1. Genetically optimized face image CAPTCHA
Guided by
BINDU ELIAS
Presented by
ROMY GEORGE
S7 EB, 52 1
EEE DEPT. MACE
EEE Dept. MACE

2. INTRODUCTION Test Chart WHAT IS CAPTCHA?
Genetically optimized face image CAPTCHA
Test Chart
INTRODUCTION
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. 2
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3. Why CAPTCHA? Reduce e-mail spams.
Genetically optimized face image CAPTCHA
Why CAPTCHA?
Reduce 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 addresses. 3
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4. Existing CAPTCHAS! Text based Generic image based
Genetically optimized face image CAPTCHA
Existing CAPTCHAS!
Text based
Generic image based
Speciality image based
Knowledge based
Audio based 4
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5. Disadvantages of existing CAPTCHA
Genetically optimized face image CAPTCHA
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. 5
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6. Face image CAPTCHA : fgCAPTCHA
Genetically optimized face image CAPTCHA
Face image CAPTCHA : fgCAPTCHA
Complex background generation
Face and non-face image selection
Distortion selection
Initial chromosome generation
selection
crossover
mutation
replacements
Termination criteria
Face image CAPTCHA
Generation of computationally challenging face detection CAPTCHA.
Utilization of GENETIC ALGORITHM to optimize CAPTCHA parameters. 6
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Distortion optimization using genetic algorithm
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7. Genetically optimized Face Image CAPTCHA
The generation process can be represented by
C∅ is a CAPTCHA with distortion ∅ applied.
When a simulation is used to get results, we have:
F is the fitness value, indicating difference between two likelihoods 7
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EEE DEPT. MACE
EEE Dept. MACE

8. Genetically optimized Face Image CAPTCHA
Background generation and image selection
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. 8
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9. Genetically optimized Face Image CAPTCHA
DISTORTION SELECTION
geometric
noise
degradation
alter shape, size or position of embedded images
add interference that is not present in the
original image.
reduce details or contrast, making it difficult to distinguish embedded images 9
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EEE DEPT. MACE
EEE Dept. MACE

10. Distortion optimization using genetic algorithm
Genetically optimized face image CAPTCHA
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. 10
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11. Genetically optimized face image CAPTCHA
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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12. Steps in genetic algorithm
Genetically optimized face image CAPTCHA
Steps in genetic algorithm
initial population
terminate
Run replacements
mutation
Crossover
selection No
Yes 12
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13. 1.Generation of initial chromosomes
Genetically optimized face image CAPTCHA
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. 13
EEE DEPT. MACE
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14. 2. Selection of candidates for next generation.
Genetically optimized face image CAPTCHA
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. 14
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15. 3. Perform crossover Crossover is best understood visually.
Genetically optimized face image CAPTCHA
3. Perform crossover
Crossover is best understood visually.
Single point crossover. 15
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16. Genetically optimized face image CAPTCHA
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. 16
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17. generations Time limit Stall generations Fitness limit
Genetically optimized face image CAPTCHA
5. Run replacements and evaluate termination
criteria
Running replacements: chromosomes with the best fitness value is kept from both parent and child generations.
Termination criteria
generations
Time limit
Stall generations
Fitness limit 17
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18. Examples of face image CAPTCHA
Genetically optimized face image CAPTCHA
Examples of face image CAPTCHA 18
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19. Comparison of fgCAPTCHA with other powerful CAPTCHAS
Genetically optimized face image CAPTCHA
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. 19
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20. Genetically optimized face image CAPTCHA
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. 20
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21. Genetically optimized face image CAPTCHA
REFERENCES 1 2 3 4 5
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.
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 , Sep
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
D.-J. Kim, K.-W. Chung, and K.-S. Hong, ``Person authentication using
face, teeth and voice modalities for mobile device security,'' IEEE Trans.
Consum. Electron., vol. 56, no. 4, pp , Nov
H. Lee, S.-H. Lee, T. Kim, and H. Bahn, ``Secure user identication for
consumer electronics devices,'' IEEE Trans. Consum. Electron., vol. 54,
no. 4, pp , Nov 21
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22. Any Questions ? O U A N K Y H T 22
Genetically optimized face image CAPTCHA O U A N K H Y T
Any Questions ? 22
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