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Enhancing Deepfake Detection: Spatial-Temporal Preprocessing and Self-Attention ResI3D Model 6

Enhancing Deepfake Detection: Spatial-Temporal Preprocessing...

引用

6th Artificial Intelligence and Cloud Computing conference (AICCC) / 5th Asia Digital image processing conference

作者： Sangho, Son Jaekyu, Lee Kyungha, Min Kim, Wooju Yonsei Univ Dept Ind Engn 50 Yonsei Ro Seoul 03722 South Korea LG CNS AI Engn Team 71Magokjungang 8 Ro Seoul 07795 South Korea

ISBN: (纸本)9798400716225

Deepfake technology is the outcome of employing deep learning techniques to overlay the face of one individual onto the video of another. As deep learning technology advances rapidly, the proliferation of high-quality deepfakes for malicious digital activities is notably on the rise. With growing concerns about the misuse of deepfake technology, there is an increasing demand for research into deep learning-based methodologies to detect and counteract it. While Deepfake detection using deep learning has been a subject of prior research, these approaches primarily rely on images hence not utilizing temporal information. Additionally, research combining CNN and RNN has inherent limitations. It operates with compressed data, resulting in the loss of spatial information and the utilization of the inherent temporal characteristics in pixel-to-pixel temporal data. In this study, we propose a detection model that harnesses the inherent attributes of video data through self-attention on both the spatial and temporal axes, using the ResI3D model along with the Non-Local Block. Additionally, we conducted experiments during the preprocessing phase to validate and implement methods that facilitate the model's effective learning of both temporal and spatial information. As a result, our model demonstrated enhanced performance when compared to existing deepfake video detection models.

关键词： computer vision Neural Networks Anomaly Detection Deepfakes

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computer vision Based 3D Model Floor Construction for Smart Parking System 6th

Computer Vision Based 3D Model Floor Construction for Smart...

引用

6th IFIP International conference on Internet of things, IFIP IoT 2023

作者： Patra, Jayaprakash Panda, Satyajit Negi, Vipul Singh Chinara, Suchismita National Institute of Technology Rourkela Odisha Rourkela India

ISBN: (纸本)9783031458811

A Smart Parking system has a lot of components, such as an automated parking infrastructure, sensors, and a navigation system. For the implementation of the navigation system in smart parking, a 3D floor map is required. A 3D view of maps is always better than traditional maps, but making a 3D model comes at a cost and requires specialized tools. Infrastructures such as hospitals and offices usually have little luxury when it comes to maintaining their parking spaces, and the proposed system provides a simple yet effective solution for this problem in this paper. Till now, images are two-dimensional, and tools like Lidar or Kinect are used to get the depth element right. However, to make the floor construction handy, portable, and lightweight, a smartphone image-based approach is proposed here to make a 3D model of indoor parking lots. the pillars and the separation walls between parking spaces are easy to identify using deep learning models. A convolution neural network-based architecture was used for object detection. the main problem that remains is to calculate the depth of the objects in the image. Here in this paper, a successful approach is proposed to overcome the problem of finding depth in images. © 2024, IFIP International Federation for Information processing.

关键词： computer vision

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Designing a Sequential Hybrid BCI Speller on Fusion of Motor imagery and Steady-State Visually Evoked Potentials EEG Data 14

Designing a Sequential Hybrid BCI Speller on Fusion of Motor...

引用

14th indian conference on computer vision, graphics and image processing, ICVGIP 2023

作者： Nama, Tutan Samanta, Debasis Department of Computer Science and Engineering Indian Institute of Technology Kharagpur West Bengal Kharagpur India

ISBN: (纸本)9798400716256

Brain-computer interfaces (BCIs) enable direct communication between the human brain and external devices, interpreting signals like Electroencephalogram (EEG) to translate user intentions into commands. While EEG-based BCIs have potential, they face accuracy and user training challenges. Hybrid BCIs (hBCIs) integrate various input modalities to enhance robustness of BCI applications. Based on the operation mode hBCIs are two types, sequential and simultaneous. In simultaneous hBCI two or more input modalities work together to achieve a single goal. Sequential hBCIs, where modalities work in sequence, offer intuitive interactions. this work introduces a hybrid BCI speller combining MI and SSVEP in a sequential operation mode of hybrid BCI. Two-class MI EEG signals navigate through character groups at the initial stage and initiate SSVEP-based spelling. the interface integrates letters, punctuation, space, error correction operators, and digits. Promising results show an average entry rate of 8.28 char/min with 92.35% accuracy among six participants. this study showcases the potential of hybrid BCI spellers, enhancing communication for individuals with motor impairments. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Brain computer interface

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ColCompNeT: Deep Learning-Based Colorization-Based Coding Network 1

引用

4th International conference on Machine Intelligence and Signal processing, MISP 2022

作者： Mishra, Dipti Singh, Satish Kumar Mahindra University Hyderabad500043 India Indian Institute of Information Technology Allahabad Allahabad211012 India

ISBN: (数字)9789819900855

ISBN: (纸本)9789819900848

Converting a grayscale image to a visually plausible and perceptually meaningful color image is an exciting research topic in computer vision and graphics. However, predicting the chrominance channels from a grayscale image is an ill-posed problem, especially when the objective is to produce the same color as in the ground truth image, like for the application of compression using colorization or video/image restoration. Compression using colorization efficiently reduces the storage space and transmission bandwidth by two-thirds and maintains the image’s visual quality. However, colorizing decompressed gray images exactly as ground truth with maximum accuracy is a challenging task. In this paper, learning-based CNN network ColCompNeT has been proposed in which concept of parallel training of colorization and compression network is utilized. With the proposed algorithm, a maximum bit saving of 36.45% is achieved with the improved objective and subjective performance when compared with the state-of-the-art methods. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Network coding

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MAFT: An image Super-Resolution Method Based on Mixed Attention and Feature Transfer 1

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6th Asia Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint International conference on Web and Big Data (APWeb-WAIM)

作者： Liu, Xin Li, Jing Cui, Yuanning Zhu, Wei Qian, Luhong Nanjing Univ Aeronaut & Astronaut Coll Artificial Intelligence Coll Comp Sci & Technol Nanjing 211106 Peoples R China Nanjing Univ Nanjing 210023 Peoples R China Kunshan Huaheng Welding Co Ltd Suzhou 215300 Peoples R China

ISBN: (数字)9783031251986

ISBN: (纸本)9783031251979;9783031251986

Reference-based image super-resolution methods, which enhance the restoration of a low-resolution (LR) images by introducing an additional high-resolution (HR) reference image, have made rapid and remarkable progress in the field of image super-resolution in recent years. Most of the existing methods use an implicit correspondence matching approach to transfer HR features from the reference image (Ref) to the LR image. However, these methods lack the further judgment and processing of the HR features from Ref, which limits them in challenging cases. In this paper, We propose an image super-resolution method based on mixed attention and feature transfer (MAFT). First, we obtain the deep features of the LR and Ref images through the encoder network, then extract the transferred features from Ref through the attention network, and perform adaptive optimization processing on the features, and finally fuse the transferred features with LR features to achieve a high-quality image reconstruction. the quantitative and qualitative experiments on these benchmarks, i.e., CUFED5, Urban100 and Manga109, show that MAFT outperforms the state-of-the-art baselines with significant improvements.

关键词： computer vision Machine learning Super-resolution Attention mechanism

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OpenCV-based SF6 density controller dial pointer angle recognition 6

OpenCV-based SF6 density controller dial pointer angle recog...

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2022 6th International conference on Electrical, Mechanical and computer Engineering, ICEMCE 2022

作者： Yu, Zhangsi Ning, Zhang Yang, Houcheng School of Mechanical Engineering Xijing University Shaanxi Xi'an710000 China

To solve the problems of the low efficiency and poor accuracy of the manual detection in the factory inspection of the SF6 density controller, this paper proposes an SF6 density controller dial pointer angle identification method. First, the image of the dial pointer at a specific position is acquired according to the working characteristics of the SF6 density controller. the center and radius of the contour circle inside the scale line in the dial are extracted using the Hoff gradient method and the image is coordinate transformed using this parameter. then the coordinate-transformed image is pre-processed. And finally, the threshold of the image after pre-processing is traversed and the length is used to characterize the pointer angle through the correspondence between the scale line and the angle. the angle of the pointer is characterized by the correspondence between the scale and the angle using the length. this paper uses Python and OpenCV vision library to complete the algorithm and through experiments, the algorithm can achieve high precision pointer angle recognition. © Published under licence by IOP Publishing Ltd.

关键词： Controllers

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An Avenue Study of Convolutional Neural Network for image Stratification 6th

An Avenue Study of Convolutional Neural Network for Image St...

引用

6th International conference on Recent Innovations in Computing, ICRIC 2023

作者： Pal, Ashok Yadav, Neetu Department of Mathematics Chandigarh University Mohali India

ISBN: (纸本)9789819734412

In our rapidly advancing world, the automatic classification of images has become one of the most intricate challenges within the realm of computer vision. this complexity arises from the constant evolution in the recognition of digital content. It is more challenging for a computer to automatically grasp and analyze images than it is for a human visions. CNN is used for image classification which has prominent part of deep learning algorithm. Deep learning we prevail as like speech recognition, classification of objects in video games through automatic algorithm of machine learning as well. these images could be any kind of as like dog, cat, bus, elephant, butterfly, chair, and so on. It uses convolutional layers for filtering worthwhile information in automatic image processing. this paper has showed that there are numerous prominent CNN architecture for classifying images. In order to predict the class label for new objects, a model has been trained using a labeled datasets of images and their accompanying class labels. On the another hand we will be extracted all the features of images like edges, shapes, group of pixels, texture attributes, spatial to identify an object. In this course of action the research group has used Tensorflow’s and PyTorch and scikit-learn to classifying images. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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Model Based Anomaly Detection in High Dimensional DATA 6

Model Based Anomaly Detection in High Dimensional DATA

引用

6th International conference on Advanced Technologies for Signal and image processing, ATSIP 2022

作者： Gatfaoui, Imen Solaiman, Basel Farah, Imed Riadh National Institute of Computer Science Riadi Laboratory Imt Atlantique Iti Laboratory Tunis Tunisia Imt Atlantique Department of Image and Information Processing Brest France National Institute of Computer Science Riadi Laboratory Tunis Tunisia

ISBN: (纸本)9781665451161

Anomaly detection is a growing research issue in several application domains. this paper attempts to provide a structured overview of anomaly detection research. A state-of-the-art of anomaly detection techniques is then presented. A classification of methods based both on the type of datasets (Big DATA, data flow, graphs, time series, etc.), application domains (fraud detection, intrusion detection, medical anomaly detection, etc.) and the approach considered (Deep Learning, statistical, classification, clustering based, etc.) is proposed. We propose a multi-vision approach of case base representation for anomaly detection in a high- dimensional data using Case-Based Reasoning. © 2022 IEEE.

关键词： Anomaly detection

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Unique Identity Generation with Global Features from Multimodal Biometric Data 14

Unique Identity Generation with Global Features from Multimo...

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14th indian conference on computer vision, graphics and image processing, ICVGIP 2023

作者： Dash, Priyabrata Samanta, Debasis Sarma, Monalisa Indian Institute of Technology Kharagpur West Bengal Kharagpur India

ISBN: (纸本)9798400716256

the patterns in biometric data, such as fingerprints, iris, etc., are random and distinct from one individual to another, making them ideal for generating unique identities suitable for many applications. this work proposes creating users' unique identities based on their fingerprint and iris biometric data. the global feature descriptor, which is invariant to scale, rotation, and translation, is considered in this work. the global feature descriptors are obtained from fingerprint and iris images and fused to create a single feature vector. Next, feature selection based on the Laplacian score is applied to remove irrelevant features. Further, a metric-learning-based mechanism is introduced to enhance the feature vectors' intra-class similarity and inter-class separability. Finally, a novel quantization mechanism is used to generate a unique identity. Experiments with multimodal datasets show that the generated identities are distinct, random, revocable, and unlinkable. this identity generation scheme can be utilized in storage security, digital signature schemes, remote authentication protocols, blockchain security, etc. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Authentication

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Fine-grain Cluster Estimation of Land Cover Classes using Landsat 8 Multi-spectral images 14

Fine-grain Cluster Estimation of Land Cover Classes using La...

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14th indian conference on computer vision, graphics and image processing, ICVGIP 2023

作者： Pal, Shankho Subhra Chatterjee, Ankita Mukhopadhyay, Jayanta Sarkar, Sudeshna Das, Partha Pratim Indian Institute of Technology Kharagpur West Bengal Kharagpur India Ashoka University Haryana Sonipat India

ISBN: (纸本)9798400716256

Earth observation satellites provide us with ample amount of raw data for land cover analysis. However, annotating these data is a cumbersome process, subjected to human error which compel us to shift from supervised to unsupervised techniques. Although clustering methods are being widely used for the past few years in the field of remote sensing, identifying the number of fine-grain classes present in a region remain a challenging problem. therefore, we propose a rule-based and neural-network learning technique that can divide the pixels into three standard classes, water bodies, vegetation and vegetation-void. these classes are easier to identify and is not region-specific. Later we apply fine-grain clustering on each of these classes to segregate them into finer groups. Our clustering method identifies the appropriate number of fine-grain classes present in a specific region. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Landsat

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