Locating splicing forgery by adaptive-SVD noise estimation and vicinity noise descriptor

One of the common image forgery techniques is the splicing, where parts from different images are copied and pasted onto a single image. This paper proposes a new forensics method for detecting splicing forgeries in images containing human faces. Our approach is based on extracting an illumination-signature from the faces of people present in an image using the dichromatic reflection model (DRM). The dichromatic plane histogram (DPH), which is calculated by applying the 2D Hough Transform on the face images, is used as the illumination-signature. The correlation measure is employed to compute the similarity between the DPHs obtained from different faces present in an image. Finally, a simple threshold on this similarity measure exposes splicing forgeries in the image. Experimental results show the efficacy of the proposed method.

Copy-move forgery is one of the most commonly used forgeries in digital images, in which some regions of a digital image is coped and pasted to another part of the image with the aim of concealing certain features or objects in the original images. In this paper, a novel copy-move forgery detection scheme in digital images is proposed. It treats the digital images as high dimensional data and uses the intrinsic dimension estimation method first to segment the image and then identify the copy-move forgeries in the image regions with the same texture. Experimental results show that the proposed method is efficient and robust to retouching and many other operations, such as lossy compression, blurring, filtering, etc.

The copy-move forgery is a common way to manipulate the digital images, which copied regions come from the same image. The detection process of altered regions is known as forgery detection. In this paper, we propose a new block-based approach for detecting copy-move forgery in digital images. The novelty of the proposed approach is the usage of structural similarity index as the method for similarity matching step. Additionally, Gaussian pyramid decomposition method allows us to improve run-time speed of the approach considerably. Therefore, feature extraction would not require, hence; it can help to diminish time-consuming issue. Accordingly, simplicity in calculating and analyzing is the significant specification of the proposed approach. Experiments are provided to illustrate an efficiency of suggested approach and evaluate its robustness and sensitivity against some post-processing operations.

Digital forgery is increasing day by day due to the increase in the usage of digital media files and the ease of editing media files. But digital media editing is a serious issue, as digital media are the primary source of evidence for criminal cases in the courtrooms. Consequently, there is an urge to detect and localize forgeries in digital media to aid digital forensics. The main aim of the proposed work is to detect forgeries in digital images. Image splicing forgery involves copying an image region from one image and pasting it into another image. A deep learning-based technique is proposed to detect image splicing forgery. A pre-trained deep Convolution Neural Network is transferred for the proposed application. The network is trained using spliced and original images to adapt it to the image splicing detection problem. The layers in the network are modified and fine-tuned to make it perform well for the new unseen dataset. This re-designed convolution network discriminates spliced and original images accurately. Also, the proposed work locates the spliced image regions if both the source and spliced images are provided as input image pairs. The proposed work is tested on CASIA and Columbia splicing image datasets and achieved good results.

Digital image forgery and its forensics have emerged as a significant research domain. Digital forensics is required to examine the questioned images and classify them as authentic or tampered. This paper aims at image tamper detection using a novel energy deviation measure (EDM). The EDM is a measure of deviation in pixel intensity with respect to its immediate and distant neighbourhood. It is extracted by measuring the inter pixel intensity difference across and inside the DCT block boundary of a JPEG image. Features from EDM have been used for the classification of the authentic and tampered images. Support vector machine is used for image classification. The experimental results have shown that the proposed method performs better with fewer dimensions as compared to other state of the art methods. It gives improved accuracy and area under curve while classifying images. It is robust to noise and JPEG image compression quality factor.

In today's modern life, digital images have significant importance because they have become a leading source of information dissemination. However, the availability of image editing tools made it easier to forge the contents of a digital image; making the authenticity untrustful. Different techniques can be used to forge the digital images. Copy move forgery is the most popular and common approach where a specific part of an image is copied and pasted elsewhere in the same image to conceal unwanted part or object. In this study, we attempted to survey several passive block based copy move forgery detection techniques. A passive technique attempts to identify forgery in digital images without any prior information. A comparison between various techniques is also included.

Copy-move forgery is a type of image tampering that involves copying a portion of an image and pasting it to another part of the same image with the intention of deceiving the viewer. In recent years, many approaches have been proposed to detect copy-move forgery, including those based on local binary patterns (LBP). In this paper, we perform a comprehensive evaluation of LBP-based methods for copy-move forgery detection using a dataset of 50 digital images. We compare the performance of four LBP-based methods, namely LBP, SIFT and SURF using metrics such as accuracy, precision, recall, and F1-score. Our results show that LBP outperforms the other methods in terms of accuracy and F1-score, while SIFT has the highest precision and recall. We also investigate the effect of various parameters, such as patch size and threshold values, on the performance of LBP. Our study provides valuable insights into the strengths and weaknesses of LBP-based methods for copy-move forgery detection, which can guide future research in this area. This study evaluates the performance of Local Binary Patterns (LBP) for detecting copy-move forgery in digital images. LBP is a widely used feature extraction technique in image processing and has been applied to various computer vision tasks, including forgery detection. The comparative study involves analyzing the accuracy, precision, recall, and F1-score of LBP and other popular forgery detection techniques, including SIFT and SURF, using a dataset of 50 digital images. The results show that LBP performs better than the other techniques, achieving an accuracy of 96.6%, precision of 94.0%, recall of 100%, and F1-score of 96.9%. This study provides useful insights for researchers and practitioners in the field of forgery detection, particularly for those interested in using LBP as a feature extraction technique.

A robust detection algorithm for copy-move forgery in digital images

On Image Forgery Detection Using Two Dimensional Discrete Cosine Transform and Statistical Moments

Block-based copy-move forgery detection (CMFD) methods work with features from overlapping blocks. As overlapping blocks are involved, thresholds related to similarity and the physical distances are defined to identify the duplicated regions. However, these thresholds are controlled manually in localising the forged regions. In order to overcome this, an intelligent block matching approach for localisation is proposed using colour and texture features (CTF) through firefly algorithm. Investigation of the proposed CTF method is carried out on a standard database, which achieved an average true detection rate of 0.98 and an average false detection rate of 0.07. The proposed CTF method is robust against brightness change, colour reduction, blurring, contrast adjustment attacks, and additive white Gaussian noise. Performance analysis of the CTF method validates its superiority over other existing methods.

With the advent of digital technology, digital image has gradually taken the place of the original analog photograph, and the forgery of digital image has become more and more easy and indiscoverable. Image splicing is a commonly used technique in image tampering. To implement image splicing detection a blind, passive and effective splicing detection scheme was proposed in this paper. Image splicing detection can be treated as a two-class pattern recognition problem, the model was based on moment features and some image quality metrics (IQMs) extracted from the given test image, which are sensitive to spliced image. Artificial neural network (ANN) is chosen as a classifier to train and test the given images. This model can measure statistical differences between original image and spliced image. Experimental results demonstrate that this new splicing detection algorithm is effective and reliable; indicating that the proposed approach has a broad application prospect.

We present a new approach to detection of forgeries in digital images under the assumption that either the camera that took the image is available or other images taken by that camera are available. Our method is based on detecting the presence of the camera pattern noise, which is a unique stochastic characteristic of imaging sensors, in individual regions in the image. The forged region is determined as the one that lacks the pattern noise. The presence of the noise is established using correlation as in detection of spread spectrum watermarks. We proposed two approaches. In the first one, the user selects an area for integrity verification. The second method attempts to automatically determine the forged area without assuming any a priori knowledge. The methods are tested both on examples of real forgeries and on non-forged images. We also investigate how further image processing applied to the forged image, such as lossy compression or filtering, influences our ability to verify image integrity.

Integrated phenology and climate in rice yields prediction using machine learning methods

Artificial intelligence has been applied in wildfire science and management since the 1990s, with early applications including neural networks and expert systems. Since then, the field has rapidly progressed congruently with the wide adoption of machine learning (ML) methods in the environmental sciences. Here, we present a scoping review of ML applications in wildfire science and management. Our overall objective is to improve awareness of ML methods among wildfire researchers and managers, as well as illustrate the diverse and challenging range of problems in wildfire science available to ML data scientists. To that end, we first present an overview of popular ML approaches used in wildfire science to date and then review the use of ML in wildfire science as broadly categorized into six problem domains, including (i) fuels characterization, fire detection, and mapping; (ii) fire weather and climate change; (iii) fire occurrence, susceptibility, and risk; (iv) fire behavior prediction; (v) fire effects; and (vi) fire management. Furthermore, we discuss the advantages and limitations of various ML approaches relating to data size, computational requirements, generalizability, and interpretability, as well as identify opportunities for future advances in the science and management of wildfires within a data science context. In total, to the end of 2019, we identified 300 relevant publications in which the most frequently used ML methods across problem domains included random forests, MaxEnt, artificial neural networks, decision trees, support vector machines, and genetic algorithms. As such, there exists opportunities to apply more current ML methods — including deep learning and agent-based learning — in the wildfire sciences, especially in instances involving very large multivariate datasets. We must recognize, however, that despite the ability of ML models to learn on their own, expertise in wildfire science is necessary to ensure realistic modelling of fire processes across multiple scales, while the complexity of some ML methods such as deep learning requires a dedicated and sophisticated knowledge of their application. Finally, we stress that the wildfire research and management communities play an active role in providing relevant, high-quality, and freely available wildfire data for use by practitioners of ML methods.

Biomarkers are known to be the key driver behind targeted cancer therapies by either stratifying the patients into risk categories or identifying patient subgroups most likely to benefit. However, the ability of a biomarker to stratify patients relies heavily on the type of clinical endpoint data being collected. Of particular interest is the scenario when the biomarker involved is a continuous one where the challenge is often to identify cut-offs or thresholds that would stratify the population according to the level of clinical outcome or treatment benefit. On the other hand, there are well-established Machine Learning (ML) methods such as the Support Vector Machines (SVM) that classify data, both linear as well as non-linear, into subgroups in an optimal way. SVMs have proven to be immensely useful in data-centric engineering and recently researchers have also sought its applications in healthcare. Despite their wide applicability, SVMs are not yet in the mainstream of toolkits to be utilised in observational clinical studies or in clinical trials. This research investigates the very role of SVMs in stratifying the patient population based on a continuous biomarker across a variety of datasets. Based on the mathematical framework underlying SVMs, we formulate and fit algorithms in the context of biomarker stratified cancer datasets to evaluate their merits. The analysis reveals their superior performance for certain data-types when compared to other ML methods suggesting that SVMs may have the potential to provide a robust yet simplistic solution to stratify real cancer patients based on continuous biomarkers, and hence accelerate the identification of subgroups for improved clinical outcomes or guide targeted cancer therapies.