With the rapid development of digital image processing technology, image tampering methods have become increasingly complex, posing significant challenges to the identification of image authenticity. A network for image tampering detection based on dual-path feature fusion and dual-pooling adaptive compression was proposed to address issues such as insufficient feature extraction, scattered focus, and difficulty in distinguishing between noise and tampering features in image tampering detection. Firstly, dual-path feature fusion was utilized to extract image features from multiple perspectives, effectively highlighting the differences between tampered and untampered regions. Secondly, a dual-pooling adaptive compression mechanism was proposed, through the combination of global average pooling and maximum pooling, the network's ability to perceive global context was enhanced, and the channel compression ratio was adaptively adjusted according to the characteristics of input data, thereby improving feature representation capability and tampering detection accuracy. In addition, an adaptive attention feature fusion module was used to integrate multiple feature sources, fusing noise, edge, and texture information by means of an attention mechanism. To further optimize the convolutional structure, a residual feedback module embedded with gating signals was introduced to more accurately capture and strengthen task-related features, and dilated convolution was adopted to increase the receptive field, thus better capturing contextual information. Experimental results show that compared with methods such as multi-view multi-scale supervised networks (MVSS-Net), the proposed model increases the F₁ score by 2.8% and 3.3% on the CASIA dataset from the Institute of Automation, Chinese Academy of Sciences, and the COLUMB dataset from Columbia University, respectively. The method exhibits high accuracy and robustness in handling subtle tampering features, and has good adaptability and generalization ability in complex image tampering scenarios.