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Robust Detection of Image Operator Chain with Two-stream Convolutional Neural Network
IEEE Journal of Selected Topics in Signal Processing ( IF 7.5 ) Pub Date : 2020-08-01 , DOI: 10.1109/jstsp.2020.3002391
Xin Liao , Kaide Li , Xinshan Zhu , K. J. Ray Liu

Many forensic techniques have recently been developed to determine whether an image has undergone a specific manipulation operation. When multiple consecutive operations are applied to images, forensic analysts not only need to identify the existence of each manipulation operation, but also to distinguish the order of the involved operations. However, image operator chain detection is still a challenging problem. In this paper, an order forensics framework for detecting image operator chain based on convolutional neural network (CNN) is presented. Two-stream CNN architecture is designed to capture both tampering artifact evidence and local noise residual evidence. Specifically, the new CNN-based method is proposed for forensically detecting a chain made of two image operators, which could automatically learn manipulation detection features directly from image data. Further, we empirically investigate the robustness of our proposed method in two practical scenarios: forensic investigators have no access to the operating parameters, and manipulations are applied to a JPEG compressed image. Experimental results show that the proposed framework not only obtains significant detection performance but also can distinguish the order in some cases that previous works were unable to identify.

中文翻译:

两流卷积神经网络图像算子链的鲁棒检测

最近开发了许多取证技术来确定图像是否经历了特定的操作操作。当对图像应用多个连续操作时,取证分析人员不仅需要识别每个操作操作的存在,还需要区分所涉及操作的顺序。然而,图像算子链检测仍然是一个具有挑战性的问题。在本文中,提出了一种基于卷积神经网络(CNN)的用于检测图像算子链的订单取证框架。双流 CNN 架构旨在捕获篡改伪影证据和局部噪声残留证据。具体来说,提出了基于 CNN 的新方法,用于取证检测由两个图像算子组成的链,它可以直接从图像数据中自动学习操纵检测特征。此外,我们在两个实际场景中凭经验研究了我们提出的方法的稳健性:法医调查人员无法访问操作参数,并且对 JPEG 压缩图像进行操作。实验结果表明,所提出的框架不仅获得了显着的检测性能,而且在一些以前的工作无法识别的情况下也能区分顺序。
更新日期:2020-08-01
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