Recently, digital images have become used in many applications, where they have become the focus of digital image processing researchers. Image forgery represents one hot topic on which researchers prioritize their studies. We concentrate on the copy-move image forgery topic as a deceptive forgery type. In copy-move image forgery, a part of an image is copied and placed in the same image to produce the forgery image. In this paper, an accurate convolutional neural network(CNN) architecture is proposed for the effective detection of copy-move image forgery. The proposed architecture is computationally lightweight with a suitable number of convolutional and max-pooling layers. We also present a fast and accurate testing process with 0.83 seconds for every test. Many empirical experiments have been conducted to ensure the efficiency of the proposed model in terms of accuracy and time. These experiments were done on benchmark datasets and have achieved 100% accuracy.