{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.}

通过离散余弦变换 (DCT) 导出的频率成分的研究已被广泛应用于图像分析。最近观察到这些成分可以对图像的生命周期提供重要信息，但尚无研究专注于它们与图像的源分辨率之间的分析。本研究调查了一种新的图像分辨率分类器，该分类器采用 DCT 统计数据，旨在检测图像的原始分辨率；特别是利用此洞察力来解决识别裁剪图像的挑战。通过在整个图像上训练机器学习 (ML) 分类器 (而非裁剪过的图像)，生成的模型可以利用此信息来检测裁剪。结果表明，该分类器可靠地区分裁剪和非裁剪图像，可可靠估计其原始分辨率。此项突破对图像处理应用具有重要意义，包括数字安全、真实性验证和视觉质量分析，为检测图像操作和提升图像质量评估提供了新工具。这项工作为该领域开启了新的视角，具有潜力改变多个领域中的图像分析与使用。