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Encryption-based Adversarial Defense for Resilient Facial Recognition Deep Learning Models
Alsharabi, Reem Dalloul, Dina Almalki, Leen
Alsharabi, Reem
Dalloul, Dina
Almalki, Leen
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Abstract
acial recognition is a vital technology that has been widely adopted for various applications, including security and identification. However, facial recognition models are vulnerable to adversarial attacks, raising concerns about their security and reliability. This project investigates the impact of encryption on the security and robustness of facial recognition deep learning models (FR-DL) against adversarial attacks. A comprehensive literature review was conducted to identify research gaps, explore defense methods, evaluate datasets, and examine model accuracies. Based on our literature review, image transformation (encryption) has been identified as a de- fense method that oers a high level of reliability, feasible implementation, and demonstrated accuracy. This study will investigate weather deep learning models can eectively learn from images that have been encrypted, and how can encryption improve their robustness against adversarial attacks. The proposed methodology involves data collection and processing to curate a suitable dataset. Leveraging the expansive and diverse VGGFace2 dataset, we will train and test deep learning models. Pixel shuing will be applied to the dataset as the encryp- tion method. The resultant encrypted data will serve as the foundation for building and training the models. Rigorous testing will assess the models’ resilience against adversarial attacks. Continuous performance analysis and accuracy assessments will be integral, aiming to achieve a 90% accuracy rate throughout the process. The expected outcome of the project is to provide valuable insight into how en- cryption impacts the robustness of deep learning models against adversarial attacks, contributing to the development of secure AI systems. This aligns with Vision 2030, transforming Saudi Arabia into a modern, economically, and socially vibrant nation.