Face manipulation is the process of modifying facial features in videos or images to produce a variety of artistic or deceptive effects. Face manipulation detection looks for altered or falsified visual media in order...
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Face manipulation is the process of modifying facial features in videos or images to produce a variety of artistic or deceptive effects. Face manipulation detection looks for altered or falsified visual media in order to differentiate between real and fake facial photographs or videos. The intricacy of the techniques used makes it difficult to detect face manipulation, particularly in the context of technologies like deepFake. This paper presents an efficient framework based on Hybrid learning and Kernel Principal Component Analysis (KPCA) to extract more extensive and refined face-manipulating attributes. The proposed method utilizes the EfficientNetV2-L model for feature extraction, topped up with KPCA for feature dimensionality reduction, to distinguish between real and fake facial images. The proposed method is robust to various facial manipulations techniques such as identity swap, expression swap, attribute-based manipulation, and entirely synthesized faces. In this work, data augmentation is used to solve the problem of class imbalance present in the dataset. The proposed method has less execution time while achieving an accuracy of 99.3% and an F1 Score of 0.98 on the Diverse Fake Face Dataset (DFFD).
deeplearning (DL) models have emerged as alternative methods to conventional ultrasound (US) signal processing, offering the potential to mimic signal processing chains, reduce inference time, and enable the portabil...
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deeplearning (DL) models have emerged as alternative methods to conventional ultrasound (US) signal processing, offering the potential to mimic signal processing chains, reduce inference time, and enable the portability of processing chains across hardware. This article proposes a DL model that replicates the fine-tuned BMode signal processing chain of a high-end US system and explores the potential of using it with a different probe and a lower end system. A deep neural network (DNN) was trained in a supervised manner to map raw beamformed in-phase and quadrature component data into processed images. The dataset consisted of 30 000 cardiac image frames acquired using the GE HealthCare Vivid E95 system with the 4Vc-D matrix array probe. The signal processing chain includes depth-dependent bandpass filtering, elevation compounding, frequency compounding, and image compression and filtering. The results indicate that a lightweight DL model can accurately replicate the signal processing chain of a commercial scanner for a given application. Evaluation on a 15-patient test dataset of about 3000 image frames gave a structural similarity index measure (SSIM) of 98.56 +/- 0.49. Applying the DL model to data from another probe showed equivalent or improved image quality. This indicates that a single DL model may be used for a set of probes on a given system that targets the same application, which could be a cost-effective tuning and implementation strategy for vendors. Furthermore, the DL model enhanced image quality on a Verasonics dataset, suggesting the potential to port features from high-end US systems to lower end counterparts.
images once were considered a reliable source of information. However, when photo-editing software started to get noticed it gave rise to illegal activities which is called image tampering. These days we can come acro...
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images once were considered a reliable source of information. However, when photo-editing software started to get noticed it gave rise to illegal activities which is called image tampering. These days we can come across innumerable tampered images across the internet. Software such as Photoshop, GNU image Manipulation Program, etc. are applied to form tampered images from real ones in just a few minutes. To discover hidden signs of tampering in an imagedeeplearning models are an effective tool than any other methods. Models used in deeplearning are capable of extracting intricate features from an image automatically. Here we proposed a combination of traditional handcrafted features along with a deeplearning model to differentiate between authentic and tampered images. We have presented a dual-branch Convolutional Neural Network in conjunction with Error Level Analysis and noise residuals from Spatial Rich Model. For our experiment, we utilized the freely accessible CASIA dataset. After training the dual-branch network for 16 epochs, it generated an accuracy of 98.55%. We have also provided a comparative analysis with other previously proposed work in the field of image forgery detection. This hybrid approach proves that deeplearning models along with some well-known traditional approaches can provide better results for detecting tampered images.
Due to the increasing demand for artificial intelligence technology in today's society, the entire industrial production system is undergoing a transformative process related to automation, reliability, and robust...
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Due to the increasing demand for artificial intelligence technology in today's society, the entire industrial production system is undergoing a transformative process related to automation, reliability, and robustness, seeking higher productivity and product competitiveness. Additionally, many hardware platforms are unable to deploy complex algorithms due to limited resources. To address these challenges, this paper proposes a computationally efficient lightweight convolutional neural network called Brightness Improved by Light-DehazeNet, which removes the impact of fog and haze to reconstruct clear images. Additionally, we introduce an efficient hardware accelerator architecture based on this network for deployment on low-resource platforms. Furthermore, we present a brightness visibility restoration method to prevent brightness loss in dehazed images. To evaluate the performance of our method, extensive experiments were conducted, comparing it with various traditional and deeplearning-based methods, including images with artificial synthesis and natural blur. The experimental results demonstrate that our proposed method excels in dehazing ability, outperforming other methods in comprehensive comparisons. Moreover, it achieves rapid processing speeds, with a maximum frame rate of 105 frames per second, meeting the requirements of real-timeprocessing.
An efficient method for predicting occupants' thermal comfort is crucial for developing optimal environmental control strategies while minimizing energy consumption in buildings. This paper presents a non-invasive...
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An efficient method for predicting occupants' thermal comfort is crucial for developing optimal environmental control strategies while minimizing energy consumption in buildings. This paper presents a non-invasive visionbased personal comfort model that integrates thermographic images and deeplearning. Unlike previous studies, the entire thermographic image of the upper body is directly used during model training, minimizing complex data processing and maximizing the use of rich skin temperature distribution. The proposed method is validated using thermographic images and corresponding thermal sensation votes (TSV) from 10 participants under different experimental conditions. Results show that the model based on a 3-point TSV scale achieves exceptional classification performance with an average accuracy of 99.51 %, outperforming existing models. The model performance using a 7-point TSV scale is slightly lower, with an average accuracy of 89.90 %. This method offers potential for integrating thermal comfort models into real-time building environmental control, optimizing occupant comfort and energy consumption.
The efficiency of intelligent sugarcane harvesters in harvesting depends on the effectiveness of identifying and locating the sugarcane during the harvesting process. In the actual harvesting process, accurately extra...
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The efficiency of intelligent sugarcane harvesters in harvesting depends on the effectiveness of identifying and locating the sugarcane during the harvesting process. In the actual harvesting process, accurately extracting valid features of sugarcane amidst the dense and interwoven sugarcane becomes a challenging task. To address this issue, we propose a hybrid deeplearning approach to extract sugarcane stem contours and internal stem node feature information from sugarcane efficiently in the context of a complex harvest. Firstly, this study combined the MobileNetV3 and U-Net networks to segment overall images that contain information about the external contours of the sugarcane stem. Then, the extracted overall profile images were optimized using a variety of imageprocessing techniques to meet the requirements of harvesting. Lastly, the improved YOLOX model was utilized to identify the internal stem node features of sugarcane from the optimized overall images. The experimental results on a real sugarcane dataset show that the proposed external sugarcane stem segmentation model achieves a high mean intersection over union (MIoU) of 91.68% with an average segmentation time of just 0.025 seconds. Moreover, the proposed model for internal stem node recognition in sugarcane achieves an average precision (AP) of 96.19% with an average detection time of 0.026 seconds. Additionally, this study compares image segmentation models such as PSPNet and deepLabv3+ with target detection models such as YoloV5 and YoloV7. The experimental results show that the sugarcane feature extraction models proposed in this article all exhibit high accuracy and robustness.
The proceedings contain 27 papers. The topics discussed include: fast multi-modal reuse: co-occurrence pre-trained deeplearning models;deeplearning for fast super-resolution reconstruction from multiple images;an ef...
ISBN:
(纸本)9781510626577
The proceedings contain 27 papers. The topics discussed include: fast multi-modal reuse: co-occurrence pre-trained deeplearning models;deeplearning for fast super-resolution reconstruction from multiple images;an efficient algorithm for fast block matching motion estimation using an adaptive threshold scheme;low exposure image frame generation algorithms for feature extraction and classification;parallel image and video self-recovery scheme with high recovery capability;learning optimal actions with imperfect images;CNN classification based on global and local features;kalman-based motion estimation in video surveillance systems for safety applications;and recent advances in integrated photonic-electronic technologies for high-speed processing and communication circuits for light-based transducers.
The accurate detection of traffic signs is a critical component of self-driving systems, enabling safe and efficient navigation. In the literature, various methods have been investigated for traffic sign detection, am...
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The accurate detection of traffic signs is a critical component of self-driving systems, enabling safe and efficient navigation. In the literature, various methods have been investigated for traffic sign detection, among which deeplearning-based approaches have demonstrated superior performance compared to other techniques. This paper justifies the widespread adoption of deeplearning due to its ability to provide highly accurate results. However, the current research challenge lies in addressing the need for high accuracy rates and real-timeprocessing requirements. In this study, we propose a convolutional neural network based on the YOLOv8 algorithm to overcome the aforementioned research challenge. Our approach involves generating a custom dataset with diverse traffic sign images, followed by conducting training, validation, and testing sets to ensure the robustness and generalization of the model. Experimental results and performance evaluation demonstrate the effectiveness of the proposed method. Extensive experiments show that our model achieved remarkable accuracy rates in traffic sign detection, meeting the real-time requirements of the input data.
To analyze images in various fields of science and technology, it is often necessary to count observed objects and determine their parameters. This can be quite labor-intensive and time-consuming. This article present...
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To analyze images in various fields of science and technology, it is often necessary to count observed objects and determine their parameters. This can be quite labor-intensive and time-consuming. This article presents DLgram, a universal, user-friendly cloud service that is developed for this purpose. It is based on deeplearning technologies and does not require programming skills. The user labels several objects in the image and uploads it to the cloud where the neural network is trained to recognize the objects being studied. The user receives recognition results, which if necessary, can be corrected, errors removed, or missing objects added. In addition, it is possible to carry out mathematical processing of the data obtained to get information about the sizes, areas, and coordinates of the observed objects. The article describes the service features and discusses examples of its application. The DLgram service allows to reduce significantly the time spent on quantitative image analysis, reduce subjective factor influence, and increase the accuracy of analysis.
The smart applications development worldwide demands for ultra-reliable data communication to assure the richness of data and processing in time. These smart applications create massive amounts of data to be processed...
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The smart applications development worldwide demands for ultra-reliable data communication to assure the richness of data and processing in time. These smart applications create massive amounts of data to be processed in 6G networks with advanced technologies. 6G big data analytics become the demand for next-generation data communication and smart city applications. Traditional data analytics algorithms lag in efficiency while processing big data due to huge volume, data dependency and timely processing. A deeplearning model called reinforcement learning is promising for processing big data in smart applications. The proposed study, advanced big data Analytics using deeplearning (ABDAS-DL), gives a pioneering approach that combines deep Reinforcement learning (DRL) based deep Q network (DQN) with long-term, short-term memory (LSTM) for harnessing the vast capacity of 6G connectivity within the domain of advanced big data analytics. This study utilises smart transport-based data for taxi route optimisation by analysing climatic and surrounding factors. The look of 6G connectivity guarantees incredible facts of data transmission speeds and tremendously low latency, taking off new horizons for managing large datasets in realtime. The performance of the proposed model is measured in terms of processingtime, network, reliability and scalability. The proposed model takes 30 s to process the data and fix the taxi route, while another traditional model consumes more than an hour.
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