In the field of image forensics， image splicing detection technology can identify splicing and locate the splicing area through the analysis of image content. However， in common scenarios like transmission and scanning， salt-and-pepper （s&p） noise appears randomly and inevitably， and as the intensity of the noise increases， the current splicing forensic methods lose effectiveness progressively and might ultimately fail， thereby significantly impacting the effect of existing splicing forensic methods. Therefore， a splicing forensic algorithm against high-intensity s&p noise was proposed. The proposed algorithm was divided into two main parts： preprocessing and splicing forensics. Firstly， in the preprocessing part， a fusion of the ResNet32 and median filter was employed to remove s&p noise from the image， and the damaged image content was restored through the convolutional layer， so as to minimize the influence of s&p noise on splicing forensic part and restore image details. Then， in the splicing forensics part， based on the Siamese network structure， the noise artifacts associated with the image’s uniqueness were extracted， and the spliced area was identified through inconsistency assessment. Experimental results on widely used tampering datasets show that the proposed algorithm achieves good results on both RGB and grayscale images. In a 10% noise scenario， the proposed algorithm increases the Matthews Correlation Coefficient （MCC） value by over 50% compared to FS（Forensic Similarity） and PSCC-Net（Progressive Spatio-Channel Correlation Network） forensic algorithms， validating the effectiveness and advancement of the proposed algorithm in forensic analysis of tampered images with noise.