As image modification and tampering, especially multiple editing operations, prevail in today's world, identifying authenticity and credibility of digital images becomes increasingly important. Recently, two editing operations, upsampling and mean filtering, have attracted increasing attention. While there are many existing image forensics techniques to identify the existence and order of specific operations in a certain processing chain, few detecting methods are concerned about the order of upsampling and mean filtering operations. Following some strongly indicative analysis in different domains of DFTs of images' p-maps, this paper discusses a newly designed method which utilizes features to determine the order of upsampling and mean filtering operations. Specifically, our goal is to use two features, the symmetry-based PSNR and the fourth order energy fitting curve, to characterize the features of operation chains in the DFTs of images' p-maps. We calculate the variance of the fitting curve and examine the change of fingerprints under different operating intensities to ensure these two features can be broadly applied to operation detection. These features are fed to SVM, effectively discriminating among five combinations of upsampling and mean filtering. The representative experiments can verify the effectiveness of the proposed method.

Detectability of the Image Operation Order: Upsampling and Mean Filtering

This paper describes a quick and simple method to detect brush editing in JPEG images. The novelty of the
proposed method is based on detecting the discarded coefficients during the quantization of the image. Another
novelty of this paper is the development of a subjective metric named intentions. The method directly analyzes
the allegedly tampered image and generates a forgery mask indicating forgery evidence for each image block.
The experiments show that our method works especially well in detecting brush strokes, and it works reasonably
well with added captions and image splicing. However, the method is less effective detecting copy-moved and
blurred regions. This means that our method can effectively contribute to implementing a complete imagetampering detection tool. The editing operations for which our method is less effective can be complemented
with methods more adequate to detect them.

/pdf/detecting-image-brush-editing-using-the-discarded-4xy3r76dr6.pdf

Detecting Image Brush Editing Using the Discarded Coefficients and Intentions

Image deblurring, which is an important branch of digital image processing, is one of the difficulties in digital image processing. The main goal of image deblurring is to improve the quality of the given image, and to reconstruct or restore the original image using the relevant prior knowledge. Genetic algorithm (GA) is a global optimization search algorithm, which can quickly and effectively calculate complex multidimensional data area and indirectly. Now,genetic algorithm is slowly showing its excellent performance in the field of image processing. Therefore, based on the fuzzy features of the image, this paper uses the image enhancement algorithm to realize the image deblurring by capturing the gray value of the pixels in the image, and the improved genetic algorithm is used to better select the image threshold. The experimental results show that the new algorithm obtains higher definition than the traditional image deblurring method, and the improved genetic operator algorithm improves the degree of noise reduction.

https://iopscience.iop.org/article/10.1088/1742-6596/1852/2/022042/pdf

BenYong Liu

Papers

Feature Fusion for Blurring Detection in Image Forensics

Blind Image Deblurring Using Weighted Sum of Gaussian Kernels for Point Spread Function Estimation

Tensorial Kernel Based on Spatial Structure Information for Neuroimaging Classification

Spatial and Anatomical Regularization Based on Multiple Kernel Learning for Neuroimaging Classification