Genuine Images Research Articles

An efficient digital image forgery detection using Pelican search optimisation-based DCNN

ABSTRACT In the digital age, images are widely used to document events, provide evidence, and communicate information. Ensuring the authenticity of digital images is crucial to maintaining trust and integrity in various domains, including journalism, forensics, legal proceedings, and historical documentation. In this research, a PeCA-DCNN model for digital image detection is proposed, employing a hybrid approach. Initially, the data from image forgery databases is collected and preprocessed to eliminate noise and artefacts. Subsequently, the Viola–Jones algorithm detects frontal faces, and feature extraction is performed using pre-trained models VGG-16 and Resnet-101. To reduce the computational overhead, feature extraction is performed and generates a feature vector. The PeCA algorithm, combined with an adaptive self-boosted DCNN, is used to classify fake and genuine images. The PeCA algorithm enhances model performance by adjusting classifier parameters’ weights and biases. When evaluating the PeCA-DCNN, significant improvements in accuracy, sensitivity, and specificity are obtained with enhancement rates of 1.48, 3.06, and 0.05 in an 80% training scenario, and 3.92, 3.24, and 2.22 in k-fold cross-validation. These results demonstrate the effectiveness of the proposed approach compared to existing techniques.

Content-aware Nakagami morphing for incremental brain MRI

Within the carcinogenesis mechanism, from the initiation of the very first tumor cell to the preneoplastic and neoplastic cancer cell groups, cancer cells omnidirectionally and unpredictably proliferate in three-dimensional (3D) space during the promotion and progression steps. Whole tumors areas, consisting of edema, necrosis, enhancing and non-enhancing tumor subareas, could easily be identified by the typical sequences of magnetic resonance imaging (MRI), mostly by FLAIR, the fluid attenuated inversion recovery sequence. Depending on the increment value of the scanner, not every scan could be contiguous though, which might create some gaps between the slices and cause some crucial loss of information. Shape-based morphing by erosion and dilation operations might be inadequate when the source or target lesions are not found on the MRI slices, which might emerge due to unpredictable shape of tumors. Furthermore, the real trajectories and genuine shapes of the tumors in spatial view might not be seen in the planar MRI slices due to approximations during the voxel-to-pixel transformations executed in MRI devices. Therefore, we propose a novel content-aware morphing procedure to generate imaginary slices by m-parametric Nakagami imaging. The mathematical and parametric design in our framework not only leads to precise segmentation of the whole tumors according to the ground truth images; but also, to realistic morphing, based on the content of tumor footprints in MRI slices. For testing purposes, a total number of 5133 morphs are created using 1831 genuine images containing at least one lesion taken from the Brats 2012 dataset. Given the average dice score coefficients (DSC), we obtained an average of 88.44% DSC with our morphing framework outperforming the conventional dilation-based morphing result which is 83.25%. Besides being a novel morphing methodology, the outcomes of this research could also be very beneficial in smoother 3D reconstruction and more realistic volumetric calculations of lesions to help the experts working on assessment of lesions.

«Мать-Россия новых родных деток нашла…»: о роли образа ребенка в трактовке славянской идеи в «Дневнике писателя» Ф.М. Достоевского

Perceptions of the Russo-Turkish War and Russia’s foreign policy of the 1870s received much attention in “A Writer”s Diary” by Fyodor Dostoevsky. Within the narrative of Slavic suffering in the Balkans, the writer frequently employs the image of a suffering child, the central motif in the “Diary.” This paper argues for the importance of examining sections related to the Eastern Question within the broader ideological context of Dostoevsky’s journal. The identification of parallels between journalistic and artistic segments facilitates a more comprehensive understanding of the author’s perspective on the Slavic idea, particularly the significance of the child’s image in its portrayal throughout the “Diary.” While the author provides vivid depictions of Slavic children’s sufferings, he also characterizes Slavs collectively as Russia’s children in need of its advocacy and protection. The portrayal of suffering Slavic Orthodox children is integrated into the overarching system of child imagery in the “Diary,” serving as poignant evidence for the necessity of pan-Slavism. Dostoevsky associates the liberation of Orthodox peoples in the Balkans with Russia’s religious mission, emphasizing its duty to provide altruistic assistance to Slavic “children” in the name of true Christ, whose genuine image endures solely within Orthodoxy.

African-American Cultural Expression: The Defiance of Black Aesthetics

This research delves into the transformative realm of Black Aesthetics as a profound and resilient cultural resistance strategy employed by African-Americans. In a historical context marked by the degradation of the genuine image of African-Americans through Western perspectives, Black Aesthetics emerges as a dynamic force challenging stereotypes and reclaiming agency over cultural narratives. The study explores the foundational influences of key socio-political movements, namely the Harlem Renaissance, the Black Power Movement, and the Civil Rights Movement, in shaping and catalyzing the development of Black Aesthetics. During the 1920s, the Harlem Renaissance served as a crucible for cultural revitalization amid the multifaceted struggles faced by African-Americans. Fueled by a desire to break free from racial stereotypes, this movement laid the groundwork for the emergence of Black Aesthetics as a tool for empowerment and self-expression. The subsequent impact of the Black Power Movement and the Civil Rights Movement on Black Aesthetics is examined, revealing how these movements contested prevailing Western perspectives and sought to redefine the narrative surrounding African-Americans. The Black Power Movement, emphasizing self-determination and autonomy, stood in stark contrast to the assimilations goals of the Civil Rights Movement, collectively contributing to the nuanced evolution of Black Aesthetics. Through an interdisciplinary lens, this research navigates the intersection of art, ideas, and socio-political dynamics, elucidating how Black Aesthetics serves as a cultural resistance mechanism. It explores the multifaceted dimensions of this resistance, including the creation of alternative narratives, the celebration of cultural identity, and the reclamation of dignity. Ultimately, this research contributes to a comprehensive understanding of Black Aesthetics as a transformative force in cultural resistance, shedding light on its historical roots, its evolution through significant movements, and its enduring impact on reshaping the narrative of African-American identity.

TextureWGAN: texture preserving WGAN with multitask regularizer for computed tomography inverse problems.

This paper presents a deep learning (DL) based method called TextureWGAN. It is designed to preserve image texture while maintaining high pixel fidelity for computed tomography (CT) inverse problems. Over-smoothed images by postprocessing algorithms have been a well-known problem in the medical imaging industry. Therefore, our method tries to solve the over-smoothing problem without compromising pixel fidelity. The TextureWGAN extends from Wasserstein GAN (WGAN). The WGAN can create an image that looks like a genuine image. This aspect of the WGAN helps preserve image texture. However, an output image from the WGAN is not correlated to the corresponding ground truth image. To solve this problem, we introduce the multitask regularizer (MTR) to the WGAN framework to make a generated image highly correlated to the corresponding ground truth image so that the TextureWGAN can achieve high-level pixel fidelity. The MTR is capable of using multiple objective functions. In this research, we adopt a mean squared error (MSE) loss to maintain pixel fidelity. We also use a perception loss to improve the look and feel of result images. Furthermore, the regularization parameters in the MTR are trained along with generator network weights to maximize the performance of the TextureWGAN generator. The proposed method was evaluated in CT image reconstruction applications in addition to super-resolution and image-denoising applications. We conducted extensive qualitative and quantitative evaluations. We used PSNR and SSIM for pixel fidelity analysis and the first-order and the second-order statistical texture analysis for image texture. The results show that the TextureWGAN is more effective in preserving image texture compared with other well-known methods such as the conventional CNN and nonlocal mean filter (NLM). In addition, we demonstrate that TextureWGAN can achieve competitive pixel fidelity performance compared with CNN and NLM. The CNN with MSE loss can attain high-level pixel fidelity, but it often damages image texture. TextureWGAN can preserve image texture while maintaining pixel fidelity. The MTR is not only helpful to stabilize the TextureWGAN's generator training but also maximizes the generator performance.

3D MRI Segmentation using U-Net Architecture for the detection of Brain Tumor

Segmentation of brain tumor from 3D images is one of the most important and difficult tasks in the field of medical image processing as a manual human-assisted categorization can result in incorrect prediction and diagnosis. Furthermore, it is a difficult process when there is a huge amount of data to assist. Extracting brain tumour regions from MRI images becomes challenging due to the great variety of appearances of brain tumours and how similar they are to normal tissues. In this paper, we have designed modified U-Net architecture under a deep-learning framework for the detection and segmentation of brain tumors from MRI images. The applied model has been evaluated on genuine images provided by Medical Image Computing and Computer-Assisted Interventions BRATS 2020 datasets. Test accuracy of 99.4% has been achieved using the above-mentioned dataset. A comparative review with other papers shows our model using U-Net performs better than other deep learning-based models.

Diversity Patterns of Vascular Plants at Varied Elevations in Arghakhanchi, West Nepal

The genuine image of biodiversity, as well as their germplasm, is visualized by the turnover of species rather than their similarities in each location. The major goal of this study was to determine the b-diversity pattern of vascular plants growing at 100 m contour elevations and to explore its association with a-diversity and elevation. Primary data were gathered utilizing 4-6 10 x 10 m2 quadrats at 100 m contour elevation, on both the south and north sides, in Arghakhanchi district, west Nepal. Beta diversity represents the change in diversity of species between two communities and is measured by two different matrices: species turnover and similarity. Between two adjacent elevational bands, the species turnover was calculated using the Bray-Curtis dissimilarity index and similarity was assessed using the Jaccard index technique in the Vegan package for R version 4.03. The species richness (a-diversity) and b-diversity indices (Bray-Curtis dissimilarity index and Jaccard similarity index) were regressed by generalized linear model (GLM) method with elevation. The species richness and Jaccard similarity index suggests statistically significant unimodal structure with elevation; however, Bray-Curtis dissimilarity index suggests statistically significant but reverse unimodal pattern. As a result, rather than species turnover, the presence of more related species usually creates the peak area of a unimodal pattern of alpha diversity. The geographical scale of biodiversity loss or its effective preservation by human activities is revealed by beta-diversity. Any region with high beta diversity suggests a wide variety of species, which aids in the administration of conservation programs.

Application of optimum level set contour classifier tool for diagnosis of cowpox disease

A new variational definition for infection contamination dynamic shapes that powers the level set capability to be near a marked distance capability, and hence totally dispenses with the need for the exorbitant re- introduction system. Our variational definition comprises an inward energy term that performs the deviation of the level set capability from a marked distance capability, and an outgoing energy term that drives the movement of the underlying level set toward the ideal image highlights, like item boundaries. The subsequent development of the level set capability is the slope stream that limits the general energy practical. The proposed variational level set detailing enjoys three principal approaches over the customary level set plans. Initially, an essentially bigger time step can be utilized for mathematically handling the development, and thus accelerating the tilt development. Second, the level set capability can be instated with general capabilities that are more proficient to develop and simpler to use practically speaking than the broadly utilized marked distance capability. Third, the level set advancement in our plan can be effectively carried out by a basic limited distinction plot and is computationally more proficient. The proposed calculation has been applied to both mimicked and genuine images with promising outcomes.

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Person Re-Identification Using LBPH and K-Reciprocal Encoding

Individual re-identification proof (Re-ID) targets recovering an individual of interest across different non-covering cameras. With the recent development of technological algorithm and expanding request of intelligence video observation, it has acquired fundamentally expanded interest in the computer vision. Person re-identification is characterized as the issue of perceiving an individual caught in different occasions and additionally areas more than a few nonoverlapping camera sees, thinking about a huge arrangement of up-and-comers. This issue influences essentially the administration of disseminated, multiview observation frameworks, in which subjects should be followed across better places, either deduced or on-the-fly when they travel through various areas. Re-identification proof is a truly challenging issue, as more often than not individuals can be caught by a few low goal cameras, under impediment conditions, severely (and not quite the same as view to see) enlightened, and in differing presents. In this context an encoding technique K-reciprocal results using the LBPH (Local Binary Patterns Histogram) Algorithm has been proposed. This work aims to obtain a genuine image match more prone to the probe in the K-corresponding closest neighbour. When probe image is given, complementary is encoded with the k-equal nearest neighbours into a vector to rerank using the Jaccard matrix. The obtained result is a combination of a Mahalanobis metric, the Jaccard metric, and the LBPH algorithm. The reranking activity needs no Human interference in producing an appropriate enormous scale dataset. The performance of rank-1 metrics 77.27, 61.90, 76.34 &.55.11 percentage is achieved for large-scale Market-1501, CUHK03, MARS, and PRW datasets. The other metrics used for person re-id named mAP recorded 65.01%, 61.21%, 68.21% and 38.13% for the same dataset in that order.

Indian Culture and Music as a Source of Inspiration for French Opera Composers

"The current paper strives to discover and reveal the influences of Indian culture and classical Indian music in French operas. At first, the evocation of India was obtained through the subjects of the operas and stunning scenic designs, fulfilling the requirements of exoticism. Gradually, the composers attempted to include in their musical discourses exotic rhythmic and melodic elements, in some instances inspired by Indian classical music, thus aiming to evoke a genuine image of India. At the same time, the use of elements pertaining to Indian music (rāgas, rhythmic patterns, timbres) offered the musicians the possibility to create novel sound discourses. The analysis focuses on several operas, composed between the eighteenth century and the beginning of the twentieth century, following the evolution of Indian representations in several dimensions: dramatic (libretto), visual (scenic representations, dance), and musical (melody, rhythm, timbre). The present paper investigates the way Indian themes influenced the conception of the libretto, and at the same time the visual dimension of the works (setting, costumes), observing how these visual elements were gradually absorbed into the musical discourse (analysis of the melodic structures), through the incorporation of Indian rāgas in works conceived according to the rules of Western music composition. Keywords: exoticism, orientalism, India, French opera, rāga "

Image Copy–Move Forgery Detection Using Combination of Scale-Invariant Feature Transform and Local Binary Pattern Features

Today, manipulating, storing, and sending digital images are simple and easy because of the development of digital imaging devices from hardware and software points of view. Digital images are used in different contexts of people’s lives such as news, forensics, and so on. Therefore, the reliability of received images is a question that often occupies the viewer’s mind and the authenticity of digital images is increasingly important. Detecting a forged image as a genuine one as well as detecting a genuine image as a forged one can sometimes have irreparable consequences. For example, an image that is available from the scene of a crime can lead to a wrong decision if it is detected incorrectly. In this paper, we propose a combination method to improve the accuracy of copy–move forgery detection (CMFD) reducing the false positive rate (FPR) based on texture attributes. The proposed method uses a combination of the scale-invariant feature transform (SIFT) and local binary pattern (LBP). Consideration of texture features around the keypoints detected by the SIFT algorithm can be effective to reduce the incorrect matches and improve the accuracy of CMFD. In addition, to find more and better keypoints some pre-processing methods have been proposed. This study was evaluated on the COVERAGE, GRIP, and MICC-F220 databases. Experimental results show that the proposed method without clustering or segmentation and only with simple matching operations, has been able to earn the true positive rates of 98.75%, 95.45%, and 87% on the GRIP, MICC-F220, and COVERAGE datasets, respectively. Also, the proposed method, with FPRs from 17.75% to 3.75% on the GRIP dataset, has been able to achieve the best results.

Signature Verification using Convolutional Neural Network and Autoencoder

Signature has been one of the widely used verification biometrics out there. Handwritten signatures are used in cheques, forms, letters, applications, minutes, etc. The Signature of every individual is unique in nature, that is why it is essential that a person’s handwritten signature be uniquely identified. Signature Verification is a widely used method for authenticating any individual during absence. Human verification is prone to inaccuracy and sometimes indecisiveness. This paper presents an investigation of using Convolutional Neural Network (CNN) for Writer-Dependent models in signature verification. Random distortions were generated in genuine images using an autoencoder to get forged signatures, which were passed to the classifier during training. The paper details all the pre-processing steps carried out on the image and shows various test results for changing the number of training sets of images. The average test accuracy for Persian dataset is 83% when the system was trained with 22 genuine images. There was a decrease of 9.4% in accuracy when the model was trained with 9 genuine images.

Suppressing Spoof-Irrelevant Factors for Domain-Agnostic Face Anti-Spoofing

Face anti-spoofing aims to prevent false authentications of face recognition systems by distinguishing whether an image is originated from a human face or a spoof medium. In this work, we note that images from unseen domains having different spoof-irrelevant factors (e.g., background patterns and subject) induce domain shift between source and target distributions. Also, when the same SiFs are shared by the spoof and genuine images, they show a higher level of visual similarity and this hinders accurate face anti-spoofing. Hence, we aim to minimize the discrepancies among different domains via alleviating the effects of SiFs, and achieve improvements in generalization to unseen domains. To realize our goal, we propose a novel method called a Doubly Adversarial Suppression Network (DASN) that is trained to neglect the irrelevant factors and to focus more on faithful task-relevant factors. Our DASN consists of two types of adversarial learning schemes. In the first adversarial learning scheme, multiple SiFs are suppressed by deploying multiple discrimination heads that are trained against an encoder. In the second adversarial learning scheme, each of the discrimination heads is also adversarially trained to suppress a spoof factor, and the group of the secondary spoof classifier and the encoder aims to intensify the spoof factor by overcoming the suppression. We evaluate the proposed method on four public benchmark datasets, and achieve remarkable evaluation results in generalizing to unseen domains. The results demonstrate the effectiveness of the proposed method.

Finger Vein Recognition via Sparse Reconstruction Error Constrained Low-Rank Representation

Vein pattern-based methods have powerfully promoted the performance of finger vein recognition. However, it is not easy to precisely extract vein patterns from images, especially from low-quality images, and the non-vein area have been proved to be helpful for recognition. This paper proposes to use low-rank representation to extract as much noiseless discriminative information as possible from finger vein images. However, image deformation and image quality variations weaken the correlation of genuine images, and therefore damage the low-rank linear representation. To further deal with this problem, the class labels of training images and the local geometric structure between testing images and training images, reflected by sparse reconstruction errors of testing images, are used as constraints of low-rank coefficients. In particular, vein backbone decomposition based sparse representation is proposed to fast compute the deformation-robust reconstruction errors of each testing image. The reconstruction errors on sub-backbones of one training image are summed and modified as the constraint of the low-rank coefficient on this training image. We evaluate the proposed method on three widely used finger vein databases, and experimental results show that the proposed method performs well in finger vein recognition.

Yolo V4 for Advanced Traffic Sign Recognition With Synthetic Training Data Generated by Various GAN

Convolutional Neural Networks (CNN) achieves perfection in traffic sign identification with enough annotated training data. The dataset determines the quality of the complete visual system based on CNN. Unfortunately, databases for traffic signs from the majority of the world's nations are few. In this scenario, Generative Adversarial Networks (GAN) may be employed to produce more realistic and varied training pictures to supplement the actual arrangement of images. The purpose of this research is to describe how the quality of synthetic pictures created by DCGAN, LSGAN, and WGAN is determined. Our work combines synthetic images with original images to enhance datasets and verify the effectiveness of synthetic datasets. We use different numbers and sizes of images for training. Likewise, the Structural Similarity Index (SSIM) and Mean Square Error (MSE) were employed to assess picture quality. Our study quantifies the SSIM difference between the synthetic and actual images. When additional images are used for training, the synthetic image exhibits a high degree of resemblance to the genuine image. The highest SSIM value was achieved when using 200 total images as input and 32×32 image size. Further, we augment the original picture dataset with synthetic pictures and compare the original image model to the synthesis image model. For this experiment, we are using the latest iterations of Yolo, Yolo V3, and Yolo V4. After mixing the real image with the synthesized image produced by LSGAN, the recognition performance has been improved, achieving an accuracy of 84.9% on Yolo V3 and an accuracy of 89.33% on Yolo V4.

Finger Vein Recognition Based on Fusion of Deformation Information

As a convenient and fraud-proof biometric, finger vein has received increasing attention from researchers. However, the deformation problem in finger vein images is still a challenge preventing the performance of finger vein recognition from prefect. In our opinion, the deformations are distinguishable between genuine and imposter fingers rather than performing as disturbing noises. It is because that the diversity between genuine images is mainly caused by different finger postures, and this difference can be characterized and utilized. While in the imposter matching, it is not the case. In this paper, we design a framework to ensemble the encoded deformation information with traditional texture-based method to improve the recognition performance. First, the pixel-level features are extracted to represent the finger vein images. Next, the best matching for each pixel is discovered by an optimized matching. Consequently, the displacement of each pixel is obtained, and the texture features of deformation is extracted from the displacement matrices. Furthermore, the results of direct and optimized matching for pixel-level features, together with the encoded deformations are fused using weights learned by the Support Vector Machine (SVM) model. Evaluated on the publicly available databases HKPU and SDU-MLA, extensive experiments demonstrate the superiority of the proposed method, with the Equal Error Rates (EERs) of 0.0040 and 0.0266 respectively on the two databases, which are much lower than recognition solely with deformation information. The proposed method is also applicable in high security scenarios, the False Reject Rate at zero False Accept Rate (FRR-at-0-FAR) can be reduced to 0.0363 on the HKPU database through adjusting the fusion parameters.

Translation as a means of internationalization: the case for world literature

ABSTRACTTranslation and Intercultural Studies, with its interdisciplinary, cross-linguistic, and inter-cultural nature shares common grounds with world literature, which thrives on comparative studies and the internationalization of literary themes and genres. The present study explores the role of translation in building bridges of knowledge, literary appreciation, and recognition between Arabic literature and culture and other world literatures. It examines the effect of translation on the works of two Arab writers, namely Naguib Mahfouz, an Egyptian writer who won the 1988 Nobel Prize for Literature, and Mahmoud Darwish, a Palestinian poet, whose 35 poetry and prose collections have been translated into 35 languages. The translation of their works by world expert translators has been functional enough to embody the noble human values shared by international audience. The findings support the thesis that although the translation of literature may not fully convey and transform the authentic and genuine essence and images of the original, this does not render the translation of literature as a threat to the integrity of the original text. This interactive relationship between translation and world literature has proven its zest and vigor to inspire further interdisciplinary research.

Event Horizon Image within Black Hole Shadow

We argue that a genuine image of the black hole viewed by a distant observer is not its shadow, but a more compact event horizon image probed by the luminous matter plunging into black hole. The external border of the black hole shadow is washed out by radiation from matter plunging into black hole and approaching the event horizon. This effect will crucially influence the results of future observations by the Event Horizon Telescope. We show that gravitational lensing of the luminous matter plunging into black hole provides the possibility for visualization of the event horizon. The lensed image of the event horizon is formed by the highly red-shifted photons emitted by the plunging matter very near the black hole event horizon and detected by a distant observer. The resulting event horizon image is a gravitationally lensed projection on the celestial sphere of the whole black hole event horizon sphere. Seemingly, black holes are the unique objects in the Universe which may be viewed by distant observers at once from both the front and back sides.

A Snapshot of Haryana's Dynamics in The Mughal Period

Haryana has been a focus of attention throughout history, including the ancient, medieval, and modern periods. Because of its location in the north, it remained strategically important. During the Sultanate and Mughal periods, the Haryana district was extremely important in terms of trade and resistance. In ancient times, the Mahabharata was fought at Kurukshetra, and three skirmishes at Panipat, which completely transformed India's fortunes, were fought in Haryana. This study intends to contribute to the socioeconomic and unique situations of the Haryana region during the Mughal period in an objective and scientific manner (1707-1857). This period of Mughal history was chosen for research because it was a time of great change, replete with perplexity, curiosity, and upheaval. This time, the sunset represented the rise and collapse of the Sikhs', Marathas', and Jats' powers, as well as the rise and power of the theory British. It is concerning that the region's historical realities, socioeconomic, and religious problems are not fully recognized, despite attempts in both cases. This revelation was necessary in order to showcase a larger network of regional opportunities and to portray the genuine image of the time. The analysis attempts to close this gap in this way. It will also respond to the ever-changing demand, as well as the necessity to rewrite and recompose the regional history in light of the wide range of expertise and education now accessible.