Evasion Attacks on Deep Learning-Based Helicopter Recognition Systems

Abstract

Identifying objects in surveillance and reconnaissance systems with the human eye can be challenging, underscoring the growing importance of employing deep learning models for the recognition of enemy weapon systems. These systems, leveraging deep neural networks known for their strong performance in image recognition and classification, are currently under extensive research. However, it is crucial to acknowledge that surveillance and reconnaissance systems utilizing deep neural networks are susceptible to vulnerabilities posed by adversarial examples. While prior adversarial example research has mainly utilized publicly available internet data, there has been a significant absence of studies concerning adversarial attacks on data and models specific to real military scenarios. In this paper, we introduce an adversarial example designed for a binary classifier tasked with recognizing helicopters. Our approach generates an adversarial example that is misclassified by the model, despite appearing unproblematic to the human eye. To conduct our experiments, we gathered real attack and transport helicopters and employed TensorFlow as the machine learning library of choice. Our experimental findings demonstrate that the average attack success rate of the proposed method is 81.9%. Additionally, when epsilon is 0.4, the attack success rate is 90.1%. Before epsilon reaches 0.4, the attack success rate increases rapidly, and then we can see that epsilon increases little by little thereafter.