Soon after the first radio communications were invented in the late 19th century, radio interference problem arose. Spectrum monitoring helps managers plan and use spectrum resources more effectively, avoids incompatible use, and identifies harmful interference sources promptly (Lu et al., 2017;Papadias et al., 2020). The importance of anomaly detection is due to the fact that anomalies in data translate to significant, and often critical, actionable information in a wide variety of application domains (Chandola et al., 2009;Zhou et al., 2021). For example, anomalies in the radio spectrum could suggest that the interfering emitters, malfunctioning equipment, or malicious users within their licensed bands is occurring in real scenarios (O'Shea et al., 2016). Many radio services such as mobile communication, FM broadcasting and radio digital video broadcasting (DVB), need to deploy a large amount of base stations and radio transmitting stations to improve wireless coverage to achieve their goals. Once the radio equipment is interfered and attacked, the received signal will contain anomalies and the routine work will be interrupted. To make matters worse, it is hard to detect abnormal behavior in these systems due to the high level of variation in exceptions or attacks (Feng et al., 2017). Therefore, it is necessary to study various spectrum monitoring technologies for detecting and defending against interference (Calvo-Palomino et al., 2020;Lu et al., 2017). Many methods have been proposed to monitor the radio spectrum (Ali & Hamouda, 2016;Qin & Li, 2020). And energy detection has an improving performance by increasing the sampling points under low signal-to-noise ratio (SNR) environment when the noise power is precisely known (determined SNR). However, when there is uncertainty in the noise power, it is no longer effective to manipulate the sampling points (Tandra & Sahai, 2008), and covariance detection has the ability to overcome this weakness (Jia et al., 2015), unfortunately, it is invalid for locating anomalies in the spectrum. Recently, some more competitive approaches have been proposed and insights progressed, such as detection based on convolutional neural network (Conn & Josyula, 2019), adversarial autoencoder (AAE)-based anomaly detector (Rajendran et al., 2019), hidden Markov model detection (Allahdadi et al., 2021).In most cases, data resources are highly imbalanced toward examples of normality, while lacking in examples of abnormality (Akcay et al., 2019), which makes anomaly detection more challenging. Since the pattern of normal signals in radio monitoring is known, anomaly methods based on the generative adversarial network (GAN) may be a decent solution. GAN originally proposed by Goodfellow and Pouget-Abadie (2014), has gained a lot of attention in the computer vision community due to their capability of data generation without explicitly modeling the probability density function. GAN is a powerful generative