Convolutional Neural Networks （CNN） are used for image forensics because of their high recognizable property， easy understanding， and strong learnability. However， their inherent disadvantages of the receptive field increasing slowly and neglecting long-range dependencies， and high computational cost cause the unsatisfactory accuracy and lightweight deployment of deep learning algorithms. To solve the above problems， a lightweight network-based image copy-paste tamper detection algorithm namely LKA-EfficientNet （Large Kernel Attention EfficientNet） was proposed. The characteristics of long-range dependencies and global receptive field were contained in LKA-EfficientNet， and the number of EfficientNetV2 parameters was optimized. As a result， the localization speed and detection accuracy of image tamper were improved. Firstly， the image was inputted into and processed in the backbone network based on Large Kernel Attention （LKA） to obtain the candidate feature maps. Then， the feature maps of different scales were used to construct the feature pyramid for feature matching. Finally， the candidate feature maps after feature matching were fused to locate the tampered area of the image. In addition， the triple cross entropy loss function was used by LKA-EfficientNet to further improve the accuracy of the algorithm in image tamper localization. Experimental results show that LKA-EfficientNet can not only reduce the floating-point operations by 29.54% but also increase the F1 by 4.88% compared to the same type algorithm — Dense-InceptionNet. The above verifies that LKA-EfficientNet can reduce computational cost and maintain high detection performance at the same time.