We aim to address the near-duplicate image (NDI) detection problem with a deep learning network. With the advancement of digital acquisition devices and easy-to-use image editing software, NDI forgery is ubiquitous nowadays. This rising problem demands robust detection algorithms that can efficiently prevent the NDI forgery and its distribution. We present a modified deep learning model to detect NDI forensics attacks based on extracted wavelet Haar features. Unlike standard deep learning models, a wavelet decomposed preprocessing layer is used before the deep learning networks, and a support vector machine classifier is employed in the classification stage of a modified deep learning model. The model is tested on distinctly available image databases. The experimental results show that the proposed model more effectively detect the NDIs. In addition, the comparison result shows that the modified model outperforms the other approaches significantly.