Abstract
Image sharpening is one of the basic operations used to improve the visual effect of images. Image editing makes it impossible to confirm the authenticity of an image, and the purpose of image forensics is to detect whether an image has been artificially edited. To solve this problem, a forensic algorithm based on global image pixel values is proposed. Twelve difference sets composed of first-order and second-order differences in different directions are used as the image feature to reveal the difference between image pixels. Additionally, the feature is used to determine whether the image has been sharpened or not. To verify the performance of the algorithm, a series of experiments are performed on different formats of image datasets. Compared with the existing forensic algorithms based on traditional machine learning, the detection accuracy of the proposed method is greatly improved.
Highlights
With the rapid development of the internet and information technology, the digital image has become one of the most considered information carriers
We propose an image sharpening detection algorithm based on all pixel points of the image
7/10 positive and negative samples are randomly selected for the support vector machine (SVM) model training, and the remaining 3/10 samples are used for model testing
Summary
With the rapid development of the internet and information technology, the digital image has become one of the most considered information carriers. Image editing software, such as Photoshop, Fireworks and so on, are used to obtain a better visual effect for the image. The editing of digital images has become easier, and the tampering traces are more difficult to be found with the rapid development of digital techniques. We cannot judge the authenticity of the image, and the credibility of the image is seriously threatened. When the tampered images are used in newspapers, court evidence and other fields, it may have a negative impact on society. Digital image forensic technology has become one of the hot research issues in the past decades [1]–[6]
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