Evaluating the Robustness of Neural Networks Against Adversarial Perturbations
Chandrasekhar Rohith Bhat, J. Nandhini, Narayanasamy S, B. Karthik, Prasanna Kumar Lakineni, Syed Noeman Taqui · 2023
N eural networks have impressive powers in the field of deep learning and have been used in many different applications. Nevertheless, the issue of robustness against deliberate harmful inputs, commonly referred to as adversarial perturbations, continues to be a prominent topic. The presence of these perturbations, which are frequently undetectable by human observers, has the potential to cause erroneous predictions in models, hence presenting potential hazards to crucial domains such as autonomous vehicles or medical diagnostics. The primary objective of this study is to assess the vulnerability of neural networks to adversarial perturbations, ascertain the underlying factors contributing to this susceptibility, and investigate viable solutions for mitigating such vulnerabilities. Various adversarial attack strategies were utilized on diverse neural network topologies, and their performances were assessed. The results of our study suggest that whereas deeper models exhibit enhanced classification abilities, they may not necessarily possess more resilience against hostile inputs. Additionally, it has been found that certain conventional regularization techniques might further amplify susceptibility. In order to address these problems, innovative training techniques and adjustments to the network structures are implemented, resulting in enhanced resilience against adversarial attacks. This study highlights the significance of developing neural networks that possess both accuracy and robustness, hence advancing the frontier of safe deep learning applications.