Image deraining is a highly ill-posed *** significant progress has been made due to the use of deep convolutional neural networks,this problem still remains challenging,especially for the details restoration and gener...
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Image deraining is a highly ill-posed *** significant progress has been made due to the use of deep convolutional neural networks,this problem still remains challenging,especially for the details restoration and generalization to real rain *** this paper,we propose a deep residual channel attention network(DeRCAN)for *** channel attention mechanism is able to capture the inherent properties of the feature space and thus facilitates more accurate estimations of structures and details for image *** addition,we further propose an unsupervised learning approach to better solve real rain images based on the proposed *** qualitative and quantitative evaluation results on both synthetic and real-world images demonstrate that the proposed DeRCAN performs favorably against state-of-the-art methods.
With the rising popularity of online social interactions, emojis play a pivotal role in communication, effectively conveying people's emotions. Hence, accurately converting facial micro-expressions into correspond...
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Recommender systems aim to filter information effectively and recommend useful sources to match users' requirements. However, the exponential growth of information in recent social networks may cause low predictio...
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The rapid development of the Internet has led to the widespread dissemination of manipulated facial images, significantly impacting people's daily lives. With the continuous advancement of Deepfake technology, the...
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The rapid development of the Internet has led to the widespread dissemination of manipulated facial images, significantly impacting people's daily lives. With the continuous advancement of Deepfake technology, the generated counterfeit facial images have become increasingly challenging to distinguish. There is an urgent need for a more robust and convincing detection method. Current detection methods mainly operate in the spatial domain and transform the spatial domain into other domains for analysis. With the emergence of transformers, some researchers have also combined traditional convolutional networks with transformers for detection. This paper explores the artifacts left by Deepfakes in various domains and, based on this exploration, proposes a detection method that utilizes the steganalysis rich model to extract high-frequency noise to complement spatial features. We have designed two main modules to fully leverage the interaction between these two aspects based on traditional convolutional neural networks. The first is the multi-scale mixed feature attention module, which introduces artifacts from high-frequency noise into spatial textures, thereby enhancing the model's learning of spatial texture features. The second is the multi-scale channel attention module, which reduces the impact of background noise by weighting the features. Our proposed method was experimentally evaluated on mainstream datasets, and a significant amount of experimental results demonstrate the effectiveness of our approach in detecting Deepfake forged faces, outperforming the majority of existing methods.
Vehicular crowdsensing has recently received considerable attention, due to its promising capability of collecting useful information for the Internet of Vehicles. However, existing researches in crowdsensing mainly f...
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The integration of the contrastive learning paradigm into deep clustering has led to enhanced performance in image clustering. However, in existing researches, the samples in the class of the target may be still treat...
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In the era of network communication,digital image encryption(DIE)technology is critical to ensure the security of image ***,there has been limited research on combining deep learning neural networks with chaotic mappi...
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In the era of network communication,digital image encryption(DIE)technology is critical to ensure the security of image ***,there has been limited research on combining deep learning neural networks with chaotic mapping for the encryption of digital ***,this paper addresses this gap by studying the generation of pseudo-random sequences(PRS)chaotic signals using dual logistic chaotic *** signals are then predicted using long and short-term memory(LSTM)networks,resulting in the reconstruction of a new chaotic *** the research process,it was discovered that there are numerous training parameters associated with the LSTM network,which can hinder training *** overcome this challenge and improve training efficiency,the paper proposes an improved particle swarm optimization(IPSO)algorithm to optimize the LSTM ***,the obtained chaotic signal from the optimized model training is further scrambled,obfuscated,and diffused to achieve the final encrypted *** research presents a digital image encryption(DIE)algorithm based on a double chaotic map(DCM)and *** algorithm demonstrates a high average NPCR(Number of Pixel Change Rate)of 99.56%and a UACI(Unified Average Changing Intensity)value of 33.46%,indicating a strong ability to resist differential ***,the proposed algorithm realizes secure and sensitive digital image encryption,ensuring the protection of personal information in the Internet environment.
The rapid acceleration of urbanization and industrialization has led to a significant increase in PM2.5 pollution, making it a critical global concern. The accurate prediction of PM2.5 concentrations is of utmost impo...
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Gesture recognition has diverse application prospects in the field of human-computer ***,gesture recognition devices based on strain sensors have achieved remarkable results,among which liquid metal materials have con...
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Gesture recognition has diverse application prospects in the field of human-computer ***,gesture recognition devices based on strain sensors have achieved remarkable results,among which liquid metal materials have considerable advantages due to their high tensile strength and *** improve the detection sensitivity of liquid metal strain sensors,a sawtooth-enhanced bending sensor is proposed in this *** with the results from previous studies,the bending sensor shows enhanced resistance *** addition,combined with machine learning algorithms,a gesture recognition glove based on the sawtooth-enhanced bending sensor is also fabricated in this study,and various gestures are accurately *** the fields of human-computer interaction,wearable sensing,and medical health,the sawtooth-enhanced bending sensor shows great potential and can have wide application prospects.
Current state-of-the-art QoS prediction methods face two main limitations. Firstly, most existing QoS prediction approaches are centralized, gathering all user-service invocation QoS records for training and optimizat...
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