Proposal of Movie CAPTCHA Method Using Amodal Completion

Mori, Takuro; Uda, Ryuya; Kikuchi, Masayuki

doi:10.1109/saint.2012.12

Cited by 8 publications

(3 citation statements)

References 12 publications

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“…We introduce a concept and the history of our Amodal CAPTCHA in this chapter. The first appearance of CAPTCHA with amodal completion was that by Mori et al [17]. We call it MUK CAPTCHA in this paper.…”

Section: Overview Of Captcha With Amodal Completionmentioning

confidence: 87%

“…However, it also decreases the readability of human beings so that another solution is desired. Mori, T. et al invented the first Amodal CAPTCHA as a countermeasure of OCR reading [17]. They succeeded to increase the cost for calculation on reading by OCR while human beings were able to recognize characters easily with amodal completion.…”

Section: Amodal Captcha and Other Text-based Captchamentioning

confidence: 99%

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Evaluation of Ergonomically Designed CAPTCHAs using Deep Learning Technology

Azakami

Shibata

Uda

2018

Journal of Information Processing

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Although most existing text-based CAPTCHAs use distorted images of alphanumerics, they have been criticized because large image distortions make it difficult for human beings to recognize the characters, despite the ease with which computers can eliminate distortions and consequently recognize them. Ergonomically designed CAPTCHAs, which exploit human-specific phenomena, are a solution to this problem. Ergonomic design enables humans to momentarily recognize them, while significantly increasing computational costs for machines to recognize characters. Recently, owing to the development of deep learning, the image recognition capability of machines has improved dramatically. Characters are easily recognized within reasonable time by deep convolutional neural networks (DCNNs), which have similar architectures to visual perception mechanisms of the brain. In this paper, to clarify whether ergonomically designed CAPTCHAs can withstand state-of-the-art methods of deep learning, we use several kinds of DCNNs to measure the classification rates of the characters displayed. With respect to ergonomic designs, we first use Amodal CAPTCHA proposed by Mori et al., which exploits the two human-specific phenomena of amodal completion and aftereffects. We secondly modify Amodal CAPTCHA by adding jagged lines to the edges of characters, aiming to prevent DCNNs from recognizing them correctly, since edges are one of the most fundamental features for DCNNs. Experimental results, however, show that both naive and jagged-lined Amodal CAPTCHAs are almost completely broken. Another approach we conducted is to use only complete characters without shielding as training data, assuming that attackers have no information about how amodal completion and jagged edges were applied. However, even for this assumption, the classification rate of DCNNs is still sufficiently high. On the whole, our results in this paper show that any ergonomic effects such as amodal completion and jagged edges are no longer countermeasures against character recognition by DCNNs.

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Section: Overview Of Captcha With Amodal Completionmentioning

confidence: 87%

Section: Amodal Captcha and Other Text-based Captchamentioning

confidence: 99%

Evaluation of Ergonomically Designed CAPTCHAs using Deep Learning Technology

Azakami

Shibata

Uda

2018

Journal of Information Processing

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“…Mori et al proposed a video CAPTCHA [12] with amodal completion, which is one of the abilities of human visual interpolation. Their method utilizes black letters and gray circles that occlude letters.…”

Section: Introductionmentioning

confidence: 99%

Generation of CAPTCHA Using Sprinkled Destructors

Chang

Urayama

Watanabe

et al. 2018

The Journal of the Society for Art and Science

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CAPTCHA is a security system that is intended to distinguish computers and humans. Although many types of CAPTCHA programs exist, many CAPTCHA solver services are now becoming available. Humans can recognize partially-hidden objects that computers cannot. In this paper, we use this feature to propose a new text-based CAPTCHA, in which sprinkled destructors are placed on and under the characters. Based on the evaluation of our method, we have found that a general character recognition software called Google Tesseract and a CAPTCHA solver system are unable to recognize the CAPTCHA generated by our method although humans can recognize them.

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