Abstract
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.
Highlights
Motivated by the massive use of social media e.g., Facebook, Instagram, and Twitter, etc. and enhancements in image processing software applications, image forgery has become very popular and the need for image forgery detection has increased.Image manipulations that are done by the procedure of clipping and pasting areas, are one of the most well-known forms of digital image editing
Sensitivity or True Positive Rate (TPR) or Probability of Detection (PD) or Recall (r): A quotient of examples absolutely detected as X to all examples that were exactly X
In this sub-section, we demonstrate the analysis and achievement of the proposed convolution neural network (CNN) model for image forgery detection
Summary
Motivated by the massive use of social media e.g., Facebook, Instagram, and Twitter, etc. and enhancements in image processing software applications, image forgery has become very popular and the need for image forgery detection has increased. The detection models proposed are beneficial to many applications in which the authenticity of a to this, there are numerous editing processes executed on the forged areas to appear digital image has an influential impact. Some questions exist with the account to CNNs design and training for image forgery should be able to detect all types of manipulation editing rather than focalizing on a certain type. We conduct a large scale experimental evaluation of the proposed architecture and show that it can outperform existing image manipulation detection techniques, can differentiate between multiple editing operations even when their parameters change, can localize fake detection results, and can provide excessively accurate forgery detection results when trained using a huge training dataset.
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