Challenges and Advances in Securing Deep Learning Models: A Comprehensive Review

The deep learning models have transformed many processes, making them make breakthroughs in tasks like image recognition, natural language processing, and automotive. Nevertheless, their implementation is full of security gaps that jeopardize their integrity and confidentiality as well as strength. In this paper, the recent progress on neural network security has been extensively reviewed as it is crucial to consider the security-related challenges including the adversarial attacks, data poisoning, and model inversion. We also conduct a review of the literature in an organized way to recognize common challenges and analyze the current defense algorithms, such as cryptographic methods, federated learning, and adversarial training. Moreover, the analysis of the state-of-the-art approaches, which is conducted in a different manner, shows the gaps and future areas of enhancing the security of DL. The proposed review will inform researchers and practitioners of the current changes in the threat sphere and the development of new innovative solutions to make deep learning systems deployment secure in the most important applications.