On the exploitation of DCT statistics for cropping detectors
This work addresses image manipulation detection for applications in digital security and authenticity verification, representing an incremental advancement in image analysis.
The authors tackled the problem of detecting cropped images by developing a novel image resolution classifier using DCT statistics, which reliably distinguishes between cropped and not cropped images and estimates original resolution. The results demonstrate the classifier's dependability in these tasks.
{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.}