In the field of image copy-move forgery detection， it is very challenging to locate the boundaries of tampered small objects accurately. Current deep learning-based methods locate forged regions by detecting similar content in images. However， these methods usually just transmit the final features extracted by the encoder to the decoder to generate the mask， and ignore more spatial information of forged regions contained in the high-resolution encoding features， resulting in inaccurate model output prediction results for the boundary identification of small objects. To address this problem， a detection network based on multi-scale feature extraction and fusion called SimiNet was proposed. Firstly， abundant features were extracted by the multi-scale feature extraction module. Secondly， a skip connection was added between the feature extraction module and the decoding module to bridge the gap between the encoding and decoding features， so as to identify the boundaries of small objects accurately. Finally， Log-Cosh Dice Loss function was used to take the place of cross entropy loss to reduce the impact of class-imbalance problem on detection results. Experimental results show that the F1 score of SimiNet on USCISI dataset reaches 72.54%， which is 3.39 percentage points higher than that of the suboptimal method CMSDNet （Copy-Move Similarity Detection Network）. It can be seen that SimiNet is more accurate for boundary identification of small objects and has better visualization.