Image Sharpening Detection Based on Difference Sets

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.

CD-SDN: Unsupervised Sensitivity Disparity Networks for Hyper-Spectral Image Change Detection

“…To 'Santa Barbara', each scene image contains 728,160 pixels, including 52,134 changed pixels, 80,418 unchanged pixels, and 595,608 unknown pixels; while each scene image of 'Bay Area' has 300,000 pixels including 39,270 changed pixels, 34,211 unchanged pixels, and 226,519 unknown pixels; Unlike the other two, the full ground truth map of 'Hermiston' is known, with 9986 pixels marked with white and 68,014 pixels marked with black to represent the changed region and unchanged region, respectively. We implement the proposed CD-SDN, and evaluate the performance with five quantitative coefficients OA_CHG, OA_UN, OA [31], Kappa, and F1 [32], as defined in ( 13)- (17). We implement the proposed CD-SDN, and evaluate the performance with five quantitative coefficients OA_CHG, OA_UN, OA [31], Kappa, and F1 [32], as defined in ( 13)- (17).…”

“…Argument Region In the tests, we draw several findings of note as follows. First, it will lose significance if CD-USNet and CD-CSNet underperform in comprehensive coefficients (e.g., OA [31], Kappa, and F1 [32]); second, USNet and CSNet should have pretty remarkable sensitivity disparity to changed pixels/unchanged pixels, to produce the effective argument region; third, to generate the final detected BCM, we suggest to first transform the AM-CPM/AL-CPM into AM-BCM/AL-BCM by using K-means, as shown in Figure 5b, then merge the In AM approach, we apply arithmetic mean operation to the change probability map of argument (CPMA) related to unchanged sensitivity network and changed sensitivity network, respectively, denoted as USNet-CPMA and CSNet-CPMA, as (c1) shows; to reestimate the change probability of BAM, the obtained AM-CPM is shown as (d1). Then, K-means is used to generate the AM-BCM, displayed in (e1).…”

“…In the tests, we draw several findings of note as follows. First, it will lose significance if CD-USNet and CD-CSNet underperform in comprehensive coefficients (e.g., OA [31], Kappa, and F1 [32]); second, USNet and CSNet should have pretty remarkable sensitivity disparity to changed pixels/unchanged pixels, to produce the effective argument region; third, to generate the final detected BCM, we suggest to first transform the AM-CPM/AL-CPM into AM-BCM/AL-BCM by using K-means, as shown in Figure 5b, then merge the AM-BCM/AL-BCM with HAM shown as Figure 5c, to obtain the final detected BCM, as shown in Figure 5d; the discouraged subprocess start to obtain the combination CPM by merging the AM-CPM/AL-CPM with USNet-CPM (or CSNet-CPM), then K-means is used to generate the final detected BCM. In this circumstance, the threshold may occasionally fail to segment the AM-CPM/AL-CPM as part of the combination CPM, regarding the influence of non-argument pixels, as shown in Figure 5a.…”

Design and evaluation of ultra‐fast 8‐bit approximate multipliers using novel multicolumn inexact compressors

“…[13][14][15][16][17][18][19][20][21][32][33][34][35][36][37] Afterward, the proposed multipliers are utilized in the image sharpening application, which is one of the fundamental operations to improve visual effects and highlight fine details in an image. 38 The rest of the paper is organized as follows: The new multicolumn 3,3:2 inexact compressor is introduced in Section 2. Then, the proposed approximate multipliers through systematic analysis of performance and assessment of error are suggested in Section 3.…”

“…To the best of our knowledge, none of the previous approximate multipliers can accumulate partial products in such a few stages 13–21,32–37 . Afterward, the proposed multipliers are utilized in the image sharpening application, which is one of the fundamental operations to improve visual effects and highlight fine details in an image 38 …”