2022/03/05 by Thierry Pinheiro Moreira, Marcos Cleison Silva Santana, Moreira, Thierry P. +5
Computer Science · Medicine · #Computer Vision and Pattern Recognition (cs.CV) #Digital Media Forensic Detection #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #Retinal Imaging and Analysis
paper · pdf · doi:10.48550/arxiv.2203.02728
openalex publication_date 2022/03/05 · openalex created_date 2022/08/27 · openalex updated_date 2026/07/28
Seam carving is a computational method capable of resizing images for both reduction and expansion based on its content, instead of the image geometry. Although the technique is mostly employed to deal with redundant information, i.e., regions composed of pixels with similar intensity, it can also be used for tampering images by inserting or removing relevant objects. Therefore, detecting such a process is of extreme importance regarding the image security domain. However, recognizing seam-carved images does not represent a straightforward task even for human eyes, and robust computation tools capable of identifying such alterations are very desirable. In this paper, we propose an end-to-end approach to cope with the problem of automatic seam carving detection that can obtain state-of-the-art results. Experiments conducted over public and private datasets with several tampering configurations evidence the suitability of the proposed model.