A Training-based Identification Approach to VIN Adversarial Examples
This addresses a security threat in AI applications like robot path planning by automating the identification of adversarial examples, though it is incremental as it builds on existing VIN methods.
The paper tackles the problem of automatically identifying adversarial examples in Value Iteration Networks (VIN) for robot path planning, where adversarial maps add obstacles to cause false results, and proposes a training-based approach that achieves high-accuracy and faster identification compared to manual methods.
With the rapid development of Artificial Intelligence (AI), the problem of AI security has gradually emerged. Most existing machine learning algorithms may be attacked by adversarial examples. An adversarial example is a slightly modified input sample that can lead to a false result of machine learning algorithms. The adversarial examples pose a potential security threat for many AI application areas, especially in the domain of robot path planning. In this field, the adversarial examples obstruct the algorithm by adding obstacles to the normal maps, resulting in multiple effects on the predicted path. However, there is no suitable approach to automatically identify them. To our knowledge, all previous work uses manual observation method to estimate the attack results of adversarial maps, which is time-consuming. Aiming at the existing problem, this paper explores a method to automatically identify the adversarial examples in Value Iteration Networks (VIN), which has a strong generalization ability. We analyze the possible scenarios caused by the adversarial maps. We propose a training-based identification approach to VIN adversarial examples by combing the path feature comparison and path image classification. We evaluate our method using the adversarial maps dataset, show that our method can achieve a high-accuracy and faster identification than manual observation method.