Title : 
fgCAPTCHA: Genetically Optimized Face Image CAPTCHA 5
         
        
            Author : 
Powell, Brian M. ; Goswami, Gaurav ; Vatsa, Mayank ; Singh, Rajdeep ; Noore, Afzel
         
        
            Author_Institution : 
Lane Dept. of Comput. Sci. & Electr. Eng., West Virginia Univ., Morgantown, WV, USA
         
        
        
        
        
        
        
            Abstract : 
The increasing use of smartphones, tablets, and other mobile devices poses a significant challenge in providing effective online security. CAPTCHAs, tests for distinguishing human and computer users, have traditionally been popular; however, they face particular difficulties in a modern mobile environment because most of them rely on keyboard input and have language dependencies. This paper proposes a novel image-based CAPTCHA that combines the touch-based input methods favored by mobile devices with genetically optimized face detection tests to provide a solution that is simple for humans to solve, ready for worldwide use, and provides a high level of security by being resilient to automated computer attacks. In extensive testing involving over 2600 users and 40000 CAPTCHA tests, fgCAPTCHA demonstrates a very high human success rate while ensuring a 0% attack rate using three well-known face detection algorithms.
         
        
            Keywords : 
face recognition; mobile computing; security of data; automated computer attacks; face detection algorithms; fgCAPTCHA; genetically optimized face image CAPTCHA; modern mobile environment; novel image-based CAPTCHA; online security; touch-based input methods; CAPTCHAs; Face detection; Face recognition; Mobile communication; Mobile handsets; Noise measurement; Security; CAPTCHA; Mobile security; face detection; web security;
         
        
        
            Journal_Title : 
Access, IEEE
         
        
        
        
        
            DOI : 
10.1109/ACCESS.2014.2321001