LAUSR

Seam carving is a representative content-aware image retargeting approach to adjust the size of an image. To preserve visually prominent content, seam-carving algorithms first calculate the connected path of pixels, referred to as the seam, according to a defined cost function and then adjust the size of an image by removing or duplicating repeatedly calculated seams. Seam carving is actively exploited to overcome diversity in the resolution of images between applications and devices; hence, detecting the distortion caused by seam carving has become important in image forensics. In this paper, we propose a convolutional neural network (CNN)-based approach to classifying seam-carving forgery. To attain the ability to learn low-level features, we designed a convolutional neural network (CNN) architecture comprising five types of network blocks specialized in capturing local artifacts caused by seam carving. An ensemble module is further adopted to both enhance performance and comprehensively analyze the features in the local areas. To validate the effectiveness of our work, extensive experiments based on various CNN-based baselines were conducted. Compared to the baselines, our work exhibits state-of-the-art performance in terms of three-class classification (original, seam inserted, and seam removed). The experimental results also demonstrate that our model with the ensemble module is robust for various unseen cases.