Optimization of a Pre-Trained AlexNet Model for Detecting and Localizing Image Forgeries
Optimization of a Pre-Trained AlexNet Model for Detecting and Localizing Image Forgeries
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DOI：
10.3390/info11050275
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发表时间：
2020-05-01
期刊：
影响因子：
3.1
通讯作者：
Onsi, Hoda
中科院分区：
文献类型：
--
作者：
Samir, Soad;Emary, Eid;Onsi, Hoda
随着许多图像处理工具的进步，进行图像伪造和隐藏伪造变得越来越容易。本文讨论了卷积神经网络（CNN）用于图像伪造检测和定位的新方法。提出了一种基于AlexNet框架的图像伪造检测模型。我们提出了一个改进的模型来优化AlexNet模型，它使用批量归一化而不是局部响应归一化，使用maxout激活函数而不是整流线性单元，并在最后一层使用softmax激活函数作为分类器。因此，AlexNet提出的模型可以进行特征提取以及forests的检测，而无需进一步的操作。通过大量的实验，我们研究并区分了几个重要的AlexNet设计选择的影响。所提出的网络模型应用于CASIA v2.0，CASIA v1.0，DVMM和NIST Nimble Challenge 2017数据集。我们还对数据集应用k折交叉验证，将其分为训练和测试数据样本。实验结果表明，该模型对不同类型的入侵检测都有很好的效果。定量性能分析表明，该模型可以检测到图像伪造的准确率为98.176%。
With the advance of many image manipulation tools, carrying out image forgery and concealing the forgery is becoming easier. In this paper, the convolution neural network (CNN) innovation for image forgery detection and localization is discussed. A novel image forgery detection model using AlexNet framework is introduced. We proposed a modified model to optimize the AlexNet model by using batch normalization instead of local Response normalization, a maxout activation function instead of a rectified linear unit, and a softmax activation function in the last layer to act as a classifier. As a consequence, the AlexNet proposed model can carry out feature extraction and as well as detection of forgeries without the need for further manipulations. Throughout a number of experiments, we examine and differentiate the impacts of several important AlexNet design choices. The proposed networks model is applied on CASIA v2.0, CASIA v1.0, DVMM, and NIST Nimble Challenge 2017 datasets. We also apply k-fold cross-validation on datasets to divide them into training and test data samples. The experimental results achieved prove that the proposed model can accomplish a great performance for detecting different sorts of forgeries. Quantitative performance analysis of the proposed model can detect image forgeries with 98.176% accuracy.