PGGAN: Improve Password Cover Rate Using the Controller

Guo, Xiaozhou; Liu, Yi; Tan, Kaijun; Jin, Min; Lu, Huaxiang

doi:10.1088/1742-6596/1856/1/012012

Cited by 4 publications

(3 citation statements)

References 6 publications

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“…As a modified GAN, Progressively Growing GAN (PGGAN) [74] can solve the problem of duplicate samples [75] using a multi-stage training method. PGGAN utilizes the concept of a progressive neural network that was first proposed by Rusu et al in 2016 [76].…”

Section: Pgganmentioning

confidence: 99%

Oil Palm USB (Unstripped Bunch) Detector Trained on Synthetic Images Generated by PGGAN

Aji,

Bin Ghazali,

Akbar

2023

Journal of Robotics and Control

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Identifying Unstriped Bunches (USB) is a pivotal challenge in palm oil production, contributing to reduced mill efficiency. Existing manual detection methods are proven time-consuming and prone to inaccuracies. Therefore, we propose an innovative solution harnessing computer vision technology. Specifically, we leverage the Faster R-CNN (Region-based Convolution Neural Network), a robust object detection algorithm, and complement it with Progressive Growing Generative Adversarial Networks (PGGAN) for synthetic image generation. Nevertheless, a scarcity of authentic USB images may hinder the application of Faster R-CNN. Herein, PGGAN is assumed to be pivotal in generating synthetic images of Empty Fruit Bunches (EFB) and USB. Our approach pairs synthetic images with authentic ones to train the Faster R-CNN. The VGG16 feature generator serves as the architectural backbone, fostering enhanced learning. According to our experimental results, USB detectors that were trained solely with authentic images resulted in an accuracy of 77.1%, which highlights the potential of this methodology. However, employing solely synthetic images leads to a slightly reduced accuracy of 75.3%. Strikingly, the fusion of authentic and synthetic images in a balanced ratio of 1:1 fuels a remarkable accuracy surge to 87.9%, signifying a 10.1% improvement. This innovative amalgamation underscores the potential of synthetic data augmentation in refining detection systems. By amalgamating authentic and synthetic data, we unlock a novel dimension of accuracy in USB detection, which was previously unattainable. This contribution holds significant implications for the industry, ensuring further exploration into advanced data synthesis techniques and refining detection models.

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Section: Pgganmentioning

confidence: 99%

Oil Palm USB (Unstripped Bunch) Detector Trained on Synthetic Images Generated by PGGAN

Aji,

Bin Ghazali,

Akbar

2023

Journal of Robotics and Control

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“…Nam improves the generator by using an RNN to obtain a better iterative representation [60]. Guo analyzes the generation effect of the GAN and proposes a PG-GAN model that can reduce the password repetition rate [61].…”

Section: Other Password-generation Modelsmentioning

confidence: 99%

LPG–PCFG: An Improved Probabilistic Context- Free Grammar to Hit Low-Probability Passwords

Guo

Tan

Liu

et al. 2022

Sensors

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With the development of the Internet, information security has attracted more attention. Identity authentication based on password authentication is the first line of defense; however, the password-generation model is widely used in offline password attacks and password strength evaluation. In real attack scenarios, high-probability passwords are easy to enumerate; extremely low-probability passwords usually lack semantic structure and, so, are tough to crack by applying statistical laws in machine learning models, but these passwords with lower probability have a large search space and certain semantic information. Improving the low-probability password hit rate in this interval is of great significance for improving the efficiency of offline attacks. However, obtaining a low-probability password is difficult under the current password-generation model. To solve this problem, we propose a low-probability generator–probabilistic context-free grammar (LPG–PCFG) based on PCFG. LPG–PCFG directionally increases the probability of low-probability passwords in the models’ distribution, which is designed to obtain a degeneration distribution that is friendly for generating low-probability passwords. By using the control variable method to fine-tune the degeneration of LPG–PCFG, we obtained the optimal combination of degeneration parameters. Compared with the non-degeneration PCFG model, LPG–PCFG generates a larger number of hits. When generating 107 and 108 times, the number of hits to low-probability passwords increases by 50.4% and 42.0%, respectively.

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“…Nevertheless, our observation finds that the Markov model suffers from a high repetition rate, and this problem becomes more severe as the number of passwords the model generates increases. It is obvious that a high repetition rate will also lead to an insufficient cover rate [25]. The enumeration method is different from the random sampling method, which is a deterministic algorithm.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Markov Model: Password Guessing Using Probability Adjustment Method

et al. 2021

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In password guessing, the Markov model is still widely used due to its simple structure and fast inference speed. However, the Markov model based on random sampling to generate passwords has the problem of a high repetition rate, which leads to a low cover rate. The model based on enumeration has a lower cover rate for high-probability passwords, and it is a deterministic algorithm that always generates the same passwords in the same order, making it vulnerable to attack. We design a dynamic distribution mechanism based on the random sampling method. This mechanism enables the probability distribution of passwords to be dynamically adjusted and tend toward uniform distribution strictly during the generation process. We apply the dynamic distribution mechanism to the Markov model and propose a dynamic Markov model. Through comparative experiments on the RockYou dataset, we set the optimal adjustment degree α. Compared with the Markov model without the dynamic distribution mechanism, the dynamic Markov model reduced the repetition rate from 75.88% to 66.50% and increased the cover rate from 37.65% to 43.49%. In addition, the dynamic Markov model had the highest cover rate for high-probability passwords. Finally, the model avoided the lack of a deterministic algorithm, and when it was run five times, it reached almost the same cover rate as OMEN.

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PGGAN: Improve Password Cover Rate Using the Controller

Cited by 4 publications

References 6 publications

Oil Palm USB (Unstripped Bunch) Detector Trained on Synthetic Images Generated by PGGAN

Oil Palm USB (Unstripped Bunch) Detector Trained on Synthetic Images Generated by PGGAN

LPG–PCFG: An Improved Probabilistic Context- Free Grammar to Hit Low-Probability Passwords

Dynamic Markov Model: Password Guessing Using Probability Adjustment Method

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