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

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 EEE DEPT. MACE EEE DEPT. MACE

Why CAPTCHA? Reduce e-mail spams. Genetically optimized face image CAPTCHA Why CAPTCHA? 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. 3 EEE DEPT. MACE EEE DEPT. MACE

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 EEE DEPT. MACE EEE DEPT. MACE

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 EEE DEPT. MACE EEE DEPT. MACE

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 EEE DEPT. MACE Distortion optimization using genetic algorithm EEE DEPT. MACE

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 EEE DEPT. MACE EEE DEPT. MACE EEE Dept. MACE

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 EEE DEPT. MACE EEE DEPT. MACE EEE Dept. MACE

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 EEE DEPT. MACE EEE DEPT. MACE EEE Dept. MACE

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 EEE DEPT. MACE EEE DEPT. MACE

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 EEE DEPT. MACE EEE DEPT. MACE

Steps in genetic algorithm Genetically optimized face image CAPTCHA Steps in genetic algorithm initial population terminate Run replacements mutation Crossover selection No Yes 12 EEE DEPT. MACE EEE DEPT. MACE

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 EEE DEPT. MACE

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 EEE DEPT. MACE EEE DEPT. MACE

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 EEE DEPT. MACE EEE DEPT. MACE

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 EEE DEPT. MACE EEE DEPT. MACE

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 EEE DEPT. MACE EEE DEPT. MACE

Examples of face image CAPTCHA Genetically optimized face image CAPTCHA Examples of face image CAPTCHA 18 EEE DEPT. MACE EEE DEPT. MACE

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 EEE DEPT. MACE EEE DEPT. MACE

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 EEE DEPT. MACE EEE DEPT. MACE

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. 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 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. 26782685, Nov. 2010. 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. 17981802, Nov. 2008. 21 EEE DEPT. MACE EEE DEPT. MACE

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 EEE DEPT. MACE EEE DEPT. MACE