Iris textures are easily hidden or even forged by textured contact lenses, which further threatens the security of the iris recognition system. Considering the tiny differences in the optical properties and texture features of authentic irises and irises forged by textured contact lenses, this paper proposes an anti-spoofing detection method for contact lens irises based on recurrent attention, namely recurrent attention iris net (RAINet). Specifically, the recurrent attention mechanism is employed to locate the key regions that can be used to distinguish authentic irises from forged ones in an unsupervised manner, and multi-level feature fusion is applied to improve the anti-spoofing detection accuracy. An end-to-end anti-spoofing detection network is built for the direct detection of authentic and forged features without image pre-processing. MobileNetV2 is used as the feature classification network to reduce the number of parameters and amount of computation of the network in addition to maintaining the detection accuracy. Experimental verification is performed on two public databases (IIITD CLI and ND series) containing both authentic iris samples and contact lens iris samples. The results show that the proposed RAINet outperforms other anti-spoofing detection networks in detection accuracy. Its average correct classification rates under intra-sensor, inter-sensor, and inter-database experimental conditions reach 99.93%, 97.31%, and 97.86%, respectively.