Abstract
There are many image resizing techniques, which include scaling, scale-and-stretch, seam carving, and so on. They have their own advantages and are suitable for different application scenarios. Therefore, a universal detection of tampering by image resizing is more practical. By preliminary experiments, we found that no matter which image resizing technique is adopted, it will destroy local texture and spatial correlations among adjacent pixels to some extent. Due to the excellent performance of local Tchebichef moments (LTM) in texture classification, we are motivated to present a detection method of tampering by image resizing using LTM in this paper. The tampered images are obtained by removing the pixels from original images using image resizing (scaling, scale-and-stretch and seam carving). Firstly, the residual is obtained by image pre-processing. Then, the histogram features of LTM are extracted from the residual. Finally, an error-correcting output code strategy is adopted by ensemble learning, which turns a multi-class classification problem into binary classification sub-problems. Experimental results show that the proposed approach can obtain an acceptable detection accuracies for the three content-aware image re-targeting techniques.
Highlights
As image editing tools and various mobile devices are acquired and conveniently used, maximizing the viewing experience of end users on small devices becomes very important
By the principle analysis of the three image resizing methods, we found that the correlation between adjacent pixels can be destroyed in the process of image resizing
It can be found from experiments that local Tchebichef moments (LTM) can effectively reflect the correlation changes between adjacent pixels
Summary
As image editing tools and various mobile devices are acquired and conveniently used, maximizing the viewing experience of end users on small devices becomes very important. Compared to traditional image re-targeting methods, such as linear scaling and cropping, many content-aware image resizing methods can preserve salient areas, avoiding serious distortions or loss of significant information[1,2,3]. Many content-aware resizing algorithms have been adopted using image editing tools, such as photoshop and GIMP. An ordinary user can very create tampered images for malicious purposes using image editing tools. It is impossible to distinguish those tampered images from authentic images with the naked eye. How to detect tampered images is a hot topic in the field of image content security
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