Syntactic Generation of Similar Pictures
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In visual password systems it is important to display distractor pictures along with the user’s password picture on the authentication screen. These distractors should be similar to the password picture to make it difficult for shoulder surfers to easily identify the true password picture. In this work, we present approaches to generating similar pictures using bag context picture grammars (BCPGs). We describe how these approaches are implemented and present galleries of pictures that have similar characteristics. We then determine the mathematical similarity of the generated pictures, using the spatial colour distribution descriptor (SpCD). The spatial colour distribution descriptor has been shown to be effective in determining the similarity of computer-generated pictures in previous research, and so was seen as a good similarity measure for this research. We then describe an online survey that was conducted to determine the perceptual similarity of the pictures generated by the picture grammars. This is followed by a comparison of perceptual similarity from the survey with mathematical similarity to determine if the results are consistent. The results show that we can easily generate galleries of pictures that we feel are similar (they have common characteristics) using BCPGs; that SpCD can be used to determine the mathematical similarity of the pictures; and that there is a good correlation between SpCD and perceptual similarity, although in some instances humans do make different judgements. In terms of the desired goal of generating pictures that are similar to a selected picture, we found that controlling the level of refinement of the pictures and limiting the number of refinements of subpictures are important factors.
KeywordsSyntactic picture generation Picture grammar Regulated rewriting Bag context Mathematical similarity Spatial colour distribution descriptor Perceptual similarity
This work is based upon research supported by the National Research Foundation (of South Africa). Any opinion, findings and conclusions or recommendations expressed in this material are those of the authors and therefore the NRF does not accept liability in regard thereto. The first author also acknowledges support of the Council for Scientific and Industrial Research of South Africa.
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