Deep neural networks are known to be susceptible to imperceptible adversarial perturbations. Many studies aim to interpret adversarial examples in the frequency domain. However, existing research often relies on a limited number of datasets, models, and adversarial attacks, leading to incomplete conclusions. Moreover, a quantitative interpretation of adversarial examples remains lacking. This paper proposes a quantitative frequency-based framework to comprehensively investigate adversarial examples, where six kinds of attacks against naturally and adversarially trained models across three datasets are adopted. Initially, our framework visualizes the distributions of successful adversarial perturbations in the frequency domain to locate their target regions. Subsequently, we characterize the importance of perturbations contained in different frequency bands and define adversarially effective frequency bands (AEFBs). Furthermore, we leverage the identified AEFBs to enhance two query-based black-box adversarial attacks. Our experimental results uncover the varying characteristics of adversarial perturbations, which are analyzed from dataset-level, model-level, and attack-level perspectives. After reordering frequency bands and identifying AEFBs, we further demonstrate that adversarial attacks guided by AEFBs can achieve superior performance, verifying their effectiveness and generalization. These significant findings contribute to a deeper understanding of adversarial examples and provide valuable insights for future research.