Large Feature Mining and Deep Learning in Multimedia Forensics

Qingzhong Liu, Naciye Celebi
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引用次数: 1

Abstract

As one of the most interesting areas in cyber forensics, multimedia forensics faces many challenges as users are generating a humongous amount of data with different operations. Forgery detection and steganography detection are two hotspots in multimedia forensics. To solve some highly challenging problems in multimedia forensics, especially in image forensics, we will introduce in this tutorial large feature mining-based approaches with ensemble learning in image forgery detection, including seam-carving forgery and inpainting forgery in JPEG images with the subsequent anti-forensics' operations. We will also introduce deep learning and apply the well-known deep learning models that were transferred and used for image forgery detection and image steganalysis, which considerably improve the detection accuracy.
多媒体取证中的大特征挖掘和深度学习
作为网络取证中最有趣的领域之一,多媒体取证面临着许多挑战,因为用户正在通过不同的操作生成大量的数据。伪造检测和隐写检测是多媒体取证研究的两个热点。为了解决多媒体取证中一些极具挑战性的问题,特别是在图像取证中,我们将在本教程中介绍图像伪造检测中基于集成学习的基于大特征挖掘的方法,包括接缝雕刻伪造和在JPEG图像中绘制伪造,以及随后的反取证操作。我们还将引入深度学习,并将众所周知的深度学习模型应用于图像伪造检测和图像隐写分析,这大大提高了检测精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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