Navigating The Battleground: An Analysis Of Adversarial Threats And Protections In Deep Neural Networks

Deep learning techniques find broad applications in important areas such as malware detection systems, self-driving cars, and health care. However, it is still possible to attack deep learning soft intelligent models using adversarial example approaches. This study investigates the works and findings of recent research, whose central focus has been on adversarial machine learning, its weaknesses, and its application. Some of the focus has been on malware attacks that use deep learning frameworks, such as neural network based Jacobian saliency map attacks and anatomy of the Carlini and Wagner attacks. This has again posed a limitation to the present-day research on the increasing range of the actors and their role within the range of types of attack as well as strategies and development of countermeasures, including making biases and models to mitigate the threats. Furthermore, there is also a deficiency in the evaluation of defence mechanisms, particularly in developing appropriate parameters that would demonstrate the efficacy of the existing models. In conclusion, this paper presents communication and deliberations on areas that offer an interesting promise for future research in which the challenges experienced in applying deep learning systems will be satisfactorily addressed.