In response to the growing importance of detecting maliciously altered images to mitigate their harmful effects, we propose a deep learning-based image tampering detection method that incorporates multiscale fusion and anomaly assessment. This approach addresses the limitations of existing methods that often struggle to detect diverse tampering types and exhibit suboptimal precision and localization performance. The proposed method employs a Channel-Spatial Attention module to enhance feature representations extracted from multiscale input images, thereby capitalizing on both spatial and channel-wise dependencies within the data. Furthermore, it uses a Z-score scoring mechanism and an LSTM-based mechanism to effectively capture and evaluate anomalous regions within the image. These components collectively contribute to a more robust identification of the manipulated content. For training supervision, we introduce a binary cross entropy loss, which jointly optimizes pixel-level classification and regression tasks, ensuring accurate tampering detection and localization. Experimental evaluations demonstrate that our method significantly outperforms prevailing tampering detection techniques, exhibiting an increase in AUC values ranging from 21% to 62% and achieving up to a 99.8% improvement in the best F1 score. Specifically, on benchmark datasets CASIA1.0, Coverage, and NIST16, our method attains F1 scores of 0.673, 0.714, and 0.981, respectively, underscoring its superior performance across diverse scenarios and tampering types.