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图像绘制伪造检测:一篇评论

Adrian-Alin Barglazan¹, Remus Brad¹, Constantin Constantinescu¹

¹Faculty of Engineering, Computer Science, "Lucian Blaga" University of Sibiu, 550024 Sibiu, Romania.

Journal of imaging

|February 23, 2024

概括

本文审查了基于机器学习的图像绘制和伪造检测方法,用于移除对象. 它突出了绘画中的文物,并评估了当前的检测技术,数据集及其局限性.

科学领域:

计算机视觉计算机视觉
机器学习机器学习
数字法医学数字法医学

背景情况:

机器学习的进步改善了图像恢复,但也使复杂的图像操纵成为可能.
随着互联网对操纵的多媒体信息的扩散,需要强大的检测方法.

研究的目的:

从数字图像中删除对象的 inpainting 算法和伪造检测技术进行全面审查.
分析传统和基于神经网络的方法,识别绘制文物,并评估最先进的检测技术.

主要方法:

审查纹理合成和基于神经网络的绘画技术.
通过inpainting移除物体引入的文物分析.
评估当前的伪造检测方法和相关数据集.

主要成果:

油漆技术,在提高图像质量的同时,引入可检测的文物.
现有的检测方法在有效性上有所不同,与不同的涂漆策略相比.
对方法的比较分析揭示了当前的能力和限制.

结论:

了解绘制文物对于有效的伪造检测至关重要.
该研究提供了对检测图像中物体移除的最先进技术的全面概述.
需要进一步的研究来解决当前检测方法的局限性.

关键词:

法医伪造法医伪造在painting中的图像.对象移除检测检测物体移除检测

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