Deep Convolutional Network Architecture Research Articles

Investigate the Application of Particle Swarm Optimization to Fine-Tune the Architecture and Parameters of Deep Convolutional Neural Networks for Enhanced Accuracy in Brain Tumor Detection from Medical Images

This study researches the use of Particle Swarm Optimization (PSO) to adjust the design and boundaries of Deep Convolutional Neural Networks (DCNNs) for improved precision in cerebrum growth recognition from clinical pictures. Different PSO calculation variants are investigated for division and grouping of MR pictures. A clever PSO-based strategy for picture gathering is presented, alongside a half and half K-Means/SBM-PSO approach for MR picture division. The proposed methods are assessed utilizing MR pictures from assorted sources, uncovering the adequacy of PSO in both picture division and advancing the K-Means grouping strategy. A half breed PSO approach is exhibited for characterizing MR pictures as typical or strange in light of the presence of mind growths, and for evaluating MR pictures as per the WHO characterization framework for cerebrum cancers. The exploratory outcomes demonstrate that the proposed techniques lead to more modest intra-bunch distances and bigger between-bunch distances, bringing about superior division results. Eminently, the reconciliation of PSO with K-Means shows improved strength and execution contrasted with individual methodologies. The reviewing discoveries recommend impediments in the ongoing X-ray approach for growth evaluating. Overall, this study highlights the potential of PSO in optimizing DCNN architectures for accurate brain tumor detection from medical images, emphasizing the effectiveness of hybrid PSO-K-Means models for improved segmentation and classification outcomes.

An integrated system for breast cancer diagnosis using convolution neural network and attention mechanism

<p>In most malignancies, breast cancer is fatal, accounting for approximately 500,000 annual deaths. The subtype of breast cancer known as Invasive Ductal Carcinoma (IDC) is surprisingly common. Pathologists commonly focus on IDC-containing regions when trying to determine if a patient has breast cancer. Although extremely fatal, survival rates and expected lifespans improve dramatically with prompt diagnosis and treatment. The treatment strategy also varies based on the breast cancer patient’s stage. In this research, we use a classification method for a publically available dataset of breast histopathology images obtained from the Kaggle. The IDC regions of the images in this dataset have been restricted for easy retrieval. The breast cancer IDC data set contains 277,524 records, of which 78,786 are positive. The 277,524 images were classified using an IDC breast cancer dataset, with 78,786 positive IDC and 198,738 negative IDC, respectively. The authors introduce a new architecture of deep convolutional neural networks and attention mechanism for classification. The model achieves state-of-the-art levels of accuracy for IDC identification, setting a new benchmark for future studies.</p>

Multiple object tracking using space-time adaptive correlation tracking

<span>In application of tracking and detecting the suspicious activities, multiple object tracking (MOT) has been given fine attention due to its application as it provides the parallel task of identification and tracking of human. MOT ensures the identification and trajectory for each object frame as they interact, despite the changes in its appearance, occlusion and various other tasks involved. Recent adoption of deep learning has given a new perspective but still achieving high metrics remains a major issue to overcome such issues, this research work presents the integrated architecture of deep convolutional covariance networks (DCCNs) and space-time adaptive correlation tracking (STACT) algorithm with similarity map function (SMF). Moreover, in proposed work, DCCNs is utilized for feature extractions through each frame capturing the distinctive information, STACT is tracking approaches that utilizes the SMF for locating and tracking objects. SMFs are updated for any changes in human appearances and motion, also it deals with occlusion. Here the proposed model is evaluated on MOT17 and MOT20 dataset. Performance analysis is carried out through comparing the existing model and Integrated-DCCN achieves higher metrics.</span>

Application of generative adversarial neural networks for the formation of databases in scanning tunneling microscopy

We discuss the development of a technique for automatic generation of databases of images obtained with a scanning tunneling microscope. An analysis of state-of-the-art methods and means of automatic processing of images obtained from probe and electron microscopes is carried out. We proposed using generative-adversarial networks for generating images taken with a scanning tunneling microscope to form training databases of images. A process of training and comparison of deep convolutional generative adversarial network (DCGAN) architectures using the OpenCV and Keras libraries together with TensorFlow is described, with the best of them identified by computing the metrics IS, FID, and KID. The scaling of images obtained from DCGAN is performed using a method of fine tuning of a super-resolution generative adversarial neural network (SRGAN) and bilinear interpolation based on the Python programming language. An analysis of calculated quantitative metrics values shows that the best results of image generation are obtained using DCGAN96 and SRGAN. It is found that FID and KID metric values for SRGAN method are better than values for bilinear interpolation in all cases except for DCGAN32. All calculations are performed on a GTX GeForce 1070 video card. A method for automatic generation of a scanning tunneling microscope image database based on the stepwise application of DCGAN and SRGAN is developed. Results of generation and comparison of the original image, the one obtained with DCGAN96 and the enlarged image with SRGAN are presented.

Performance Analysis of Deep Convolutional Network Architectures for Classification of Over-Volume Vehicles

The number of vehicle accidents has increased in recent years due to overloaded goods carriers. Off-road driving, mountain roads, and sharp edges on a road are the main causes of an imbalance in overloaded trucks. In rural areas, where smaller roads cannot accommodate high volume vehicles, such vehicles cause many problems for cars, bikes, bicycles, and other small vehicles, as well as an increase in traffic congestion in those areas. This has become a major problem in the daily lives of drivers in rural areas as well as major urban areas. Solutions are needed to detect over-volume goods carriers and alert drivers to slow down or control the volume in their trucks. This work mainly focuses on a solution that uses deep CNN models. In this work, different deep convolutional architectures are evaluated for their ability to classify goods based on their volume. The model implemented is based on a dataset-specific transfer learning process with CNN layers generated in ImageNet in which only dense layers are learned. The primary objective of this work is to identify a classification method that exhibits proven results with respect to the accuracy parameters. In this work, different deep architectures were tested, and among the efficient networks, Net-B3 was found to perform with 95% accuracy on average. The different architectures were evaluated based on their accuracy, confusion matrix, ROC curve, and AUC score with a real-time dataset.

Groundwater contamination source identification and high-dimensional parameter inversion using residual dense convolutional neural network

Data assimilation for high-dimensional parameter joint inversion of multiple time-varying source strength and hydraulic conductivity fields can be computationally intensive as a large number of forward model runs are usually required. In this study, a deep neural network-based surrogate is proposed to replace the forward model in the data assimilation process to efficiently achieve high-dimensional parameter inversion. The deep convolutional encoding-decoding network architecture is used to leverage the advantages of the convolutional network in processing image-like data, where the high-dimensional input and output fields of the forward model are expressed as images. The encoding network extracts the main features of the multiple time-varying source input image and high-dimensional hydraulic conductivity fields, and the decoding network refines the extracted features to generate the output hydraulic head and contaminant concentration fields. The Residual Dense Convolutional Neural Network (RDCNN) is proposed by combining residual learning with a densely-connected convolutional neural network to solve the image regression problem for surrogate construction. Iterative local updating ensemble smoother (ILUES) is utilized to assimilate hydraulic head and contaminant concentration data to inverse high-dimensional parameters. The proposed method is evaluated by jointly inversing the high-dimensional hydraulic conductivity and source parameters for a synthetic multiple-source contaminated aquifer. The results indicate that compared to the surrogate constructed by Dense Convolutional Neural Network (DCNN) without addressing residual learning, the RDCNN surrogate captures the model input-output relationship better with relatively high training efficiency. The RDCNN surrogate-coupled ILUES achieves state-of-the-art performance in terms of inversion accuracy of 9 pollution source parameters and 255 hydraulic conductivity field parameters and computational efficiency in comparison to the ILUES based on the forward model.

Hyperspectral Image Super-Resolution via Deep Spatiospectral Attention Convolutional Neural Networks.

Hyperspectral images (HSIs) are of crucial importance in order to better understand features from a large number of spectral channels. Restricted by its inner imaging mechanism, the spatial resolution is often limited for HSIs. To alleviate this issue, in this work, we propose a simple and efficient architecture of deep convolutional neural networks to fuse a low-resolution HSI (LR-HSI) and a high-resolution multispectral image (HR-MSI), yielding a high-resolution HSI (HR-HSI). The network is designed to preserve both spatial and spectral information thanks to a new architecture based on: 1) the use of the LR-HSI at the HR-MSI's scale to get an output with satisfied spectral preservation and 2) the application of the attention and pixelShuffle modules to extract information, aiming to output high-quality spatial details. Finally, a plain mean squared error loss function is used to measure the performance during the training. Extensive experiments demonstrate that the proposed network architecture achieves the best performance (both qualitatively and quantitatively) compared with recent state-of-the-art HSI super-resolution approaches. Moreover, other significant advantages can be pointed out by the use of the proposed approach, such as a better network generalization ability, a limited computational burden, and the robustness with respect to the number of training samples. Please find the source code and pretrained models from https://liangjiandeng.github.io/Projects_Res/HSRnet_2021tnnls.html.

An Attention-Based Deep Convolution Network for Mining Airport Delay Propagation Causality

The airport network is a highly dynamic and complex network connected by air routes, and it is difficult to study the impact of delays at one airport on another airport by means of human intervention. Due to the delay propagation law contained in the delay time series, some studies have used Granger causality and transfer entropy to explore whether there is a causal relationship between any two airports. However, no research has yet established a delay causal network from the perspective of the airport network as a whole. To this end, an attention mechanism is introduced into the deep convolutional network architecture, and a deep temporal convolution prediction model considering the attention mechanism is proposed, so as to establish the relationship between different airport delay time series under the same network architecture. According to the attention factor score, the delay propagation causality between airports is preliminarily screened, and the direct causality is verified based on a t-test and propagation delay analysis. Taking China’s civil airport network as an example, the method proposed in this paper can not only discover the causal relationship of delays between airports but also characterize the strength of the relationship. Further analysis found that each airport is affected by an average of six airports, and airports with small delays are more likely to be affected by other airports.

Deep and Machine Learning Image Classification of Coastal Wetlands Using Unpiloted Aircraft System Multispectral Images and Lidar Datasets

The recent developments of new deep learning architectures create opportunities to accurately classify high-resolution unoccupied aerial system (UAS) images of natural coastal systems and mandate continuous evaluation of algorithm performance. We evaluated the performance of the U-Net and DeepLabv3 deep convolutional network architectures and two traditional machine learning techniques (support vector machine (SVM) and random forest (RF)) applied to seventeen coastal land cover types in west Florida using UAS multispectral aerial imagery and canopy height models (CHM). Twelve combinations of spectral bands and CHMs were used. Our results using the spectral bands showed that the U-Net (83.80–85.27% overall accuracy) and the DeepLabV3 (75.20–83.50% overall accuracy) deep learning techniques outperformed the SVM (60.50–71.10% overall accuracy) and the RF (57.40–71.0%) machine learning algorithms. The addition of the CHM to the spectral bands slightly increased the overall accuracy as a whole in the deep learning models, while the addition of a CHM notably improved the SVM and RF results. Similarly, using bands outside the three spectral bands, namely, near-infrared and red edge, increased the performance of the machine learning classifiers but had minimal impact on the deep learning classification results. The difference in the overall accuracies produced by using UAS-based lidar and SfM point clouds, as supplementary geometrical information, in the classification process was minimal across all classification techniques. Our results highlight the advantage of using deep learning networks to classify high-resolution UAS images in highly diverse coastal landscapes. We also found that low-cost, three-visible-band imagery produces results comparable to multispectral imagery that do not risk a significant reduction in classification accuracy when adopting deep learning models.

Deep networks may capture biological behavior for shallow, but not deep, empirical characterizations

We assess whether deep convolutional networks (DCN) can account for a most fundamental property of human vision: detection/discrimination of elementary image elements (bars) at different contrast levels. The human visual process can be characterized to varying degrees of “depth,” ranging from percentage of correct detection to detailed tuning and operating characteristics of the underlying perceptual mechanism. We challenge deep networks with the same stimuli/tasks used with human observers and apply equivalent characterization of the stimulus–response coupling. In general, we find that popular DCN architectures do not account for signature properties of the human process. For shallow depth of characterization, some variants of network-architecture/training-protocol produce human-like trends; however, more articulate empirical descriptors expose glaring discrepancies. Networks can be coaxed into learning those richer descriptors by shadowing a human surrogate in the form of a tailored circuit perturbed by unstructured input, thus ruling out the possibility that human–model misalignment in standard protocols may be attributable to insufficient representational power. These results urge caution in assessing whether neural networks do or do not capture human behavior: ultimately, our ability to assess “success” in this area can only be as good as afforded by the depth of behavioral characterization against which the network is evaluated. We propose a novel set of metrics/protocols that impose stringent constraints on the evaluation of DCN behavior as an adequate approximation to biological processes.

SEGMENTATION OF ADDITIVE MANUFACTURING DEFECTS USING U-NET

Abstract Additive manufacturing (AM) provides design flexibility and allows rapid fabrications of parts with complex geometries. The presence of internal defects, however, can lead to deficit performance of the fabricated part. X-ray Computed Tomography (XCT) is a non-destructive inspection technique often used for AM parts. Although defects within AM specimens can be identified and segmented by manually thresholding the XCT images, the process can be tedious and inefficient, and the segmentation results can be ambiguous. The variation in the shapes and appearances of defects also poses difficulty in accurately segmenting defects. This paper describes an automatic defect segmentation method using U-Net based deep convolutional neural network (CNN) architectures. Several models of U-Net variants are trained and validated on an AM XCT image dataset containing pores and cracks, achieving a best mean intersection over union (IOU) value of 0.993. Performance of various U-Net models is compared and analyzed. Specific to AM porosity segmentation with XCT images, several techniques in data augmentation and model development are introduced. This work demonstrates that U-Net can be effectively applied for automatic segmentation of AM porosity from XCT images with high accuracy. The method can potentially help improve quality control of AM parts in an industry setting.

Deep GAN-Based Cross-Spectral Cross-Resolution Iris Recognition

In recent years, cross-spectral iris recognition has emerged as a promising biometric approach to establish the identity of individuals. However, matching iris images acquired at different spectral bands (i.e., matching a visible (VIS) iris probe to a gallery of near-infrared (NIR) iris images or vice versa) shows a significant performance degradation when compared to intraband NIR matching. Hence, in this paper, we have investigated a range of deep convolutional generative adversarial network (DCGAN) architectures to further improve the accuracy of cross-spectral iris recognition methods. Moreover, unlike the existing works in the literature, we introduce a resolution difference into the classical cross-spectral matching problem domain. We have developed two different novel techniques using the conditional generative adversarial network (cGAN) as a backbone architecture for cross-spectral iris matching. In the first approach, we simultaneously address the cross-resolution and cross-spectral matching problem by training a cGAN that jointly translates cross-resolution as well as cross-spectral tasks to the same resolution and within the same spectrum. In the second approach, we design a coupled generative adversarial network (cpGAN) architecture consisting of a pair of cGAN modules that project the VIS and NIR iris images into a low-dimensional embedding domain to ensure maximum pairwise similarity between the feature vectors from the two iris modalities of the same subject. To assure the efficacy of our methods, we perform several experiments considering multiple real-life scenarios on three publicly-available cross-spectral iris datasets. Our best experimental results obtained from the cpGAN network outperform the existing benchmark convolutional neural network (CNN) with a supervised discrete hashing (SDH) approach <xref ref-type="bibr" rid="ref1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</xref> by as much as 1.67%, and 2.22% GAR at FAR of 0.01, while our cGAN provides recognition accuracy with significantly lower EER value of 1.5%, and 1.54% for PolyU bi-spectral dataset, and Cross-eyed-cross-spectral iris recognition database, respectively. It indicates the superiority of our approaches over results previously published in the literature.

An adaptive approach to machine learning for compact particle accelerators

Machine learning (ML) tools are able to learn relationships between the inputs and outputs of large complex systems directly from data. However, for time-varying systems, the predictive capabilities of ML tools degrade if the systems are no longer accurately represented by the data with which the ML models were trained. For complex systems, re-training is only possible if the changes are slow relative to the rate at which large numbers of new input-output training data can be non-invasively recorded. In this work, we present an approach to deep learning for time-varying systems that does not require re-training, but uses instead an adaptive feedback in the architecture of deep convolutional neural networks (CNN). The feedback is based only on available system output measurements and is applied in the encoded low-dimensional dense layers of the encoder-decoder CNNs. First, we develop an inverse model of a complex accelerator system to map output beam measurements to input beam distributions, while both the accelerator components and the unknown input beam distribution vary rapidly with time. We then demonstrate our method on experimental measurements of the input and output beam distributions of the HiRES ultra-fast electron diffraction (UED) beam line at Lawrence Berkeley National Laboratory, and showcase its ability for automatic tracking of the time varying photocathode quantum efficiency map. Our method can be successfully used to aid both physics and ML-based surrogate online models to provide non-invasive beam diagnostics.

Unsupervised feature learning-based encoder and adversarial networks

In this paper, we propose a novel deep learning-based feature learning architecture for object classification. Conventionally, deep learning methods are trained with supervised learning for object classification. But, this would require large amount of training data. Currently there are increasing trends to employ unsupervised learning for deep learning. By doing so, dependency on the availability of large training data could be reduced. One implementation of unsupervised deep learning is for feature learning where the network is designed to “learn” features automatically from data to obtain good representation that then could be used for classification. Autoencoder and generative adversarial networks (GAN) are examples of unsupervised deep learning methods. For GAN however, the trajectories of feature learning may go to unpredicted directions due to random initialization, making it unsuitable for feature learning. To overcome this, a hybrid of encoder and deep convolutional generative adversarial network (DCGAN) architectures, a variant of GAN, are proposed. Encoder is put on top of the Generator networks of GAN to avoid random initialisation. We called our method as EGAN. The output of EGAN is used as features for two deep convolutional neural networks (DCNNs): AlexNet and DenseNet. We evaluate the proposed methods on three types of dataset and the results indicate that better performances are achieved by our proposed method compared to using autoencoder and GAN.

Cardiovascular MRI image analysis by using the bio inspired (sand piper optimized) fully deep convolutional network (Bio-FDCN) architecture for an automated detection of cardiac disorders

This manuscript proposes an automated detection method depends on sand piper optimized fully deep convolutional neural network (FDCN-SPO) for detecting the cardiovascular disease. In this detection process, the cardiovascular magnetic resonance imaging (CVD-MRI) is used, to extract that shape features from Kaggle cardio-vascular disease dataset using left ventricular (LV) volume, left ventricle end-diastolic volume (LVEDV), left ventricle end-systolic volume (LVESV), left ventricle mass (LVM), right ventricular (RV), right ventricular with end-diastolic volume (RVEDV) and right ventricular with end-systolic volume (RVESV). Here, the fully deep convolution neural network (Bio-FCN), and deep learning convolution filters are used to estimate the cardio vascular disease (CVD). The major intention of this proposed operation “to decrease the complexity of the calculation, cost function and increase the accuracy of fully deep convolution neural network, which was optimized using the proposed Sand Piper Optimization (SPO) algorithm”. The proposed FDCN-SPO algorithm shows the optimal accuracy and computational performance for mass of myocardial, thickness of wall, left and right ventricular volume, and ejection fraction (EF). The proposed fully deep convolutional neural network optimized with Sand Piper Optimization (FDCN-SPO) method shows 97.63% accuracy, 96.50% sensitivity, 98.01% specificity, 95.39% F-measure, 94.29% precision and 93.80% MCC value. The experimental outcomes illustrate the proposed FDCN-SPO scheme is more efficient than existing process like Random Forest Classifier with principle component analysis (RFC-PCA), Neural Network with partial least squares regression (NN-PLSR), recurrent neural network using principle component analysis and partial least squares regression (RNN-PCA & PLSR), Extreme Learning Model with Normal Sinus Rhythm (ELM-NSR).

Automatic fetal biometry prediction using a novel deep convolutional network architecture.

Fetal biometric measurements face a number of challenges, including the presence of speckle, limited soft-tissue contrast and difficulties in the presence of low amniotic fluid. This work proposes a convolutional neural network for automatic segmentation and measurement of fetal biometric parameters, including biparietal diameter (BPD), head circumference (HC), abdominal circumference (AC), and femur length (FL) from ultrasound images that relies on the attention gates incorporated into the multi-feature pyramid Unet (MFP-Unet) network. The proposed approach, referred to as Attention MFP-Unet, learns to extract/detect salient regions automatically to be treated as the object of interest via the attention gates. After determining the type of anatomical structure in the image using a convolutional neural network, Niblack's thresholding technique was applied as pre-processing algorithm for head and abdomen identification, whereas a novel algorithm was used for femur extraction. A publicly-available dataset (HC18 grand-challenge) and clinical data of 1334 subjects were utilized for training and evaluation of the Attention MFP-Unet algorithm. Dice similarity coefficient (DSC), hausdorff distance (HD), percentage of good contours, the conformity coefficient, and average perpendicular distance (APD) were employed for quantitative evaluation of fetal anatomy segmentation. In addition, correlation analysis, good contours, and conformity were employed to evaluate the accuracy of the biometry predictions. Attention MFP-Unet achieved 0.98, 1.14mm, 100%, 0.95, and 0.2mm for DSC, HD, good contours, conformity, and APD, respectively. Quantitative evaluation demonstrated the superior performance of the Attention MFP-Unet compared to state-of-the-art approaches commonly employed for automatic measurement of fetal biometric parameters.

COVID-Nets: deep CNN architectures for detecting COVID-19 using chest CT scans.

In this paper we propose two novel deep convolutional network architectures, CovidResNet and CovidDenseNet, to diagnose COVID-19 based on CT images. The models enable transfer learning between different architectures, which might significantly boost the diagnostic performance. Whereas novel architectures usually suffer from the lack of pretrained weights, our proposed models can be partly initialized with larger baseline models like ResNet50 and DenseNet121, which is attractive because of the abundance of public repositories. The architectures are utilized in a first experimental study on the SARS-CoV-2 CT-scan dataset, which contains 4173 CT images for 210 subjects structured in a subject-wise manner into three different classes. The models differentiate between COVID-19, non-COVID-19 viral pneumonia, and healthy samples. We also investigate their performance under three binary classification scenarios where we distinguish COVID-19 from healthy, COVID-19 from non-COVID-19 viral pneumonia, and non-COVID-19 from healthy, respectively. Our proposed models achieve up to 93.87% accuracy, 99.13% precision, 92.49% sensitivity, 97.73% specificity, 95.70% F1-score, and 96.80% AUC score for binary classification, and up to 83.89% accuracy, 80.36% precision, 82.04% sensitivity, 92.07% specificity, 81.05% F1-score, and 94.20% AUC score for the three-class classification tasks. We also validated our models on the COVID19-CT dataset to differentiate COVID-19 and other non-COVID-19 viral infections, and our CovidDenseNet model achieved the best performance with 81.77% accuracy, 79.05% precision, 84.69% sensitivity, 79.05% specificity, 81.77% F1-score, and 87.50% AUC score. The experimental results reveal the effectiveness of the proposed networks in automated COVID-19 detection where they outperform standard models on the considered datasets while being more efficient.

OutlierNets: Highly Compact Deep Autoencoder Network Architectures for On-Device Acoustic Anomaly Detection.

Human operators often diagnose industrial machinery via anomalous sounds. Given the new advances in the field of machine learning, automated acoustic anomaly detection can lead to reliable maintenance of machinery. However, deep learning-driven anomaly detection methods often require an extensive amount of computational resources prohibiting their deployment in factories. Here we explore a machine-driven design exploration strategy to create OutlierNets, a family of highly compact deep convolutional autoencoder network architectures featuring as few as 686 parameters, model sizes as small as 2.7 KB, and as low as 2.8 million FLOPs, with a detection accuracy matching or exceeding published architectures with as many as 4 million parameters. The architectures are deployed on an Intel Core i5 as well as a ARM Cortex A72 to assess performance on hardware that is likely to be used in industry. Experimental results on the model’s latency show that the OutlierNet architectures can achieve as much as 30× lower latency than published networks.

Automated image splicing detection using deep CNN-learned features and ANN-based classifier

With the present-day rapid evolution of digital technology, images have become one of the most important means of communication and information carrier in our society. Since the last decade, with the emergence of social networking sites like Facebook, Instagram, Twitter, etc., there has been a huge increase in the amount of information exchanged in the form of digital images, on a regular basis. While traditionally we might have had faith in the credibility of these images, today’s digital technology has begun to erode that faith. Before sharing an image over social networks, editing it with relevant software application has become one of the simplest things to do today. While not many people do this with any sinister intent behind, there has been a significant increase in cybercrimes related to malicious image manipulation and sharing. To this end, image splicing has emerged as one of the major forms of image manipulation attacks, among others today. This demands investigation of intrinsic differences between authentic and forged images and hence development of automated splicing detection tools. Here, we propose a blind image splicing detection technique that employs a deep convolutional residual network architecture as a backbone, followed by a fully connected classifier network, that classifies between authentic and spliced images. The classifier networks have been evaluated using the CASIA v2.0 dataset. Both are proven to yield accuracies more than 96% on an average, having surpassed the state-of-the-art results.

Attention-Based Context Aware Network for Semantic Comprehension of Aerial Scenery.

It is essential for researchers to have a proper interpretation of remote sensing images (RSIs) and precise semantic labeling of their component parts. Although FCN (Fully Convolutional Networks)-like deep convolutional network architectures have been widely applied in the perception of autonomous cars, there are still two challenges in the semantic segmentation of RSIs. The first is to identify details in high-resolution images with complex scenes and to solve the class-mismatch issues; the second is to capture the edge of objects finely without being confused by the surroundings. HRNET has the characteristics of maintaining high-resolution representation by fusing feature information with parallel multi-resolution convolution branches. We adopt HRNET as a backbone and propose to incorporate the Class-Oriented Region Attention Module (CRAM) and Class-Oriented Context Fusion Module (CCFM) to analyze the relationships between classes and patch regions and between classes and local or global pixels, respectively. Thus, the perception capability of the model for the detailed part in the aerial image can be enhanced. We leverage these modules to develop an end-to-end semantic segmentation model for aerial images and validate it on the ISPRS Potsdam and Vaihingen datasets. The experimental results show that our model improves the baseline accuracy and outperforms some commonly used CNN architectures.