is covered.

• Minimum two face images are selected.

• At least one non face image is also selected.

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Background generation and image selection

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DISTORTION SELECTION

geometricnoise

degradation

alter shape, size or position of

embedded imagesadd interference that is not present in the

original image.

reduce details or contrast, making it difficult to

distinguish embedded images9

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Distortion optimization using genetic algorithm

We need to find the distortion settings which generate the optimized CAPTCHA.

We use genetic algorithm to efficiently identify optimal distortion setting.

Fig: Image formed before distortion is applied.

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GENETIC ALGORITHM

A genetic algorithm (or GA) is a search technique used in computing to find true or approximate solutions to optimization and search problems.

Use techniques inspired by evolutionary biology such as inheritance, mutation, selection, and crossover (also called recombination).

Implemented as a computer simulation.

Solutions can be represented in binary.11

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Steps in genetic algorithm

initial population

terminate

Run replacements

mutation

Crossover

selection

No

Yes12

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1.Generation of initial chromosomes

A set of 150 chromosomes are selected.

Each chromosome represent one combination of distortion settings.

a chromosome contain two genes each encoding a distortion type and its real valued intensity.

A fitness value is calculated for each chromosome using equation 3.

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2. Selection of candidates for next generation.

A roulette wheel based process is used to select chromosomes.

The process select chromosomes at a rate proportional to their fitness.

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3. Perform crossover

Crossover is best understood visually.

Single point crossover.

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4. Perform mutation

Mutation maintains genetic diversity from one generation to the next.

alters one or more gene value from its initial state.

It preserves and introduces diversity.

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5. Run replacements and evaluate termination criteria

generations Time limit

Stall generations

Fitness limit

Termination criteria

Running replacements: chromosomes with the best fitness value is kept from both parent and child generations.

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Examples of face image CAPTCHA

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Comparison of fgCAPTCHA with other powerful CAPTCHAS

Comparison of fgCAPTCHA with reCAPTCHA and IMAGINATION yield the following results on mobile devices:

• Human success rate is maximum for reCAPTCHA and fgCAPTCHA.

• automated attack success rate is limited for fgCAPTCHA.

• fgCAPTCHA is more user friendly.

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0102030405060708090

100

fgCAPTCHAreCAPTCHAIMAGINATION

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CONCLUSION

Face image CAPTCHA incorporates improved visual distortions which strengthens the security.

Remove dependency on humans for parameter selection and optimization.

Uses GA based image generation which increases human success rates and reduces automated attack rates.

Achieve 88% accuracy rate during evaluation for humans.

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REFERENCES

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D.-J. Kim, K.-W. Chung, and K.-S. Hong, ``Person authentication usingface, teeth and voice modalities for mobile device security,'' IEEE Trans.Consum. Electron., vol. 56, no. 4, pp. 26782685, Nov. 2010.

fgCAPTCHA: Genetically Optimized Face Image CAPTCHA ,BRIAN M. POWELL1, GAURAV GOSWAMI2,IEEE Access ,date of publication April 29, 2014, date of current version May 21, 2014.

H. Lee, S.-H. Lee, T. Kim, and H. Bahn, ``Secure user identication forconsumer electronics devices,'' IEEE Trans. Consum. Electron., vol. 54,no. 4, pp. 17981802, Nov. 2008.

R. Datta, J. Li, and J. Z. Wang, ``Exploiting the human-machine gap in image recognition for designing CAPTCHAs,'' IEEE Trans. Inf. Forensic Security, vol. 4, no. 3, pp. 504518, Sep. 2009.

M. Frank, R. Biedert, E. Ma, I. Martinovic, and D. Song, ``Touchalytics: On the applicability of touchscreen input as a behavioural biometric for authentication,'' IEEE Trans. Inf. Forensics Security, vol. 8, no. 1, , Jan. 2013

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