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Recent works have demonstrated the great performance of forgery image forensics based on deep learning, but there is still a risk that detectors could be susceptible to unknown illegal attacks, raising growing security concerns. This paper starts from the perspective of reverse forensics and explores the vulnerabilities of current image manipulation detectors to achieve targeted attacks. We present a novel reverse decision aggregate gradient attack under low-frequency constraints (RevAggAL). Specifically, we first propose a novel pixel reverse content decision-making (PRevCDm) loss to optimize perturbation generation with a specific principle more suitable for segmenting manipulated regions. Then, we introduce the low-frequency component to constrain the perturbation into more imperceptible details, significantly avoiding the degradation of image quality. We also consider aggregating gradients on model-agnostic features to enhance the transferability of adversarial examples in black-box scenarios. We evaluate the effectiveness of our method on three representative detectors (ResFCN, MVSSNet, and OSN) with five widely used forgery datasets (COVERAGE, COLUMBIA, CASIA1, NIST 2016, and Realistic Tampering). Experimental results show that our method improves the attack success rate (ASR) while ensuring better image quality. [ABSTRACT FROM AUTHOR]

Recently, with the emergence of many image editing tools (photoshop, Topaz studio, etc.), the authenticity of images has been severely challenged. However, the performance of some existing traditional feature extraction methods and detection methods based on convolutional neural network (CNN) is poor, and the information provided by the features extracted from the network is limited and single. In this paper, an end-to-end ringed residual U-Net is proposed to detect image splicing forgery by blending features of non-natural regions. Some regions with significant differences from the image background are defined as non-natural regions(such as the irregular border at the splicing of images). In this paper, a feature enhancement module for non-natural regions is constructed, which the image through the pooling of four different scales, and these features are then combined with the original image and input to the backbone network for processing, aiming to highlight regions of the image that differ significantly from the background. Therefore, after adding the feature enhancement module for non-natural regions to the end-to-end ring residual U-Net, more attention will be paid to the tampering regions in the feature extraction stage, image manipulation detection and localization will also become more accurate. Compared with some mainstream methods, this method achieves better performance on the three standard datasets(CASIA2.0, NIST2016, COLUMBIA). In addition, it has excellent robustness under JPEG compression attack and noise corruption attack. [ABSTRACT FROM AUTHOR]

Manipulating digital images is not often a difficult task due to the rapid development of software and image manipulation techniques. Hence, there is no need for professional skills or training. When used as an artistic tool, it is completely harmless, but when these images can be presented in judicial system as evidence or for the creation of political associations, as well as, using them in legal documents, electronic money circulation or press; in these cases, the distinction between an original image and a forgery image is very important. In order to solve the problem in this research using a Discrete Wavelet Transform (DWT), which is performed by decomposing a signal into smaller and smaller details, as well as, the use of periodic patterns in the histogram generated by double compression with different coefficients, significant improvements were made in terms of reducing computations and increasing the detection rate of forging areas. Many of the proposed methods for detecting image forgery use a feature extraction model from a valid and manipulated dataset and then classify them using machine learning with the aim of optimizing the accuracy. In this research, following the extraction of features in the proposed method, using the SVM classification identifies image forgery and then identifies the forging area after it detects the falsification or originality of the image. The results of this study indicate 97.98% accuracy in the Columbia database and 98.1% in the IFS-TC database. [ABSTRACT FROM AUTHOR]

With the rapid development of image processing technology, it has become increasingly easy to manipulate images, which poses a threat to the stability and security of people's lives. Recent methods have proposed the fusion of RGB and noise features to uncover tampering traces. However, these approaches overlook the characteristics of features at different levels, leading to insufficient feature fusion. To address this problem, this paper proposes a double-stream multilevel feature fusion network (DMFF-Net). Unlike the traditional feature fusion approach, DMFF-Net adopts a graded feature fusion strategy. It classifies features into primary, intermediate, and advanced levels and introduces the Primary Feature Fusion Module (PFFM) and the Advanced Feature Fusion Module (AFFM) to achieve superior fusion results. Additionally, a multisupervision strategy is employed to decode the fused features into level-specific masks, including boundary, regular, and refined masks. The DMFF-Net is validated on publicly available datasets, including CASIA, Columbia, COVERAGE, and NIST16, as well as a real-life manipulated image dataset, IMD20, and achieves AUCs of 84.7%, 99.6%, 86.6%, 87.4% and 82.8%, respectively. Extensive experiments show that our DMFF-Net outperforms state-of-the-art methods in terms of image manipulation localization accuracy and exhibits improved robustness. [Display omitted] • RGB features are fused with other modal features in a way to better explore the traces of tampering. • Failure to consider the characteristics of different levels of features may lead to inadequate feature fusion. • The features are classified into three levels: primary, intermediate and advanced. • Based on the features at different levels, two feature fusion modules are proposed to achieve better feature fusion. • The multi-supervision strategy decodes the fused features into corresponding masks by level to obtain boundary masks, regular masks, and refined masks. [ABSTRACT FROM AUTHOR]

Powerful image editing software makes the process of image manipulation easy, which increases security risks. Therefore, it is urgent to locate the tampered region to uncover the processing history. However, previous research has mainly focused on feature extraction, with little discussion on classifiers for classifying original and tampered regions. We improve the splicing forgery localization method from a statistical perspective. The refined color filter array feature provides sufficient data for statistical analysis, and the geometric mean is used to eliminate anomalous data. Subsequently, a classifier that combines the expectation–maximization algorithm and Bayesian theory is proposed to binarize the original and tampered regions. The two steps of feature extraction and feature classification are associated from a statistical perspective, which ultimately improve the performance of the method effectively. Extensive experimental results demonstrate that the refined feature used for classification has several advantages, and the proposed classifier is appropriate for handling complex image manipulation across different statistical distributions. The proposed method outperforms the reference methods in both the Columbia and Korus datasets. [ABSTRACT FROM AUTHOR]

With the technology progress, a plethora of freely accessible software has questioned the authenticity of digital images. This field is continuously creating challenges for researchers to ascertain the integrity of images. Hence, there is a need to improve the performance of forgery detection algorithms from time to time. This paper is focused on the detection of splicing forgery because it is one of the most frequently used image manipulation techniques. In the proposed scheme, Markov features in both Discrete Wavelet Transform (DWT) and Local Binary Pattern (LBP) domains are extracted and combined for the detection of image splicing. Three-level DWT is applied to the source image by the means of discrete Haar wavelet. The image is split in to high and low-frequency sub-bands after applying one level DWT. Furthermore, low-frequency sub-band is decomposed twice to obtain three-level DWT, which leads to more information and less amount of noise. The efficacy of the proposed scheme has been appraised on six benchmark datasets i.e. CASIA v2.0, DVMM, IFS-TC, CASIA v1.0, Columbia, and DSO-1. Moreover, the SVM classifier is trained to classify the images as tampered or authentic. The effectiveness of the proposed scheme is evaluated based on various performance parameters such as accuracy, sensitivity, specificity, and informedness. The proposed results show improved accuracy i.e. 99.69%, 99.76%, 97.80%, 98.61%, 96.90%, and 92.50% on CASIA v1.0, CASIA v2.0, DVMM, Columbia, IFS-TC, and DSO-1, respectively, in comparison to other existing approaches. [ABSTRACT FROM AUTHOR]

The fake images and visuals can easily spread among social media users and they largely impact decisionmaking in society. Image forgery has become increasingly common as more non-professionals have access to image manipulation tools. These fake images are so sneaky that an ordinary person cannot guess them. Through social media, such photos are utilized to promote erroneous information in society. Image forgery detection is about segmenting the forged part from the images, primarily a region of interest. This paper suggests a unique method that depends on a dual attention network to detect forged segments. This network contains self-attention modules that contribute to extracting and matching features in the channel and spatial domains. These features help locate and identify the forged portions of digital images at various scales and channels. This experimental study uses typical datasets such as CASIA V1.0, CASIA V2.0, and Columbia. Proposed IFLNet technique outperforms other advanced techniques with a precision of 96 %, recall rate of 95 %, accuracy rate of 98 %, F1-score of 96 % and IoU score of 92 % for Columbia dataset and correspondingly other two datasets also. [ABSTRACT FROM AUTHOR]

The color filter array of the camera is an effective fingerprint for digital forensics. Most previous color filter array (CFA)-based forgery localization methods perform under the assumption that the interpolation algorithm is linear. However, interpolation algorithms commonly used in digital cameras are nonlinear, and their coefficients vary with content to enhance edge information. To avoid the impact of this impractical assumption, a CFA-based forgery localization method independent of linear assumption is proposed. The probability of an interpolated pixel value falling within the range of its neighboring acquired pixel values is computed. This probability serves as a means of discerning the presence and absence of CFA artifacts, as well as distinguishing between various interpolation techniques. Subsequently, curvature is employed in the analysis to select suitable features for generating the tampering probability map. Experimental results on the Columbia and Korus datasets indicate that the proposed method outperforms the state-of-the-art methods and is also more robust to various attacks, such as noise addition, Gaussian filtering, and JPEG compression with a quality factor of 90. [ABSTRACT FROM AUTHOR]

With the increasing technology, digital images have become a widely used data type in crucial areas such as medical journalism and law. Since it is used in such important areas, it has become questionable whether digital images are original or not. Image splicing forgery is one of the most common forgery types applied to digital images. This work proposes a new image splicing detection and localization method. Our motivation is to reveal the boundaries of forgery by using statistical features of the image blocks. The proposed method has two main stages: training and localizing. In both phases, image blocks that contain edge information are used because the splicing operation causes some inconsistency on the edges. In the training stage, original blocks are selected from the regions that include original boundaries, and forged blocks are selected from the areas that contain splicing operation-induced edges. Transition probability matrices are calculated in eight directions to obtain the correlation of the borders between the neighbor blocks on original and splicing edges. These matrices are used as a feature for each block. The blocks are classified as authentic and spliced using SVM. A new post-processing step has been proposed to eliminate the false positives that may occur due to the presence of original regions that are likely to be detected as spliced edges in the image. The publicly available Columbia dataset has been used to show the effectiveness of the state-of-the-art and proposed method. The results indicate that the proposed method has performed well even under JPEG compression and Gaussian blurring attacks. [ABSTRACT FROM AUTHOR]

The detection and location of image splicing forgery are a challenging task in the field of image forensics. It is to study whether an image contains a suspicious tampered area pasted from another image. In this paper, we propose a new image tamper location method based on dual-channel U-Net, that is, DCU-Net. The detection framework based on DCU-Net is mainly divided into three parts: encoder, feature fusion, and decoder. Firstly, high-pass filters are used to extract the residual of the tampered image and generate the residual image, which contains the edge information of the tampered area. Secondly, a dual-channel encoding network model is constructed. The input of the model is the original tampered image and the tampered residual image. Then, the deep features extracted from the dual-channel encoding network are fused for the first time, and then the tampered features with different granularity are extracted by dilation convolution, and then, the secondary fusion is carried out. Finally, the fused feature map is input into the decoder, and the predicted image is decoded layer by layer. The experimental results on Casia2.0 and Columbia datasets show that DCU-Net performs better than the latest algorithm and can accurately locate tampered areas. In addition, the attack experiments show that DCU-Net model has good robustness and can resist noise and JPEG recompression attacks. [ABSTRACT FROM AUTHOR]

Copyright of Revista Facultad de Ingeniería Universidad de Antioquia is the property of Universidad de Antioquia and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Freshwater fossil fish faunas have long been used to infer past drainage connections, given that they are bounded by physical freshwater barriers. Here we study a middle Miocene (15.0–15.5 Ma) freshwater fish fossil fauna (Makaraipao) from the Castilletes Formation in northern Colombia, nowadays west of the Andes. We record the presence of lungfishes (Lepidosiren), pacus (Mylossoma and Piaractus), armoured catfishes (Callichthyidae), and red‐tail catfishes (Phractocephalus). Extant members of all those groups (except the Callichthyidae, due to lack of taxonomic resolution) are found in Amazonian faunas east of the Andes and are absent from faunas west of the Andes, indicating that the riverine systems of the Guajira Peninsula were connected to Amazonia during the middle Miocene. The similarity of La Venta (west of the Andes) and Rio Acre (east of the Andes) fish faunas during the late Miocene further indicates that the northern Andean uplift was not a complete barrier at least until c. 11 Ma. However, there is a continental‐wide structuring of the Miocene fish faunas that is also found in the extant faunas, suggesting that other factors such as ecological conditions, in addition to the uplift of the Andes, have shaped the biogeographic evolution of South American fish faunas. [ABSTRACT FROM AUTHOR]

Today, it has become very easy to manipulate digital images using image processing tools and software such as Adobe Photoshop (https://www.adobe.com/products/photoshop.html). Tampering with images by splicing is an operation that consists of cutting-and-pasting an area of an image into another host image. In this paper, we propose to detect and localize such manipulations by analyzing the correlation of the image noise across the three color channels RGB, which is an intrinsic feature of the digital photography acquisition process. More precisely, we propose to detect the border between the background (host image) and the spliced area. Using a sliding window, we detect the blocks that span across the two areas which are characterized by two different color noise correlations. To do this, we propose specific features that are able to highlight these blocks. After the feature extraction, we introduce a learning phase using a Random Forest Classifier. Experimental results, specifically on the Columbia database, show very good results in comparison to other current state of the art methods. [ABSTRACT FROM AUTHOR]

Digital passive image forgery methods are extensively used to verify the authenticity and integrity of images. Splicing and copy-move are the most common types of passive digital image forgeries. Several approaches have been proposed to detect these forgeries separately, but very few approaches are available that can detect them simultaneously. However, a more efficient method is still in demand to meet the day-to-day challenges to detect these forgeries at the same time. So, a passive hybrid approach based on discrete fractional cosine transform (DFrCT) and local binary pattern (LBP) is proposed to detect copy-move and splicing forgeries simultaneously. The extra parameter i.e. fractional parameter of DFrCT is utilized to enhance the accuracy and LBP is used to highlight the tampering artifacts effectively. Then, a support vector machine (SVM) is employed to categorize the images into authentic, copy-move, and spliced images. Next, localization is performed on both the copy-move and spliced images to localize the duplicated areas in the image. Experiments on six benchmark datasets, namely, CASIA v1.0, GRIP, CASIA v2.0, IMD, COVERAGE, and Columbia, attain accuracy rates of 99.67%, 99.23%, 99.76%, 98.81%, 95%, and 98.17%, respectively. To validate the effectiveness of the proposed method, comparative analysis has been performed with existing methods in terms of ROC, precision, recall, F1 score, F2 score, and accuracy. Moreover, the robustness of the proposed work is tested under rotation attack and better results are attained in comparison to the existing techniques. [ABSTRACT FROM AUTHOR]

Two new species of short-tailed whip-scorpions (schizomids),Calima emberasp. nov. andCalima nutabesp. nov. are described based upon collected material from the Natural Park Planes de San Rafael, Risaralda department, and the Natural Reserve El Romeral, Antioquia department, Colombia, increasing the number of known species for the genus to four; an identification key and a distribution map forCalimaspecies, are provided. The voluminous spermathecal lobes in females and the presence of a small dorsal eminence basal toDm4in the male flagellum of some species, could represent diagnostic characters forCalimaand are therefore included in its diagnosis. The microsetae of the male flagellum of Hubbardiinae, continues be excluded from the setal homologies; we suggest that they must be taken into consideration in a future homology proposal.www.zoobank.org/urn:lsid:zoobank.org:pub:1D3A97A4-29A6-46E2-AD78-EE6C7036D251 [ABSTRACT FROM AUTHOR]

Copyright of Profile: Issues in Teachers' Professional Development is the property of Universidad Nacional de Colombia, Facultad de Ciencias Humanas and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Governments are increasingly committing to significant ecological restoration. However, the impacts of forest restoration on local hydrological services are surprisingly poorly understood. Particularly, limited information is available about the impacts of tree planting on soil infiltration processes and runoff pathways. Thus, we investigated the saturated hydraulic conductivity (Ks) and preferential flow pathways in three land-cover types: (i) Active Restoration, (ii) Degraded Land, and (iii) Reference Forest, with contrasting differences in soil profile and land use history in the municipality of La Jagua de Ibirico, César department, Colombia. We conducted soil sampling, using the Beerkan method to determine Ks values. We also measured vegetation attributes (i.e., canopy cover, vegetation height, diameter at breast height, and total number of trees) and carried out three dye tracer experiments for each study site. The blue dye experiments revealed that near surface matrix infiltration was dominant for Degraded Land, while at the Active Restoration and Reference Forest, this only occurred at local surface depressions. The general infiltration pattern at the three land uses is indicated as being macropore flow with mixed interaction with the matrix and highly affected by the presence of rock fragments. The deeper infiltration patterns occur by preferential flow due to the presence of roots and rock fragments. The mean Ks for the Active Restoration (240 mm h−1) was much higher than the Ks at Degraded Land (40 mm h−1) but still considerably lower than the Reference Forest (324 mm h−1). These results indicate that top soil infiltration capacity and soil physical parameters not only directly regulate the amount of infiltration but also infiltration patterns and runoff processes, leading to lower infiltration and increased excess overland flow for Degraded Land than for other land uses. [ABSTRACT FROM AUTHOR]