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This paper deals with a well-known problem in the area of the smudge attacks: when a user draws a pattern to unlock the pattern lock on a smartphone screen, pattern extraction sometimes becomes difficult owing to the existence of the oily residuals around it. This is because the phone screen becomes obscured by these residuals, which significantly lower the guess rate of the pattern lock. To address this, this paper proposes a novel attack method based on a Convolutional Neural Network (CNN). CNNs are known to exhibit high accuracy in image classification. However, using only CNNs for the attack is not sufficient, because there are 389,112 possible patterns, and training the CNN for all the cases is difficult. We therefore propose two ideas to overcome the aforementioned problem. The first one is the application of ’Screen Segmentation,’ where we divide the screen into four segments to reduce the …