Remote sensing image tampering detection is crucial in information security due to advanced image generation techniques. Previous research has not sufficiently explored generalization ability in satellite imagery. This paper firstly introduces domain generalization for satellite image tampering detection. Conventional spatial patterns vary across tampering methods, leading to insufficient information for feature extraction. We propose a Multi-Scale Frequency-Aware Representation Learning framework, namely MSFA-Net, which integrates spatial and frequency-domain knowledge to extract domain-invariant tampering features. To enhance local and global semantic correlation from different scale feature maps, we introduce a multi-scale contextual attention module. Furthermore, a CLIP-Guided Semantic Knowledge Distillation module is proposed to boost the generalizable model’s robustness through visual language knowledge. Extensive experiments conducted on three remote-sensing benchmarks demonstrate our framework’s effectiveness and superior cross-domain performance.