Machine Learning Based Encoder-Decoder for Captcha Recognition

Gogineni, Saikiran; Suryanarayana, Gowri; Swapna, Narla

doi:10.1109/icosec49089.2020.9215439

Cited by 3 publications

(2 citation statements)

References 15 publications

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“…Cryptographic CAPTCHAs [14] Leveraged encryption to create secure challenges Cryptographic protocols for challenge generation Complexity of cryptographic methods may pose implementation challenges. Reinforcement Learning CAPTCHAs [6] Improved adaptability to emerging threats…”

Section: Inserting Hidden Form Fields and Links In Webpagesmentioning

confidence: 99%

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Implementation of Captcha Mechanisms using Deep Learning to Prevent Automated Bot Attacks

Sakhare,

Patil

2023

RJCSE

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Online platforms' integrity and security are seriously threatened by the growth of automated bot attacks, necessitating the development of effective methods for telling harmful bots apart from legitimate users. In order to successfully combat automated bot attacks, this project investigates the creation and application of CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart) methods utilising deep learning techniques. In this study, we employ deep learning to create and apply complex CAPTCHA-problems that help us distinguish between real users and automated bots. Convolutional neural networks and recurrent neural networks are employed to build dynamic, adjustable CAPTCHAs in order to keep up with the bots' evolving approaches. Our research focuses on creating CAPTCHAs that are simple to use for humans but difficult to understand by robots, which creates a favourable user experience for those who are actually human. Character skewing, background noise injection, and image obfuscation are some of the methods we use to safeguard our CAPTCHAs while maintaining their usability. Furthermore, we carry out exhaustive trials in the real world to assess the effectiveness of our methods based on in-depth CAPTCHA learning. We evaluate their resistance to a variety of attack methods, such as counterattacks and machine learning-based bot attacks, in order to confirm that they are resilient. The findings of our study show that utilising deep learning in CAPTCHA methods to thwart automated bot attacks is both feasible and effective. Our method makes the internet environment safer and more user-friendly while also enhancing security and reducing user annoyance. This work is an important step in the fight against the growing menace of automated bot attacks in the digital sphere.

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Section: Inserting Hidden Form Fields and Links In Webpagesmentioning

confidence: 99%

“…By constantly learning and refining their problem creation algorithms, deep learning models can counteract new dangers as they appear. Bots [6] may have a very hard time keeping up with the constantly shifting nature of deep learning-based CAPTCHAs.…”

Section: Introductionmentioning

confidence: 99%