SEMAGE: a new image-based two-factor CAPTCHA

We present SEMAGE (SEmantically MAtching imaGEs), a new image-based CAPTCHA that capitalizes on the human ability to define and comprehend image content and to establish semantic relationships between them. A SEMAGE challenge asks a user to select semantically related images from a given image set. SEMAGE has a two-factor design where in order to pass a challenge the user is required to figure out the content of each image and then understand and identify semantic relationship between a subset of them. Most of the current state-of-the-art image-based systems like Assira [20] only require the user to solve the first level, i.e., image recognition. Utilizing the semantic correlation between images to create more secure and user-friendly challenges makes SEMAGE novel. SEMAGE does not suffer from limitations of traditional image-based approaches such as lacking customization and adaptability. SEMAGE unlike the current text-based systems is also very user-friendly with a high fun factor. These features make it very attractive to web service providers. In addition, SEMAGE is language independent and highly flexible for customizations (both in terms of security and usability levels). SEMAGE is also mobile devices friendly as it does not require the user to type anything. We conduct a first-of-its-kind large-scale user study involving 174 users to gauge and compare accuracy and usability of SEMAGE with existing state-of-the-art CAPTCHA systems like reCAPTCHA (text-based) [6] and Asirra (image-based) [20]. The user study further reinstates our points and shows that users achieve high accuracy using our system and consider our system to be fun and easy.