IMGCAT:一种利用相关特征和集成一维卷积神经网络解除源相机匿名性的方法

IF 2.3 Q2 COMPUTER SCIENCE, THEORY & METHODS
Array Pub Date : 2023-07-01 DOI:10.1016/j.array.2023.100279
Muhammad Irshad , Ngai-Fong Law , K.H. Loo , Sami Haider
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引用次数: 1

摘要

随着智能手机的普及,数字数据收集变得微不足道。分析图像的能力有所提高,但源身份验证却停滞不前。随着信号处理技术的进步,对图像进行编辑和篡改变得越来越普遍。最近的发展介绍了接缝雕刻(插入和删除)技术的使用来掩饰相机的身份,特别是在儿童色情市场。在本文中,我们主要研究基于PRNU(照片响应不均匀性)的图像中的可用特征。强制接缝雕刻技术是一种众所周知的方法,通过向每个50 × 50像素块注入接缝来创建相机归属的遮挡。为了解决这个问题,我们使用集成了20 × 20像素块特征提取的1D CNN进行相机识别。我们提出的IMGCAT(图像分类)在基线上的三类分类(原始,接缝移除,接缝插入)中实现了最先进的性能。根据我们的实验结果,我们的模型在处理与可疑相机相关的盲事实时是稳健的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
IMGCAT: An approach to dismantle the anonymity of a source camera using correlative features and an integrated 1D convolutional neural network

With the proliferation of smartphones, digital data collection has become trivial. The ability to analyze images has increased, but source authentication has stagnated. Editing and tampering of images has become more common with advancements in signal processing technology. Recent developments have introduced the use of seam carving (insertion and deletion) techniques to disguise the identity of the camera, specifically in the child pornography market. In this article, we focus on the available features in the image based on PRNU (photo response nonuniformity). The forced-seam sculpting technique is a well-known method to create occlusion for camera attribution by injecting seams into each 50 × 50 pixel block. To counter this, we perform camera identification using a 1D CNN integrated with feature extractions on 20 × 20 pixel blocks. We achieve state-of-the-art performance for our proposed IMGCAT (image categorization) in three-class classification over the baselines (original, seam removed, seam inserted). Based on our experimental findings, our model is robust when dealing with blind facts related to the questionable camera.

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来源期刊
Array
Array Computer Science-General Computer Science
CiteScore
4.40
自引率
0.00%
发文量
93
审稿时长
45 days
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