Robust CAPTCHA Image Generation Enhanced with Adversarial Example Methods

Kwon, Hyun; Yoon, Hyunsoo; Park, Ki-Woong

doi:10.1587/transinf.2019edl8194

Cited by 17 publications

(14 citation statements)

References 8 publications

(7 reference statements)

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“…GAN was also tested using actual CAPTCHA images. The second method [23] generates CAPTCHA images using the adversarial example technique. Adversarial example adds a little noise to the original CAPTCHA to maintain human perception, while CAPTCHA recognition systems misidentifies it.…”

Section: Other Methods For Generating Captchasmentioning

confidence: 99%

CAPTCHA Image Generation: Two-Step Style-Transfer Learning in Deep Neural Networks

Kwon

Yoon

Park

2020

Sensors

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Mobile devices such as sensors are used to connect to the Internet and provide services to users. Web services are vulnerable to automated attacks, which can restrict mobile devices from accessing websites. To prevent such automated attacks, CAPTCHAs are widely used as a security solution. However, when a high level of distortion has been applied to a CAPTCHA to make it resistant to automated attacks, the CAPTCHA becomes difficult for a human to recognize. In this work, we propose a method for generating a CAPTCHA image that will resist recognition by machines while maintaining its recognizability to humans. The method utilizes the style transfer method, and creates a new image, called a style-plugged-CAPTCHA image, by incorporating the styles of other images while keeping the content of the original CAPTCHA. In our experiment, we used the TensorFlow machine learning library and six CAPTCHA datasets in use on actual websites. The experimental results show that the proposed scheme reduces the rate of recognition by the DeCAPTCHA system to 3.5% and 3.2% using one style image and two style images, respectively, while maintaining recognizability by humans.

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Section: Other Methods For Generating Captchasmentioning

confidence: 99%

CAPTCHA Image Generation: Two-Step Style-Transfer Learning in Deep Neural Networks

Kwon

Yoon

Park

2020

Sensors

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“…Deep learning has become popular for different applications [ 1 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 ]; single image SR (SISR) is one of them [ 27 , 28 , 29 , 30 , 31 ]. It is a very challenging problem due to the transformation of a specific low-resolution (LR) image to a high-resolution (HR) image.…”

Section: Background Of Iim and Super Resolutionmentioning

confidence: 99%

Super-Resolution Enhancement Method Based on Generative Adversarial Network for Integral Imaging Microscopy

Alam

Erdenebat

Abbass

et al. 2021

Sensors

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The integral imaging microscopy system provides a three-dimensional visualization of a microscopic object. However, it has a low-resolution problem due to the fundamental limitation of the F-number (the aperture stops) by using micro lens array (MLA) and a poor illumination environment. In this paper, a generative adversarial network (GAN)-based super-resolution algorithm is proposed to enhance the resolution where the directional view image is directly fed as input. In a GAN network, the generator regresses the high-resolution output from the low-resolution input image, whereas the discriminator distinguishes between the original and generated image. In the generator part, we use consecutive residual blocks with the content loss to retrieve the photo-realistic original image. It can restore the edges and enhance the resolution by ×2, ×4, and even ×8 times without seriously hampering the image quality. The model is tested with a variety of low-resolution microscopic sample images and successfully generates high-resolution directional view images with better illumination. The quantitative analysis shows that the proposed model performs better for microscopic images than the existing algorithms.

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“…They used the VGG16, ResNet101, and the Inception-ResNet-V2 CNNs in conjunction with the Fast Gradient Sign Method and the Universal Adversarial Perturbations method [78] to generate their adversarial text-based CAPTCHAs. Kwon et al [79] generated their adversarial text-based CAPTCHAs using the FGSM, I-FGSM, and the Deep-Fool algorithms. Their experiment results showed a 0% recognition rate with epsilon of 0.15 for FGSM, a 0% recognition rate with alpha of 0.1 with 50 iterations for I-FGSM, and a 45% recognition rate with 150 iterations for the Deep-Fool algorithm.…”

Section: Related Workmentioning

confidence: 99%

Securing IoT Devices: A Robust and Efficient Deep Learning with a Mixed Batch Adversarial Generation Process for CAPTCHA Security Verification

Dankwa

Yang

2021

Electronics

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The Internet of Things environment (e.g., smart phones, smart televisions, and smart watches) ensures that the end user experience is easy, by connecting lives on web services via the internet. Integrating Internet of Things devices poses ethical risks related to data security, privacy, reliability and management, data mining, and knowledge exchange. An adversarial machine learning attack is a good practice to adopt, to strengthen the security of text-based CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart), to withstand against malicious attacks from computer hackers, to protect Internet of Things devices and the end user’s privacy. The goal of this current study is to perform security vulnerability verification on adversarial text-based CAPTCHA, based on attacker–defender scenarios. Therefore, this study proposed computation-efficient deep learning with a mixed batch adversarial generation process model, which attempted to break the transferability attack, and mitigate the problem of catastrophic forgetting in the context of adversarial attack defense. After performing K-fold cross-validation, experimental results showed that the proposed defense model achieved mean accuracies in the range of 82–84% among three gradient-based adversarial attack datasets.

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Robust CAPTCHA Image Generation Enhanced with Adversarial Example Methods

Cited by 17 publications

References 8 publications

CAPTCHA Image Generation: Two-Step Style-Transfer Learning in Deep Neural Networks

CAPTCHA Image Generation: Two-Step Style-Transfer Learning in Deep Neural Networks

Super-Resolution Enhancement Method Based on Generative Adversarial Network for Integral Imaging Microscopy

Securing IoT Devices: A Robust and Efficient Deep Learning with a Mixed Batch Adversarial Generation Process for CAPTCHA Security Verification

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