Image Tampering Detection Method Based on Dilated Convolution
Luhao Tian, De Li, Xun Jin · 2024
Image tampering is a common digital image processing technique that involves modifying original images through operations such as copying, cutting, pasting, and blending, with the aim of deceiving or falsifying information. With the rapid development of digital imaging technology and deep learning, image tampering detection has become a crucial research field. To address this issue, this paper proposes an image tampering detection method based on dilated convolution and anomaly feature extraction. The method introduces dilated convolutions to increase the network's receptive field and utilizes anomaly feature extraction to identify and pinpoint areas of anomaly in tampered images. Experimental results demonstrate that the proposed method achieves good performance on different test datasets, indicating promising applications in practice.