Objective With the advancement of digital technology in recent years image tampering has emerged as a growing concern. Many existing methods for image tampering and object detection suffer from inadequate recognition accuracy and unsatisfactory detection effects. To address this challenge more effectively a detection algorithm based on an improved YOLOv8 was proposed aiming to achieve higher detection accuracy. Methods Firstly to capture the edge features of tampering a local and global attention mechanism was introduced to deeply optimize the backbone network of YOLOv8. This optimization combined context awareness and local enhancement techniques significantly enhancing the recognition of edge features. Considering the diverse shapes of tampered regions a network structure of stacked feature pyramids was further adopted to capture multi-scale features. Finally to improve the computational efficiency and inference speed of the model deep separable convolutions and channel shuffling were integrated into the model. Results In a series of experiments the improved YOLOv8 tampering detection algorithm demonstrated excellent performance on the CASIA2. 0 image tampering dataset. Compared with the original algorithm it achieved an accuracy as high as 82. 3%, significantly enhancing detection effectiveness. The proposed tampering detection algorithm based on the improved YOLOv8 through deep network optimization and structural adjustments successfully improved both the accuracy and efficiency of image tampering detection. Conclusion The proposed method in the paper demonstrates high accuracy in image tampering detection representing significant advancements in this field. This study has profound and meaningful implications for the field of image tampering detection.