Existing tampering localization methods perform poorly when localizing scaled images that are missing high-frequency forensic traces. Preserving these traces is critical for accurately localizing tampering because scaling operations can obscure crucial details and introduce artifacts and distortions. To address this issue, we propose a simple yet effective tampering localization framework for scaled images, named SLRID. The framework consists of two components: the Symlet Wavelet Recovery Module (SLR) and the Detector Module (SE-RRU-net). The SLR employs an invertible network of the Symlet Wavelet Transform to simulate the loss of information in a scaled image caused by tampering, achieving high-fidelity restoration of high-frequency forensic traces. The SE-RRU-net network utilizes an end-to-end image segmentation network (RRU-net) for tampering localization, incorporating a Spatial Channel Squeezing and Excitation (SCSE) mechanism to enhance the extraction of forgery-related features in scaled images. Results indicate that the SLRID framework outperforms existing state-of-the-art methods in detecting forged images during scaling operations.