高级数字图像取证:多媒体安全中复制-移动伪造检测的混合框架。

Arslan Akram, Muhammad Arfan Jaffar, Javed Rashid, Khalid Mahmood, Anwar Ghani
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引用次数: 0

摘要

特别是在验证图像完整性方面,数字图像分析的进步深刻地影响了法医调查。对数字图像技术的日益依赖可以部分归因于一致和有效的图像捕获技术的广泛可用性。由于先进的图像编辑技术,改变图像内容的简单性给法医分析带来了新的困难。提出了一种结构化的混合框架来寻找图像中的重要目标。它通过快速傅里叶变换(FFT)进行频域滤波,尺度不变特征变换(SIFT),定向fast和旋转BRIEF (ORB)提取关键点来实现这一点。MobilenetV2和VGG16模型从关键点区域提取特征,以检测复制-移动伪造。在那之后,注意机制将这些方面结合起来并使之正常化。关键点匹配使用欧几里得距离;DBSCAN聚类对目标定位的相关关键点进行分组。该方法在检测图像复制-移动伪造方面取得了较好的效果。该框架对图像模糊、对比度改变、色彩减少、图像压缩和亮度变化等后处理技术的鲁棒性进行了验证。由于照片被修改过,传统的方法可能难以应付大量的变化;然而,该方法结合了先进的深度学习模型和聚类技术,使检测更加准确。在五个基准复制-移动伪造数据集上的广泛测试表明,建议的策略可能胜过目前的技术。这项工作提供了一种复杂的自动化方法来保证数字图像的完整性和识别图像操作。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Advanced digital image forensics: A hybrid framework for copy-move forgery detection in multimedia security.

Particularly in validating image integrity, advances in digital image analysis have profoundly affected forensic investigation. The growing reliance on digital image technology can be attributed in part to the broad availability of consistent and effective image-capturing technologies. The simplicity of changing image content thanks to advanced image-editing technologies presents fresh difficulties for forensic analysis. A structured hybrid framework is presented for finding important objects in images. It does this by using fast Fourier transformation (FFT) for frequency domain filtering, scale-invariant feature transformation (SIFT), and oriented FAST and rotated BRIEF (ORB) to pull out key points. The MobilenetV2 and VGG16 models extract features from key point areas to detect copy-move forgery. After that, an attention mechanism combines and normalizes these aspects. Key point matching uses the Euclidean distance; DBSCAN clustering groups pertinent key points for object localization. The suggested approach shows better performance than current methods and detects image copy-move forgery rather successfully. The framework's robustness is verified against image blurring, contrast alteration, color reduction, image compression, and brightness change among other post-processing techniques. Since photographs are altered, traditional approaches can struggle with a lot of variety; however, the proposed method combines advanced deep learning models and clustering techniques to make detection more accurate. Extensive testing on five benchmark copy-move forgeries datasets reveals that the suggested strategy may beat present techniques. This work offers a sophisticated automated approach to guarantee digital image integrity and identify image manipulation.

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