On the Exploitation of DCT Statistics for Cropping Detectors

Claudio Ragaglia, Francesco Guarnera, Sebastiano Battiato

2024

Abstract

The study of frequency components derived from Discrete Cosine Transform (DCT) has been widely used in image analysis. In recent years it has been observed that significant information can be extrapolated from them about the lifecycle of the image, but no study has focused on the analysis between them and the source resolution of the image. In this work, we investigated a novel image resolution classifier that employs DCT statistics with the goal to detect the original resolution of images; in particular the insight was exploited to address the challenge of identifying cropped images. Training a Machine Learning (ML) classifier on entire images (not cropped), the generated model can leverage this information to detect cropping. The results demonstrate the classifier’s reliability in distinguishing between cropped and not cropped images, providing a dependable estimation of their original resolution. This advancement has significant implications for image processing applications, including digital security, authenticity verification, and visual quality analysis, by offering a new tool for detecting image manipulations and enhancing qualitative image assessment. This work opens new perspectives in the field, with potential to transform image analysis and usage across multiple domains.

Paper Citation

in Harvard Style

Ragaglia C., Guarnera F. and Battiato S. (2024). On the Exploitation of DCT Statistics for Cropping Detectors. In Proceedings of the 4th International Conference on Image Processing and Vision Engineering - Volume 1: IMPROVE; ISBN 978-989-758-693-4, SciTePress, pages 107-114. DOI: 10.5220/0012740900003720

in Bibtex Style

@conference{improve24,
author={Claudio Ragaglia and Francesco Guarnera and Sebastiano Battiato},
title={On the Exploitation of DCT Statistics for Cropping Detectors},
booktitle={Proceedings of the 4th International Conference on Image Processing and Vision Engineering - Volume 1: IMPROVE},
year={2024},
pages={107-114},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012740900003720},
isbn={978-989-758-693-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 4th International Conference on Image Processing and Vision Engineering - Volume 1: IMPROVE
TI - On the Exploitation of DCT Statistics for Cropping Detectors
SN - 978-989-758-693-4
AU - Ragaglia C.
AU - Guarnera F.
AU - Battiato S.
PY - 2024
SP - 107
EP - 114
DO - 10.5220/0012740900003720
PB - SciTePress