Convolutional neural network designed as small truth tables, application to cryptography, formal verification & explainability - Benamira, Adrien (Nanyang Technological University, 2024)
DCNNs have revolutionized machine learning (ML) and deep learning (DL), achieving remarkable success in image recognition, natural language processing, and other domains. However, their deployment in security-critical industries such as cybersecurity, energy, finance, and the military faces persistent safety and reliability issues. This thesis addresses three primary challenges: the absence of global and precise interpretability in DCNNs, the need for exact, comprehensive, sound, and efficient formal verification of DCNN properties, and the looming privacy threat to user input in cloud-based DCNN deployments for sensitive data. Our research aims to enhance the transparency, reliability, and security of DCNNs, facilitating their wider adoption in critical real-world applications.