Complex Background Environment Research Articles

Semantic Segmentation of Remote Sensing Imagery Based on Multiscale Deformable CNN and DenseCRF

The semantic segmentation of remote sensing images is a significant research direction in digital image processing. The complex background environment, irregular size and shape of objects, and similar appearance of different categories of remote sensing images have brought great challenges to remote sensing image segmentation tasks. Traditional convolutional-neural-network-based models often ignore spatial information in the feature extraction stage and pay less attention to global context information. However, spatial context information is important in complex remote sensing images, which means that the segmentation effect of traditional models needs to be improved. In addition, neural networks with a superior segmentation performance often suffer from the problem of high computational resource consumption. To address the above issues, this paper proposes a combination model of a modified multiscale deformable convolutional neural network (mmsDCNN) and dense conditional random field (DenseCRF). Firstly, we designed a lightweight multiscale deformable convolutional network (mmsDCNN) with a large receptive field to generate a preliminary prediction probability map at each pixel. The output of the mmsDCNN model is a coarse segmentation result map, which has the same size as the input image. In addition, the preliminary segmentation result map contains rich multiscale features. Then, the multi-level DenseCRF model based on the superpixel level and the pixel level is proposed, which can make full use of the context information of the image at different levels and further optimize the rough segmentation result of mmsDCNN. To be specific, we converted the pixel-level preliminary probability map into a superpixel-level predicted probability map according to the simple linear iterative clustering (SILC) algorithm and defined the potential function of the DenseCRF model based on this. Furthermore, we added the pixel-level potential function constraint term to the superpixel-based Gaussian potential function to obtain a combined Gaussian potential function, which enabled our model to consider the features of various scales and prevent poor superpixel segmentation results from affecting the final result. To restore the contour of the object more clearly, we utilized the Sketch token edge detection algorithm to extract the edge contour features of the image and fused them into the potential function of the DenseCRF model. Finally, extensive experiments on the Potsdam and Vaihingen datasets demonstrated that the proposed model exhibited significant advantages compared to the current state-of-the-art models.

Application of FSVD Algorithm to Airborne Gamma Detection of Trace Radionuclides in the Process of a High Radon Background

We address the performance of airborne gamma detection systems equipped with a NaI(Tl) detector to monitor radionuclides in specific areas. In particular, we analyze the use of the fast singular value decomposition (FSVD) algorithm to improve the nuclide recognition ability of the system and effectively trace radioactivity in a complex background environment. We first present a theoretical analysis of the FSVD algorithm and illustrate the nuclide recognition algorithm step by step. The core of the algorithm is singular value decomposition and parameter estimation based on a Gaussian Markov linear regression model. From the estimated values of the parameters, information about radionuclides can be effectively extracted. We assume the presence of a strong background due to a high concentration of 222Rn and its progeny, which is simulated using GEANT4. By adding trace elements of 131I and 137Cs and changing the relative emissivity, the ratio of the total energy peak count of 131I and 137Cs to the background environment interval count of the corresponding 222Rn and its progeny are controlled. Assuming a counting ratio equal to 0.005, the FSVD algorithm is still able to effectively discriminate the presence of a small number of nuclides, reflecting very excellent recognition ability. Finally, based on data from an airborne gamma detection system in a self-control radon chamber, the FSVD algorithm is employed to recognize the trace of 137Cs nuclides in a strong radon background. A DURRIDGE RAD7 radon measuring instrument is used to monitor the radon concentration in the radon chamber. The actual measurement results show that the FSVD algorithm can effectively detect 137Cs nuclides.

A High-Precision Detection Method of Apple Leaf Diseases Using Improved Faster R-CNN

Apple leaf diseases seriously affect the sustainable production of apple fruit. Early infection monitoring of apple leaves and timely disease control measures are the key to ensuring the regular growth of apple fruits and achieving a high-efficiency economy. Consequently, disease detection schemes based on computer vision can compensate for the shortcomings of traditional disease detection methods that are inaccurate and time-consuming. Nowadays, to solve the limitations ranging from complex background environments to dense and small characteristics of apple leaf diseases, an improved Faster region-based convolutional neural network (Faster R-CNN) method was proposed. The advanced Res2Net and feature pyramid network architecture were introduced as the feature extraction network for extracting reliable and multi-dimensional features. Furthermore, RoIAlign was also employed to replace RoIPool so that accurate candidate regions will be produced to address the object location. Moreover, soft non-maximum suppression was applied for precise detection performance of apple leaf disease when making inferences to the images. The improved Faster R-CNN structure behaves effectively in the annotated apple leaf disease dataset with an accuracy of 63.1% average precision, which is higher than other object detection methods. The experiments proved that our improved Faster R-CNN method provides a highly precise apple leaf disease recognition method that could be used in real agricultural practice.

A novel Dynahead-Yolo neural network for the detection of landslides with variable proportions using remote sensing images

Efficient and automatic landslide detection solutions are beneficial for regional hazard mitigation. At present, scholars have carried out landslide detection based on deep learning. However, continuous improvement regarding the accuracy of landslide detection with better feature extraction of landslides remain an essential issue, especially small-proportion landslides in the remote sensing images are difficult to identify up to date. To address this issue, we propose a detection model, the so-called Dynahead-Yolo which is designed by combining unifying scale-aware, space-aware, and task-aware attention mechanisms into the YOLOv3 framework. The proposed method focuses on the detailed features of landslide images with variable proportions, improving the ability to decode landslides in complex background environments. We determine the most efficient cascade order of the three modules and compare previous detection networks based on randomly generated prediction sets from the three study areas. Compared with the traditional YOLOv3, the detection rate of Dynahead-Yolo in small-proportion landslides and complex background landslides is increased by 13.67% and 14.12%, respectively.

Segmentation and Evaluation of Crack Image From Aircraft Fuel Tank via Atrous Spatial Pyramid Fusion and Hybrid Attention Network

Jet fuel leaks not only waste resources and increase costs but also pose a risk of emergency landings and aviation accidents. With the blossom and implementation of deep learning, crack segmentation techniques have been rapidly developed in many fields. However, it is struggle to get accurate and complete crack segmentation results because of images’ complex background environment. To address this issue, we collected and labeled a dataset of 2824 crack images from the surface of aircraft fuel tank, named CAFT2800. And this article presents an atrous spatial pyramid fusion and hybrid attention network based on deep learning to deal with the complicated environment. The backbone of network uses a hierarchical structure Swin Transformer to extract features. In the neck of the network, an atrous spatial pyramid fusion module is proposed to further capture contextual information in multiple scale. Unlike the ASPP module, which aggregates image-level information with pooling, we abandon this operation according to the characteristics of crack. And the neck structure is designed for fusing high-level semantic information to all scales. At the gate between the neck and the decoder head, a SK (selective kernel) block is embedded into the network to recalibrates channel-wise feature responses. Due to the morphological characteristics of crack, we propose an evaluation index, Thinning F1 score (TFscore), which is more meaningful compared to the commonly used F1 score. Sufficient control experiments were conducted on the CAFT2800 dataset and two complicated environment benchmarks (DeepCrack and GAPs) to test the effectiveness of the network, and our method achieved superior performance. Source code and the CAFT2800 are available at https://github.com/Gu-EH/CAFT2800.

An Improved Differentiable Binarization Network for Natural Scene Street Sign Text Detection

The street sign text information from natural scenes usually exists in a complex background environment and is affected by natural light and artificial light. However, most of the current text detection algorithms do not effectively reduce the influence of light and do not make full use of the relationship between high-level semantic information and contextual semantic information in the feature extraction network when extracting features from images, and they are ineffective at detecting text in complex backgrounds. To solve these problems, we first propose a multi-channel MSER (Maximally Stable Extreme Regions) method to fully consider color information in text detection, which separates the text area in the image from the complex background, effectively reducing the influence of the complex background and light on street sign text detection. We also propose an enhanced feature pyramid network text detection method, which includes a feature pyramid route enhancement (FPRE) module and a high-level feature enhancement (HLFE) module. The two modules can make full use of the network’s low-level and high-level semantic information to enhance the network’s effectiveness in localizing text information and detecting text with different shapes, sizes, and inclined text. Experiments showed that the F-scores obtained by the method proposed in this paper on ICDAR 2015 (International Conference on Document Analysis and Recognition 2015) dataset, ICDAR2017-MLT (International Conference on Document Analysis and Recognition 2017- Competition on Multi-lingual scene text detection) dataset, and the Natural Scene Street Signs (NSSS) dataset constructed in this study are 89.5%, 84.5%, and 73.3%, respectively, which confirmed the performance advantage of the method proposed in street sign text detection.

Research on Grape-Planting Structure Perception Method Based on Unmanned Aerial Vehicle Multispectral Images in the Field

In order to accurately obtain the distribution of large-field grape-planting sites and their planting information in complex environments, the unmanned aerial vehicle (UAV) multispectral image semantic segmentation model based on improved DeepLabV3+ is used to solve the problem that large-field grapes in complex environments are affected by factors such as scattered planting sites and complex background environment of planting sites, which makes the identification of planting areas less accurate and more difficult to manage. In this paper, firstly, the standard deviation (SD) and interband correlation of UAV multispectral images were calculated to obtain the best band combinations for large-field grape images, and five preferred texture features and two preferred vegetation indices were screened using color space transformation and grayscale coevolution matrix. Then, supervised classification methods, such as maximum likelihood (ML), random forest (RF), and support vector machine (SVM), unsupervised classification methods, such as the Iterative Self-organizing Data Analysis Techniques Algorithm (ISO DATA) model and an improved DeepLabV3+ model, are used to evaluate the accuracy of each model in combination with the field visual translation results to obtain the best classification model. Finally, the effectiveness of the classification features on the best model is verified. The results showed that among the four machine learning methods, SVM obtained the best overall classification accuracy of the model; the DeepLabV3+ deep learning scheme based on spectral information + texture + vegetation index + digital surface model (DSM) obtained the best accuracy of overall accuracy (OA) and frequency weight intersection over union (FW-IOU) of 87.48% and 83.23%, respectively, and the grape plantation area relative error of extraction was 1.9%. This collection scheme provides a research basis for accurate interpretation of the planting structure of large-field grapes.

Seedling maize counting method in complex backgrounds based on YOLOV5 and Kalman filter tracking algorithm.

Maize population density is one of the most essential factors in agricultural production systems and has a significant impact on maize yield and quality. Therefore, it is essential to estimate maize population density timely and accurately. In order to address the problems of the low efficiency of the manual counting method and the stability problem of traditional image processing methods in the field complex background environment, a deep-learning-based method for counting maize plants was proposed. Image datasets of the maize field were collected by a low-altitude UAV with a camera onboard firstly. Then a real-time detection model of maize plants was trained based on the object detection model YOLOV5. Finally, the tracking and counting method of maize plants was realized through Hungarian matching and Kalman filtering algorithms. The detection model developed in this study had an average precision mAP@0.5 of 90.66% on the test dataset, demonstrating the effectiveness of the SE-YOLOV5m model for maize plant detection. Application of the model to maize plant count trials showed that maize plant count results from test videos collected at multiple locations were highly correlated with manual count results (R2 = 0.92), illustrating the accuracy and validity of the counting method. Therefore, the maize plant identification and counting method proposed in this study can better achieve the detection and counting of maize plants in complex backgrounds and provides a research basis and theoretical basis for the rapid acquisition of maize plant population density.

Adaptive CFAR Method for SAR Ship Detection Using Intensity and Texture Feature Fusion Attention Contrast Mechanism.

Due to the complexity of sea surface environments, such as speckles and side lobes of ships, ship wake, etc., the detection of ship targets in synthetic aperture radar (SAR) images is still confronted with enormous challenges, especially for small ship targets. Aiming at the key problem of ship target detection in the complex environments, the article proposes a constant false alarm rate (CFAR) algorithm for SAR ship target detection based on the attention contrast mechanism of intensity and texture feature fusion. First of all, the local feature attention contrast enhancement is performed based on the intensity dissimilarity and the texture feature difference described by local binary pattern (LBP) between ship targets and sea clutter, so as to realize the target enhancement and background suppression. Furthermore, the adaptive CFAR ship target detection method based on generalized Gamma distribution (GΓD) which can fit the clutter well by the goodness-of-fit analyses is carried out. Finally, the public datasets HRSID and LS-SSDD-v1.0 are used to verify the effectiveness of the proposed detection method. A large number of experimental results show that the proposed method can suppress clutter background and speckle noise and improve the target-to-clutter rate (TCR) significantly, and has the relative high detection rate and low false alarm rate in the complex background and multi-target marine environments.

Dandelion segmentation with background transfer learning and RGB-attention module

Automatic weed control using weeding robots has arisen as a promising alternative for reducing the amount of herbicide applied in the agriculture and landscaping industry. Dandelion detection is a challenging task for weeding robots. To obtain accurate dandelion segmentation results, a multi-category segmentation dataset is converted into a binary segmentation dataset to learn the difference between background and foreground, which is referred to as background transfer learning. This method can help us use different datasets to overcome the problem of dandelion segmentation in a complex background environment. In addition, the color space information of the dandelion images with concentrated colors is aggregated in convolutional networks. Inspired by the attention mechanism in the neural network, we design a color-attention module to learn the weight of each color space. In this way, the color information is used to segment the dandelion. By combining background transfer learning and color-attention module methods, the dandelion segmentation method can effectively segment the dandelion with a satisfactory accuracy rate. The results of ablation experiments show that for the input images of different colors, the method proposed in this paper can learn different attention information. The proposed method can be used in agricultural and landscape industry weeding.

Power Line Scene Recognition Based on Convolutional Capsule Network with Image Enhancement

With the popularization of unmanned aerial vehicle (UAV) applications and the continuous development of the power grid network, identifying power line scenarios in advance is very important for the safety of low-altitude flight. The power line scene recognition (PLSR) under complex background environments is particularly important. The complex background environment of power lines is usually mixed by forests, rivers, mountains, buildings, and so on. In these environments, the detection of slender power lines is particularly difficult. In this paper, a PLSR method of complex backgrounds based on the convolutional capsule network with image enhancement is proposed. The enhancement edge features of power line scenes based on the guided filter are fused with the convolutional capsule network framework. First, the guided filter is used to enhance the power line features in order to improve the recognition of the power line in the complex background. Second, the convolutional capsule network is used to extract the depth hierarchical features of the scene image of power lines. Finally, the output layer of the convolutional capsule network identifies the power line and non-power line scenes, and through the decoding layer, the power lines are reconstructed in the power line scene. Experimental results show that the accuracy of the proposed method obtains 97.43% on the public dataset. Robustness and generalization test results show that it has a good application prospect. Furthermore, the power lines can be accurately extracted from the complex backgrounds based on the reconstructed module.

Study on adaptive infrared camouflage of novel positive temperature coefficient (PTC) materials in space

In order to protect important military targets from the threat of advanced infrared detection systems and guided weapons, the development of adaptive infrared decoy technology has become a top priority. In this paper, the feasibility of applying PTC material to infrared camouflage of decoy was discussed for the first time. Meanwhile, a design method for performance parameters of PTC materials that satisfy the infrared camouflage was proposed with consideration given to the complex background environment in which the space decoy was located. Taking this as a guide, the room temperature PTC materials that attain the design requirements were prepared. In this paper, the phase change characteristics of the material and the change of thermal conductivity with temperature were considered, and the heat transfer model of the PTC material was further optimized, which was verified by experiments in a vacuum environment. On this basis, the infrared camouflage properties of space decoy based on thermal control of PTC materials were further simulated. The results show that the PTC resistor prepared under the guidance of this design method can achieve a more ideal infrared camouflage effect and higher adaptive temperature control precision than ordinary resistors when applied to space decoy. The research results specify possible directions for the development and thermal control of PTC material.

Detection Method of Cow Estrus Behavior in Natural Scenes Based on Improved YOLOv5

Natural breeding scenes have the characteristics of a large number of cows, complex lighting, and a complex background environment, which presents great difficulties for the detection of dairy cow estrus behavior. However, the existing research on cow estrus behavior detection works well in ideal environments with a small number of cows and has a low inference speed and accuracy in natural scenes. To improve the inference speed and accuracy of cow estrus behavior in natural scenes, this paper proposes a cow estrus behavior detection method based on the improved YOLOv5. By improving the YOLOv5 model, it has stronger detection ability for complex environments and multi-scale objects. First, the atrous spatial pyramid pooling (ASPP) module is employed to optimize the YOLOv5l network at multiple scales, which improves the model’s receptive field and ability to perceive global contextual multiscale information. Second, a cow estrus behavior detection model is constructed by combining the channel-attention mechanism and a deep-asymmetric-bottleneck module. Last, K-means clustering is performed to obtain new anchors and complete intersection over union (CIoU) is used to introduce the relative ratio between the predicted box of the cow mounting and the true box of the cow mounting to the regression box prediction function to improve the scale invariance of the model. Multiple cameras were installed in a natural breeding scene containing 200 cows to capture videos of cows mounting. A total of 2668 images were obtained from 115 videos of cow mounting events from the training set, and 675 images were obtained from 29 videos of cow mounting events from the test set. The training set is augmented by the mosaic method to increase the diversity of the dataset. The experimental results show that the average accuracy of the improved model was 94.3%, that the precision was 97.0%, and that the recall was 89.5%, which were higher than those of mainstream models such as YOLOv5, YOLOv3, and Faster R-CNN. The results of the ablation experiments show that ASPP, new anchors, C3SAB, and C3DAB designed in this study can improve the accuracy of the model by 5.9%. Furthermore, when the ASPP dilated convolution was set to (1,5,9,13) and the loss function was set to CIoU, the model had the highest accuracy. The class activation map function was utilized to visualize the model’s feature extraction results and to explain the model’s region of interest for cow images in natural scenes, which demonstrates the effectiveness of the model. Therefore, the model proposed in this study can improve the accuracy of the model for detecting cow estrus events. Additionally, the model’s inference speed was 71 frames per second (fps), which meets the requirements of fast and accurate detection of cow estrus events in natural scenes and all-weather conditions.

Research on SLAM Road Sign Observation Based on Particle Filter.

With the development of computer hardware technology, the real-time problem of visual target tracking algorithm increasingly depends on hardware solutions. The core problem of visual target tracking is how to enhance the robustness of tracking algorithm to various complex background environments and various interference factors. Aiming at overcoming the defect that the traditional SLAM (simultaneous localization and map building) algorithm based on EKF (extended Kalman filter) has a slow repair speed for environmental interference, a Monocular SLAM_WOCPF (Monocular vision SLAM based on weight optimization combined particle filter) algorithm is proposed. The weights of all particles are reoptimized in the particle set and they are combined with the tendency of particles to degenerate and deplete. In this way, the chance of self replication of low weight particles is increased, thus increasing the diversity of the whole sample. Furthermore, the improved PF (particle filter) algorithm is applied to solve the problem of road sign observation of mobile robots, so as to expand its application scope. The results show that the mean road sign errors of the Monocular SLAM_WOCPF algorithm in two noise environments are 0.332/m and 0.441/m. The conclusion shows that the Monocular SLAM_WOCPF road sign observation method proposed in this paper can effectively improve the matching success rate of visual road signs and improve the observation quality.

Improved YOLOv5 network method for remote sensing image-based ground objects recognition

High-resolution remote sensing images have the characteristics of complex background environment, clustering of objects, etc., the complex background makes the remote sensing image contain a large number of irrelevant ground objects with a high similarity or overlap, which makes the edge and texture of the objects not clear enough, and this leads to low recognition accuracy of ground objects such as airports, dams, and golf field, although the size of this object is large. Based on this problem, this paper proposes a remote sensing image object detection method based on the YOLOv5 network. By improving the backbone extraction network, the network structure can be deepened to get more information about large objects, and the detection effect can be improved by adding an attention mechanism and adding an output layer to enhance feature extraction and feature fusion. The pre-training weight is obtained by transfer learning and used as the training weight of the improved YOLOv5 to speed up the network convergence. The experiment is carried out on the DIOR dataset, the results show that the improved YOLOv5 network can significantly improve the accuracy of large object recognition compared with the YOLO series network and the EfficientDet model on DIOR dataset, and the mAP of the improved YOLOv5 network is 80.5%, which is 2% higher than the original YOLOv5 network.

Fusion of RGB, optical flow and skeleton features for the detection of lameness in dairy cows

Lameness is common in dairy cows. Methods that used RGB-based images to detect lameness in dairy cows always have low accuracy and poor robustness because of the complex background environment involved. In this study, a lameness detection method for dairy cows based on multiple features, including RGB, optical flow and skeletons, was proposed. The network was divided into three branches according to different inputs: for Branch1 and Branch3, a convolutional neural network (CNN) was used to predict lameness according to the input images and optical flow, and for Branch2, a spatial temporal graph convolutional network (ST-GCN) was used to predict lameness according to the cows’ skeletons. Finally, the weight was adjusted, and the prediction scores of these three branches were fused to complete the lameness detection process. In this research, 680 different videos were used for training and testing. The segmentation ratio of the train set and test set was 6:1. The best ACC was 97.20% when the weight of the RGB, optical flow and skeletons was 1:0.5:0.5. To verify the robustness of the method, the gamma transform was used to adjust the brightness of the image to simulate a change in illumination. Under different illumination settings, the maximum error of the method was 2.65%, which was significantly lower than other methods without skeletons. The results showed that the proposed method was effective for the detection of early-stage lameness, severe lameness, and non-lameness of dairy cows. • Fusion of RGB, optical flow and skeleton features for the detection of lameness in dairy cows. • Cow skeletons are used to increase network attention to cows. • A spatial temporal graph convolutional network (ST-GCN) was used to predict lameness according to the cows' skeletons. • This method is more stable in the antagonism of environmental robustness.

Vision-Based Power Line Segmentation With an Attention Fusion Network

Automatic power transmission line detection plays the key role in smart grid which has been widely applied into the path planning and navigation of intelligent inspection platforms, such as Unmanned Aerial Vehicles (UAVs), climbing robots, hybrid inspection robots, etc. Nevertheless, the power lines are always against complex background environment and different illumination conditions. And the power lines occupy a minimal portion image pixels in the aerial images compared with backgrounds which causes the foreground-background class imbalance issue. Therefore, robust and accurate vision-based power line detection still faces a certain challenge. Recently, deep learning has got fast development on pixel-level object segmentation due to strong contextual feature expression ability, especially U-shape network (U-Net) and its variants. However, it still exists a certain shortcomings owing to insufficient process of local contextual features to affect the segmentation precision. Meanwhile, multiple pooling operations in deep convolutional neural networks (DCNNs) also will cause the information loss. To address these issues, with the encoder-decoder architecture, a novel vision-based power line detection network is proposed in this paper to construct an end-to-end detection scheme of power lines from aerial images. To make the segmentation network capture the global contexts and emphasize target regions of power transmission lines, an attention block is proposed to be embedded into the proposed power line detection network to address the class imbalance issue. Meanwhile, faced with the insufficient process of local contextual feature maps of DCNNs, an attention fusion block is proposed for multi-scale feature fusion to acquire more rich information and improve the segmentation precision. Experiments on power lines show that the proposed power line detection network shows a good segmentation performance on real power line environment compared with other advanced detection methods.

Intelligent Image Saliency Detection Method Based on Convolution Neural Network Combining Global and Local Information

Image saliency object detection can rapidly extract useful information from image scenes and further analyze it. At present, the traditional saliency target detection technology still has the edge of outstanding target that cannot be well preserved. Convolutional neural network (CNN) can extract highly general deep features from the images and effectively express the essential feature information of the images. This paper designs a model which applies CNN in deep saliency object detection tasks. It can efficiently optimize the edges of foreground objects and realize highly efficient image saliency detection through multilayer continuous feature extraction, refinement of layered boundary, and initial saliency feature fusion. The experimental result shows that the proposed method can achieve more robust saliency detection to adjust itself to complex background environment.

Optimization of Artistic Image Segmentation Algorithm Based on Feed Forward Neural Network under Complex Background Environment.

Based on the theory and application, this paper discusses the optimization of art image segmentation algorithm based on FFNN (Feed Forward Neural Network). In this paper, residual units are used in the corresponding stages of encoder and decoder, and feature information of several convolution layers in each convolution stage of encoder is extracted at the same time. And the feature pyramid module is used to extract multiscale features from the feature map of the last convolution stage in the encoder. Finally, pixel by pixel additions combine the previously mentioned feature information into the corresponding layer of the decoder. Additionally, an improved weight adaptive algorithm based on feature preservation is suggested in this paper, which addresses the issue that the conventional image segmentation algorithm is noise-sensitive. The adaptive connection weight mechanism is also introduced. The accuracy and recall rates of this optimization algorithm can both reach 96.574%, according to the results of 50% cross-validation. All the segmentation performance evaluation indexes of this algorithm are higher than the existing main algorithms. Moreover, the algorithm takes a short time, does not need too much manual intervention, and can effectively segment artistic images. The optimization algorithm in this paper has certain reference significance for the related research of artistic image segmentation.

Quantum Transfer Learning Approach for Deepfake Detection

Deepfake image manipulation has achieved great attention in the previous year’s owing to brings solemn challenges from the public self-confidence. Forgery detection in face imaging has made considerable developments in detecting manipulated images. However, there is still a need for an efficient deepfake detection approach in complex background environments. This paper applies the state-of-the-art quantum transfer learning approach for classifying deepfake images from original face images. The proposed model comprises classical pre-trained ResNet-18 and quantum neural network layers that provide efficient features extraction to learn the different patterns of the deepfake face images. The proposed model is validated on a real-world deepfake dataset created using commercial software. An accuracy of 96.1 % was obtained.