CoMoFoD #x2014; New database for copy-move forgery detection

Due to the availability of many sophisticated image processing tools, a digital image forgery is nowadays very often used. One of the common forgery method is a copy-move forgery, where part of an image is copied to another location in the same image with the aim of hiding or adding some image content. Numerous algorithms have been proposed for a copy-move forgery detection (CMFD), but there exist only few benchmarking databases for algorithms evaluation. We developed new database for a CMFD that consist of 260 forged image sets. Every image set includes forged image, two masks and original image. Images are grouped in 5 categories according to applied manipulation: translation, rotation, scaling, combination and distortion. Also, postprocessing methods, such as JPEG compression, blurring, noise adding, color reduction etc., are applied at all forged and original images. In this paper we present database organization and content, creation of forged images, postprocessing methods, and database testing. CoMoFoD database is available at http://www.vcl.fer.hr/comofodMinistry of Science, Education and Sport, China; project numbers: 036-0361630-1635 and 036-0361630-164

Robust Image Encryption Based on Balanced Cellular Automaton and Pixel Separation

The purpose of image encryption is to protect content from unauthorized access. Image encryption is usually done by pixel scrambling and confusion, so process is possible to reverse only by knowing secret information. In this paper we introduce a new method for digital image encryption, based on a 2D cellular automaton and pixel separation. Novelty in the proposed method lies in the application of the balanced 2D cellular automata with extended Moore neighborhood separately on each level of pseudorandom key-image. This process extends key space several times when compared to the previous methods. Furthermore, pixel separation is introduced to define operation for each pixel of the source image. Thanks to pixel separation, decryption process is more difficult to conduct without knowing secret information. Moreover, encryption is robust against different statistical attacks and analysis, does not affect image quality and can cope with loss of encrypted image content

Evaluation of Blur and Gaussian Noise Degradation in Images Using Statistical Model of Natural Scene and Perceptual Image Quality Measure

In this paper we present new method for classification of image degradation type based on Riesz transform coefficients and Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) that employs spatial coefficients. In our method we use additional statistical parameters that gives us statistically better results for blur and all tested degradations together in comparison with previous method. A new method to determine level of blur and Gaussian noise degradation in images using statistical model of natural scene is presented. We defined parameters for evaluation of level of Gaussian noise and blur degradation in images. In real world applications reference image is usually not available therefore proposed method enables classification of image degradation by type and estimation of Gaussian noise and blur levels for any degraded image

Targeted Proximal Tubule Injury Triggers Interstitial Fibrosis and Glomerulosclerosis

Chronic kidney disease (CKD) remains one of the leading causes of death in the developed world and acute kidney injury (AKI) is now recognized as a major risk factor in its development. Understanding the factors leading to CKD after acute injury are limited by current animal models of AKI which concurrently target various kidney cell types such as epithelial, endothelial and inflammatory cells. Here we developed a mouse model of kidney injury using the Six2-Cre-LoxP technology to selectively activate expression of the simian diphtheria toxin receptor in renal epithelia derived from the metanephric mesenchyme. By adjusting the timing and dose of diphtheria toxin a highly selective model of tubular injury was created to define the acute and chronic consequences of isolated epithelial injury. The diphtheria toxin-induced sublethal tubular epithelial injury was confined to the S1 and S2 segments of the proximal tubule rather than being widespread in the metanephric mesenchyme derived epithelial lineage. Acute injury was promptly followed by inflammatory cell infiltration and robust tubular cell proliferation leading to complete recovery after a single toxin insult. In striking contrast, three insults to renal epithelial cells at one week intervals resulted in maladaptive repair with interstitial capillary loss, fibrosis and glomerulosclerosis which was highly correlated with the degree of interstitial fibrosis. Thus, selective epithelial injury can drive the formation of interstitial fibrosis, capillary rarefaction and potentially glomerulosclerosis, substantiating a direct role for damaged tubule epithelium in the pathogenesis of CKD

Fairness in Algorithmic Decision Making: An Excursion Through the Lens of Causality

As virtually all aspects of our lives are increasingly impacted by algorithmic decision making systems, it is incumbent upon us as a society to ensure such systems do not become instruments of unfair discrimination on the basis of gender, race, ethnicity, religion, etc. We consider the problem of determining whether the decisions made by such systems are discriminatory, through the lens of causal models. We introduce two definitions of group fairness grounded in causality: fair on average causal effect (FACE), and fair on average causal effect on the treated (FACT). We use the Rubin-Neyman potential outcomes framework for the analysis of cause-effect relationships to robustly estimate FACE and FACT. We demonstrate the effectiveness of our proposed approach on synthetic data. Our analyses of two real-world data sets, the Adult income data set from the UCI repository (with gender as the protected attribute), and the NYC Stop and Frisk data set (with race as the protected attribute), show that the evidence of discrimination obtained by FACE and FACT, or lack thereof, is often in agreement with the findings from other studies. We further show that FACT, being somewhat more nuanced compared to FACE, can yield findings of discrimination that differ from those obtained using FACE.Comment: 7 pages, 2 figures, 2 tables.To appear in Proceedings of the International Conference on World Wide Web (WWW), 201

Ancestry and dental development: A geographic and genetic perspective

Objective: In this study, we investigated the influence of ancestry on dental development in the Generation R Study. Methods: Information on geographic ancestry was available in 3,600 children (1,810 boys and 1,790 girls, mean age 9.81±0.35 years) and information about genetic ancestry was available in 2,786 children (1,387 boys and 1,399 girls, mean age 9.82±0.34 years). Dental development was assessed in all children using the Demirjian method. The associations of geographic ancestry (Cape Verdean, Moroccan, Turkish, Dutch Antillean, Surinamese Creole and Surinamese Hindustani vs Dutch as the reference group) and genetic content of ancestry (European, African or Asian) with dental development was analyzed using linear regression models. Results: In a geographic perspective of ancestry, Moroccan (β=0.18; 95% CI: 0.07, 0.28), Turkish (β=0.22; 95% CI: 0.12, 0.32), Dutch Antillean (β=0.27; 95% CI: 0.12, 0.41), and Surinamese Creole (β=0.16; 95% CI: 0.03, 0.30) preceded Dutch children in dental development. Moreover, in a genetic perspective of ancestry, a higher proportion of European ancestry was associated with decelerated dental development (β=-0.32; 95% CI: -.44, -.20). In contrast, a higher proportion of African ancestry (β=0.29; 95% CI: 0.16, 0.43) and a higher proportion of Asian ancestry (β=0.28; 95% CI: 0.09, 0.48) were associated with accelerated dental development. When investigating only European children, these effect estimates increased to twice as large in absolute value. Conclusion: Based on a geographic and genetic perspective, differences in dental development exist in a population of heterogeneous ancestry and should be considered when describing the physiological growth in children

Assessing anesthetic activity through modulation of the membrane dipole potential

There is great individual variation in response to general anaesthetics leading to difficulties in optimal dosing and sometimes even accidental awareness during general anaesthesia (AAGA). AAGA is a rare but potentially devastating complication affecting between 0.1% and 2% of patients undergoing surgery. The development of novel, personalised screening techniques to accurately predict a patient's response to GA and the risk of AAGA remains an unmet clinical need. In the present study we demonstrate the principle of using a fluorescent reporter of the membrane dipole potential, di-8-ANEPPs, as a novel method to monitor anaesthetic activity using a well-described inducer/non-inducer pair. The membrane dipole potential has previously been suggested to contribute a novel mechanism of anaesthetic action (Qin et al 1995). We show the fluorescence ratio of di-8-ANEPPs changed in response to physiological concentrations of the anaesthetic 1-chloro-1,2,2-trifluorocyclobutane (F3) but not the structurally similar non-inducer 1,2-dichlorohexafluorocyclobutane (F6) to artificial membranes and in vitro retinal cell systems. Modulation of the membrane dipole provides an explanation to overcome limitations associated with alternative membrane-mediated mechanisms of GA action. Furthermore, by combining this technique with non-invasive retinal imaging technologies, we propose this technique could provide a novel and non-invasive technique to monitor GA susceptibility and identify patients at risk of AAGA

LiFT: A Scalable Framework for Measuring Fairness in ML Applications

Many internet applications are powered by machine learned models, which are usually trained on labeled datasets obtained through either implicit / explicit user feedback signals or human judgments. Since societal biases may be present in the generation of such datasets, it is possible for the trained models to be biased, thereby resulting in potential discrimination and harms for disadvantaged groups. Motivated by the need for understanding and addressing algorithmic bias in web-scale ML systems and the limitations of existing fairness toolkits, we present the LinkedIn Fairness Toolkit (LiFT), a framework for scalable computation of fairness metrics as part of large ML systems. We highlight the key requirements in deployed settings, and present the design of our fairness measurement system. We discuss the challenges encountered in incorporating fairness tools in practice and the lessons learned during deployment at LinkedIn. Finally, we provide open problems based on practical experience.Comment: Accepted for publication in CIKM 202