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首页> 外文会议>2019 7th International Symposium on Digital Forensics and Security >Predicting Stealthy Watermarks in Files Using Deep Learning
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Predicting Stealthy Watermarks in Files Using Deep Learning

机译：使用深度学习预测文件中的隐形水印

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摘要

Digital evidence is a fundamental element in cyber-forensics and judicial processes. However, the work of forensic examiners is becoming more challenging as the volume digital content and files increases. In this paper, we use machine learning especially deep learning to detect stealthy watermarks in various types of files. We use a black box approach which is different from current steganographic and cryptographic methods to find patterns of candidate file locations for hidden data We studied Deep Neural Networks (DNN) to predict stealthy watermarks in files using the deep learning implementation (DL4J) and Multilayer Perceptron (MLP) algorithms as implemented in Weka. We evaluated MLP models by altering the number of neurons and hidden layers while the DL4J models were evaluated by varying the number of dense layers and nodes. For the MLP models, DOCX & PPTX singleton models predicted stealthy watermarks in files with predictive accuracies ranging from 47.5% to 100%; JPEG singleton models registered predictive accuracies ranging from 35% to 65%. Comparatively, HYBRID3 models had predictive accuracies ranging from 42.5% to 95% while HYBRID_OOXML had predictive accuracies of 47.5% to 100%. However, JPEG_DOCX had predictive accuracies 47.5% to 97.5% while JPEG_PPTX had predictive accuracies of 40% to 85%. Furthermore for DL4J models, we only generated HYBRID3 models, which predicted stealthy watermarks in DOCX files with predictive accuracies 100%. The HYBRID3 DL4J model predicted stealthy watermarks in PPTX with predictive accuracies ranging from 55% to 82 % while in JPEG, the predictive accuracies from 50% to 52.5%. The major finding with deep learning also revealed improvements in prediction of stealthy watermarks in PPTX files using DL4J models.
机译：数字证据是网络取证和司法程序的基本要素。但是，随着数字内容和文件数量的增加，法医审查员的工作变得越来越具有挑战性。在本文中，我们使用机器学习（尤其是深度学习）来检测各种类型文件中的隐形水印。我们使用不同于当前隐写术和密码学方法的黑匣子方法来查找隐藏数据的候选文件位置模式。我们研究了深度神经网络（DNN），以使用深度学习实现（DL4J）和多层感知器来预测文件中的秘密水印。 （MLP）算法在Weka中实现。我们通过改变神经元和隐藏层的数量来评估MLP模型，而通过改变密集层和结点的数量来评估DL4J模型。对于MLP模型，DOCX和PPTX单例模型预测文件中的秘密水印，其预测准确度范围为47.5％至100％； JPEG单例模型的预测准确性介于35％至65％之间。相比之下，HYBRID3模型的预测精度为42.5％至95％，而HYBRID_OOXML的预测精度为47.5％至100％。但是，JPEG_DOCX的预测精度为47.5％至97.5％，而JPEG_PPTX的预测精度为40％至85％。此外，对于DL4J模型，我们仅生成了HYBRID3模型，该模型以100％的预测精度预测DOCX文件中的秘密水印。 HYBRID3 DL4J模型在PPTX中预测隐含水印，其预测准确度在55％至82％之间，而在JPEG中，预测准确度在50％至52.5％之间。深度学习的主要发现还揭示了使用DL4J模型预测PPTX文件中的隐形水印的改进。

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