Security analysis of image CAPTCHA using a mask R-CNN-based attack model. (25th March 2021)

• Record Type: Journal Article
• Authors: Rathor, Vijaypal Singh; Garg, Bharat; Patil, Mandar; Sharma, G.K.
• Is Part Of: International journal of ad hoc and ubiquitous computing. Volume 36:Number 4(2021)
• Page Start: 238
• Page End: 247
• Publication Date: 2021-03-25
• Subjects: image CAPTCHA -- object detection -- convolution neural network -- security -- deep learning; Ubiquitous computing -- Periodicals; Embedded computer systems -- Periodicals; Electronic data processing -- Distributed processing -- Periodicals; Wireless communication systems -- Periodicals; Computer architecture -- Periodicals; 004.2
• Languages: English
• ISSNs: 1743-8225

Abstract

The CAPTCHAs are attacked by automated programs to break their underlying design principle. Therefore, analysing the robustness of CAPTCHA is the critical requirement. Recently, a neural style transfer-based image CAPTCHA called style area CAPTCHA (SACAPTCHA) has been reported. Though the security of SACAPTCHA is evaluated on R-CNN and FCN attack models using accuracy metrics, they are ineffective to analyse its robustness. Therefore, we propose a mask R-CNN-based attack model to critically analyse the robustness of SACAPTCHA. The proposed model performs a shape-wise analysis to test the usability of different shapes and quantifies the model performance using the F1-score. The simulation results show the highest F1 score of 0.962 and 0.828 for star and circle shapes in dataset-1 and dataset-2 respectively. The results show that model prediction is independent of the regularities of the shape. The observations prove that SACAPTCHA is vulnerable to object detection attack even after using irregular shapes.