Anti-Forensics for Unsharp Masking Sharpening in Digital Images

2013 ◽  
Vol 5 (3) ◽  
pp. 53-65 ◽  
Author(s):  
Lu Laijie ◽  
Yang Gaobo ◽  
Xia Ming
Keyword(s):  
Performance Evaluation ◽  
Image Quality ◽  
Abrupt Change ◽  
Image Database ◽  
Image Editing ◽  
Detection Ability ◽  
Unsharp Masking ◽  
Passive Forensics ◽  
Adobe Photoshop ◽  
Image Edges

As a retouching tool, image sharpening can be applied as the final step to hide those possible forgery operations in an image. Unsharp masking (USM) is a popular sharpening method supported by most image editing software such as Adobe Photoshop. Several passive forensics methods have been presented for the detection of USM Sharpening. In this paper, an anti-forensic scheme for USM Sharpening is proposed to invalidate the existing forensic algorithms. It removes the overshoot artifacts in image edges and abrupt change in histogram ends. The effectiveness of the proposed method is proved by the experimental results on a large image database with various parameter settings. Comparisons are made among the unsharpened images, the sharpened images and the anti-forensic dithered image. Both the detection ability and image quality are used for its performance evaluation.

scholarly journals Robust Gaussian Noise Detection and Removal in Color Images using Modified Fuzzy Set Filter

2020 ◽  
Vol 30 (1) ◽  
pp. 240-257
Author(s):  
Akula Suneetha ◽  
E. Srinivasa Reddy
Keyword(s):  
Image Quality ◽  
Mean Square Error ◽  
Fuzzy Set ◽  
Digital Image ◽  
Gaussian Noise ◽  
Digital Images ◽  
Structural Similarity ◽  
Fuzzy Rules ◽  
Mean Square ◽  
Image Edges

Abstract In the data collection phase, the digital images are captured using sensors that often contaminated by noise (undesired random signal). In digital image processing task, enhancing the image quality and reducing the noise is a central process. Image denoising effectively preserves the image edges to a higher extend in the flat regions. Several adaptive filters (median filter, Gaussian filter, fuzzy filter, etc.) have been utilized to improve the smoothness of digital image, but these filters failed to preserve the image edges while removing noise. In this paper, a modified fuzzy set filter has been proposed to eliminate noise for restoring the digital image. Usually in fuzzy set filter, sixteen fuzzy rules are generated to find the noisy pixels in the digital image. In modified fuzzy set filter, a set of twenty-four fuzzy rules are generated with additional four pixel locations for determining the noisy pixels in the digital image. The additional eight fuzzy rules ease the process of finding the image pixels,whether it required averaging or not. In this scenario, the input digital images were collected from the underwater photography fish dataset. The efficiency of the modified fuzzy set filter was evaluated by varying degrees of Gaussian noise (0.01, 0.03, and 0.1 levels of Gaussian noise). For performance evaluation, Structural Similarity (SSIM), Mean Structural Similarity (MSSIM), Mean Square Error (MSE), Normalized Mean Square Error (NMSE), Universal Image Quality Index (UIQI), Peak Signal to Noise Ratio (PSNR), and Visual Information Fidelity (VIF) were used. The experimental results showed that the modified fuzzy set filter improved PSNR value up to 2-3 dB, MSSIM up to 0.12-0.03, and NMSE value up to 0.38-0.1 compared to the traditional filtering techniques.

scholarly journals Guide to image editing and production of figures for scientific publications with an emphasis on taxonomy Image editing for scientific publications

2020 ◽  
Vol 96 (1) ◽  
pp. 139-158
Author(s):  
Marcus Bevilaqua
Keyword(s):  
Image Editing ◽  
Scientific Publications ◽  
Digital Photo ◽  
Adobe Photoshop ◽  
Photo Editing ◽  
Publication Quality

Figures for scientific publications go through various stages from the planning, to the capturing of images, to the production of finished figures for publication. This guide is meant to familiarise the reader with the main image-editing software used by professional photographers. The guide’s focus is on digital photo editing and the production of figures using Adobe Photoshop to produce publication-quality figures for scientific publications. This guide will be of fundamental use for the academic public, especially taxonomists and others who work with images. Besides, it should be useful for anyone interested in becoming familiar with the basic tools of image editing.

Colourlab Image Database: Geometric Distortions

2021 ◽  
Vol 2021 (29) ◽  
pp. 258-263
Author(s):  
Marius Pedersen ◽  
Seyed Ali Amirshahi
Keyword(s):  
Image Quality ◽  
High Performance ◽  
Image Database ◽  
Quality Metrics ◽  
Current Image ◽  
Geometric Distortions ◽  
Image Quality Metrics ◽  
Objective Metrics ◽  
Reference Images

Over the years, a high number of different objective image quality metrics have been proposed. While some image quality metrics show a high correlation with subjective scores provided in different datasets, there still exists room for improvement. Different studies have pointed to evaluating the quality of images affected by geometrical distortions as a challenge for current image quality metrics. In this work, we introduce the Colourlab Image Database: Geometric Distortions (CID:GD) with 49 different reference images made specifically to evaluate image quality metrics. CID:GD is one of the first datasets which include three different types of geometrical distortions; seam carving, lens distortion, and image rotation. 35 state-ofthe-art image quality metrics are tested on this dataset, showing that apart from a handful of these objective metrics, most are not able to show a high performance. The dataset is available at <ext-link ext-link-type="url" xlink:href="http://www.colourlab.no/cid">www.colourlab.no/cid</ext-link>.

Color image database for evaluation of image quality metrics

2008 ◽  
Author(s):  
N. Ponomarenko ◽  
V. Lukin ◽  
K. Egiazarian ◽  
J. Astola ◽  
M. Carli ◽  
...  
Keyword(s):  
Image Quality ◽  
Color Image ◽  
Image Database ◽  
Quality Metrics ◽  
Image Quality Metrics

An oriented gradient based image quality metric for pedestrian detection performance evaluation

2015 ◽  
Vol 31 ◽  
pp. 61-75 ◽  
Author(s):  
Anthony Winterlich ◽  
Ciarán Hughes ◽  
Liam Kilmartin ◽  
Martin Glavin ◽  
Edward Jones
Keyword(s):  
Performance Evaluation ◽  
Image Quality ◽  
Pedestrian Detection ◽  
Detection Performance ◽  
Quality Metric ◽  
Gradient Based ◽  
Image Quality Metric
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