Generative Adversarial Networks

Advanced Breach and Attack Simulation using ML

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Sat, Jul 6 2024

cybersecurity machine learning deep learning penetration testing threat modeling Generative Adversarial Networks GaN

Cybersecurity is becoming a need more than ever. Organizations need to protect their digital assets and are required to earn certifications to prove the compliance to the regulations and rules. For this, these assets must be assessed to ensure the target security posture and to get certified or pass an audit on yearly basis. This is a daunting and costly task as it often requires a third-party tester that tries to penetrate the system, under agreement. Breach and Attack Simulation is a new method that allows to do this penetration testing in-house, using some automation tools. Some of these

Latent Space Manipulation of GANs for Seamless Image Compositing

Anna Fruehstueck, Ph.D., Computer Science

Apr 17, 17:30 - 18:30

B5 L5 R5220

Generative Adversarial Networks image synthesis texture synthesis

Generative Adversarial Networks (GANs) are a very successful method for high-quality image synthesis and are a powerful tool to generate realistic images by learning their visual properties from a dataset of exemplars. However, the controllability of the generator output still poses many challenges. In this thesis, we propose several methods for achieving larger and/or higher visual quality in GAN outputs by combining latent space manipulations with image compositing operations

Anass B. El-Yaagoubi

Ph.D. (former), Statistics

Time Series neuroscience data analysis brain signals Generative Adversarial Networks topological ranking

Ph.D. Degree at the King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Hernando Ombao in the Biostatistics group.