An interpretability security framework for intelligent decision support systems based on saliency map

Benefiting from the high-speed transmission and super-low latency, the Fifth Generation (5G) networks are playing an important role in contemporary society. The accessibility and friendly experience provided by 5G results in the generation of massive data, which are recklessly transmitted in various forms and in turn, promote the development of big data and intelligent decision support systems (DSS). Although AI (Artificial Intelligence) can boost DSS to obtain high recognition performance on large-scale data, an adversarial sample generated by deliberately adding subtle noise to a clear sample will cause AI models to give false output with high confidence, which increases concerns about AI. It is necessary to enhance its interpretability and security when adopting AI in areas where decision-making is crucial. In this paper, we study the challenges posed by the next-generation DSS in the era of 5G and big data. To build trust in AI, the saliency map is adopted as a visualization method to reveal the vulnerability of neural networks. The visualization method is further taken to identify imperceptible adversarial samples and reasons for the misclassification of high-accuracy models. Finally, we conduct extensive experiments on large-scale datasets to verify the effectiveness of the visualization method in enhancing AI security for 5G-enabled DSS.