Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/157133
Title: Adversarial attack defences for neural network
Authors: Singh Kirath
Keywords: Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision
Issue Date: 2022
Publisher: Nanyang Technological University
Source: Singh Kirath (2022). Adversarial attack defences for neural network. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/157133
Abstract: Since the advent of deep learning, we have been wielding them to solve intricate problems in the field of natural language processing, image processing, etc. Furthermore, we have been deploying complex deep learning models in real-time systems like autonomous vehicles, security cameras, etc purely based on their precision only to realize that these high precision models can be vulnerable to a variety of adversaries in the environment, that can hamper the overall robustness of our deep learning models. The contemporary defense strategies in the market either cannot alleviate a variety of adversarial attacks primarily in a white box environment or do not have a standardized approach that can be applied to any form of the complex deep-learning models to make them inert from a variety of adversaries. Moreover, there is a need for standardized adversarial defense strategies for mitigating a variety of adversarial attacks to make our models more robust in a white box environment. In this project, we make use of three different state-of-the-art deep-learning architectures trained on 2 benchmarking datasets – CIFAR-10 and CIFAR-100, to analyze the difference in the performance of these models in the absence of an adversary as well as in the presence of an adversary in a white-box environment. We primarily use two white box attack methodologies – Fast Gradient Sign Method (FGSM) and Projected Gradient Descent (PGD) to plant adversarial samples using epsilon values ranging from 0.1 to 0.8. Furthermore, we go one step further to devise a defense strategy – Defensive Distillation, that can be applied to a deep-learning architecture to deplete the overall efficacy of FGSM and PGD attacks.
URI: https://hdl.handle.net/10356/157133
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)

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