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ID-Reveal: Identity-aware DeepFake Video Detection

ID-Reveal: Identity-aware DeepFake Video Detection

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international conference on Computer Vision (ICCV)

作者： Davide Cozzolino Andreas Rössler Justus Thies Matthias Nießner Luisa Verdoliva University Federico II of Naples Technical University of Munich Max Planck Institute for Intelligent Systems Tübingen

ISBN: (纸本)9781665428132

A major challenge in DeepFake forgery detection is that state-of-the-art algorithms are mostly trained to detect a specific fake method. As a result, these approaches show poor generalization across different types of facial manipulations, e.g., from face swapping to facial reenactment. To this end, we introduce ID-Reveal, a new approach that learns temporal facial features, specific of how a person moves while talking, by means of metric learning coupled with an adversarial training strategy. The advantage is that we do not need any training data of fakes, but only train on real videos. Moreover, we utilize high-level semantic features, which enables robustness to widespread and disruptive forms of post-processing. We perform a thorough experimental analysis on several publicly available benchmarks. Compared to state of the art, our method improves generalization and is more robust to low-quality videos, that are usually spread over social networks. In particular, we obtain an average improvement of more than 15% in terms of accuracy for facial reenactment on high compressed videos.

关键词： Training image coding Social networking (online) Training data Robustness Forgery Faces

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Boosting CNN Learning by Ensemble image Preprocessing Methods for Cervical Cancer Segmentation

Boosting CNN Learning by Ensemble Image Preprocessing Method...

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IEEE SSD international Multi-conference on Systems, signals and Devices

作者： Nesrine Bnouni Hadil Ben Amor Islem Rekik Mohamed Salah Rhim Basel Solaiman Najoua Essoukri Ben Amara Université de Sousse École Nationale d'Ingénieurs de Sousse LATIS-Laboratory of Advanced Technology and Intelligent Systems Sousse Tunisia Faculty of Computer and Informatics Engineering Brain And Signal Research and Analysis Laboratory Istanbul Technical University Istanbul Turkey Faculty of Medicine of Monastir Tunisia IMT Atlantique Bretagne-Pays de la Loire Ecoles Mines-Telecom France

ISBN: (纸本)9781665414937;9781665430579

Cervical cancer is the fourth most common gynecological malignant cancer in the world. It presents one of the principal causes of cancer death in women. Treatment planning depends on the cancer stage (tumor size, nodal status, and local extension), which can be identified using magnetic resonance imaging. For effective cancer diagnosis and prognosis, automated segmentation methods of cervical tumor are highly desired as they can alleviate the burden of manual segmentation. However, typical automatic segmentation methods, including deep learning methods (e.g., Convolutional Neural Networks - CNN), might fail due to intensity inhomogeneity, poor contrast, and noise present in medical images. As a solution, the performance of such methods can be boosted by using an automated image preprocessing framework. This paper first proposes an ensemble preprocessing method to improve the performance of a CNN for cervical cancer segmentation. Specifically, we propose a histogram-based, smoothing and sharpening-based, and morphological image processing methods. Then, we devise three CNNs with the same architecture. Each CNN is trained independently using each of the three proposed preprocessed datasets. For evaluation, we used leave-one-out cross-validation, where a left-out testing image sample passes through each CNN to output a probability segmentation map. Ultimately, through applying majority voting to the three outputted segmentation maps, we get the final label map. Our method significantly ( ) outperformed benchmark methods with a classification accuracy increasing from 74.1% by single CNN with no-processing to 76.8% by applying the majority voting to the three CNNs with different preprocessing methods.

关键词： Deep learning image segmentation Smoothing methods Manuals Boosting Nonhomogeneous media Planning

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Benchmarking Cameras for Open VSLAM Indoors

Benchmarking Cameras for Open VSLAM Indoors

引用

international conference on Pattern Recognition

作者： Kevin Chappellet Guillaume Caron Fumio Kanehiro Ken Sakurada Abderrahmane Kheddar University of Montpellier France UPJV MIS Laboratory Amiens France National Institute of Advanced Industrial Science and Technology (AIST) Japan

In this paper we benchmark different types of cameras and evaluate their performance in terms of reliable localization reliability and precision in Visual Simultaneous Localization and Mapping (vSLAM). Such benchmarking is merely found for visual odometry, but never for vSLAM. Existing studies usually compare several algorithms for a given camera. The evaluation methodology we propose is applied to the recent OpenVSLAM framework. The latter is versatile enough to natively deal with perspective, fisheye, 360 cameras in a monocular or stereoscopic setup, an in RGB or RGB-D modalities. Results in various sequences containing light variation and scenery modifications in the scene assess quantitatively the maximum localization rate for 360 vision. In the contrary, RGB-D vision shows the lowest localization rate, but highest precision when localization is possible. Stereo-fisheye trades-off with localization rates and precision between 360 vision and RGB-D vision. The dataset with ground truth will be made available in open access to allow evaluating other/future vSLAM algorithms with respect to these camera types.

关键词： Location awareness Visualization Service robots Hospitals Stereo image processing Robot vision systems Benchmark testing

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DRSGN: Dual Revised Semantic Graph Structured Network for image-Text Matching

DRSGN: Dual Revised Semantic Graph Structured Network for Im...

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IEEE international conference on Cloud Computing and Intelligence Systems (CCIS)

作者： Xiao Yang Xiaojun Wu Tianyang Xu School of Artificial Intelligence and Computer Science Jiangnan University Wuxi P.R. China Centre for Vision Speech and Signal Processing University of Surrey Guildford UK

ISBN: (纸本)9781665441506

image-Text matching has made significant contributions to bridge the gap in multi-modal retrieval for advanced pattern recognition systems. The key point of this task is mapping images and text semantics into a common space, where the correlations of similar pairs and dissimilar ones are distinguishable. To concentrate on obtaining semantics, most of existing methods utilize an external object detector to extract compatible region-level visual representations to match the word-level textual clues in captions. In addition to perform the local region matching, several recent studies design a classification task using global information in the final matching layer, but such global supervision signal is difficult to transmitted to the shallow feature descriptor learning layer. To address this issue, in this paper, we propose a novel Dual Revised Semantic Graph Structured Network (DRSGN) to supplement global supervision to the regional semantics adaptively in the shallow layers. In principle, DRSGN integrates regional and global descriptors to formulate an attention supervising mechanism, aiming to simultaneously highlight the regional instances and global scene to obtain complementary visual clues. A dual semantic supervising module is then established to interact between two modalities to extract the genuine matching pairs. Finally, a semantic graph consisting of the obtained multi-modal clues is designed to perform similarity reasoning between the positional relation embedded textual nodes and semantic related visual nodes. The dedicated global signals provide complementary supervision against local regions to support improved matching capacity. The experimental results on Flikr30K and MSCOCO demonstrate the effectiveness of the proposed DRSGN, with improved matching performance against the local region-based approaches

关键词： Visualization Correlation conferences Semantics Detectors Feature extraction Cognition

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Identification of Drowning Victims in Freshwater Bodies using Drift Prediction and image processing based on Deep Learning

Identification of Drowning Victims in Freshwater Bodies usin...

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international conference on advanced Computer Control, ICACC

作者： Anjana Unnikrishnan A T Roshni P R Anusha Anju M Vinny C K Anuraj Toc H Institute of Science & Technology Ernakulam India

ISBN: (纸本)9781665439206

Year after year drowning deaths are increasing tremendously, making it the 3rd leading cause of unintentional injury deaths worldwide. Drift prediction methodology is typically not used in river ecosystems and conventional methods for human rescue do not account for feasible and faster human detection. Utilization of multiple sensor data in underwater human rescue applications can capacitate faster human detection. This paper discusses the design, implementation, and testing of such an underwater human detection system, which spots the victim drifting or drowning in freshwater ecosystems. The water flow sensor attached to this portable device can calculate drift distance to track down the victim. The ultrasonic sensor activates the underwater camera upon detecting an object, to facilitate real-time human localization. We performed real-time object detection on a custom dataset by applying DarkNet-53 pre-trained weights on YOLOv3 architecture and a mean Average Precision (mAP) of 98.0% was achieved. The system attained a detection depth of 5m. Combined action of drift distance calculator and YOLOv3 real-time detection model can speed up underwater human extrication.

关键词： Performance evaluation Location awareness Ecosystems Object detection Phase frequency detectors Prediction algorithms Real-time systems

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Incremental Multi-Feature Tensor Subspace Learning Based Smart Traffic Control System and Traffic Density Calculation Using image processing

Incremental Multi-Feature Tensor Subspace Learning Based Sma...

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international conference on Computation, Automation and Knowledge Management (ICCAKM)

作者： G. Suseendran D. Akila D. Balaganesh V.R. Elangovan V. Vijayalakshmi Technology & Advanced Studies Vels Institute of Science Chennai India Computer Science and Multimedia Lincoln University College Malaysia Agurchand Manmull Jain College Chennai SRM Institute of Science and Technology Kattankulathur India

ISBN: (数字)9781728194912

ISBN: (纸本)9781728194929

In times, urban centers are growing at a high rate. Growing with them is a road traffic jam. Traffic jams, especially at peak hours, became routine. As a result, traffic management is one of the foremost pressing issues in today's towns. Several alternatives are being sought to affect the matter. These include expanding road networks, regulating the number of vehicles on the roads, and deployment of Intelligent Transportation Systems (ITSs). Aside from the ITSs, the opposite alternatives (however significant) have many practical challenges in their implementation. ITSs have supported a good range of technologies like loop sensors and video surveillance systems. Vision-based ITSs have proved advantageous over the standard methods supported loop sensors. In these modern systems, video surveillance cameras are installed along the roads and road intersections where they're wont to collect traffic data. The info is then analyzed to get traffic parameters like road traffic density. This paper presents a comfortable and stylish approach for estimating the road traffic density during daytime using image processing and computer vision algorithms. The video data collected is first weakened into frames, which are then preprocessed during a series of steps. Finally, the vehicles are detected and extracted from the pictures and Density estimated. The traffic density is then obtained because of the number of vehicles per unit area of the road section. The proposed approach was implemented in MATLAB R2013a and average vehicle detection accuracy of 96.0% and 82.1% were achieved for fast-paced and slow-moving traffic scenes.

关键词： Computer vision Roads image processing Video surveillance Cameras Intelligent sensors Matlab

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Federated Deep Learning for Heterogeneous Edge Computing

Federated Deep Learning for Heterogeneous Edge Computing

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international conference on Machine Learning and Applications (ICMLA)

作者： Khandaker Mamun Ahmed Ahmed Imteaj M. Hadi Amini Knight Foundation School of Computing and Information Sciences Florida International University Sustainability Optimization and Learning for InterDependent networks laboratory (solid lab) Miami FL USA

ISBN: (纸本)9781665443388

Nowadays, there is an ever-increasing deployment of intelligent edge devices, such as smartphones, wearable devices, and autonomous vehicles. It is enabled by the integration of advanced sensors with higher computing capabilities and widespread internet availability. These edge devices generate a vast amount of data that can be utilized for better inference. However, due to privacy concerns, communication overhead, processing delay, and security issues, traditional machine learning (ML) algorithms face challenges that work in a centralized fashion where all the available data is accumulated beforehand. Federated learning (FL) is a new distributed on-device learning method that generates a global model through the collaboration of edge devices without compromising data privacy. In this paper, we propose a federated transfer learning (FTL) model considering clients’ heterogeneity in terms of their available computing resources and model architecture. We simulate the training performance of heterogeneous clients and observe that clients with sufficient resources require significantly lower computational time. In turn, the resource-constrained clients take notably higher computational time to accomplish a given task. Inspired by that, we design an FL model that constructs multiple global models based on the available resources of the clients and carries out a separate training process for each of the global models. We demonstrate the effectiveness of our proposed strategy by evaluating our FL model on CFAR100 dataset. Our findings show that training time differs significantly among the heterogeneous clients and assigning multiple global models can notably improve convergence time.

关键词： Training Computational modeling image edge detection Transfer learning Graphics processing units Data models Workstations

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A Deep Residual Sparse and Cross Domain Reconstruction Network for Dynamic MR Imaging 2020

A Deep Residual Sparse and Cross Domain Reconstruction Netwo...

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Proceedings of the 2020 9th international conference on Computing and Pattern Recognition

作者： Zhenkun Peng Jiaxin Li Cheng Li Lan Zhang Hairong Zheng Shanshan Wang Paul C.Lauterbur Research Center for Biomedical Imaging Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China School of Fundamental Sciences China Medical University Shenyang China

ISBN: (纸本)9781450387835

In this paper, we propose a deep residual sparse and cross-domain reconstruction network to reconstruct high-quality dynamic MR image sequences from undersampled data, which alternately learns to reconstruct signals in x-f domain and image domain in an iterative manner. Specifically, in the x-f domain, we decompose the unknown signal into the prediction signal and the residual signal. In order to sparsify the residual signal as much as possible, we exploit the powerful reasoning ability of neural networks to adaptively learn the residual signal and carry out the subsequent sparse coding processing. Furthermore, the spatiotemporal correlation of dynamic MR data in x-f space is used to recover the true signal from the aliasing signal. In the image domain, bidirectional ConvLSTM and dense connections have been incorporated in a U-net based reconstruction architecture to encourage the easier transfer of information. Gradually restoring sharp image details under different resolutions is realized in the decoding part of the U-net reconstruction architecture that is designed to avoid the over-smoothness issue. Experiments on vivo MR data confirm that the proposed method can achieve superior image reconstruction quality by preserving more fine details when compared to state-of-art methods.

关键词： compressed sensing Dynamic MR imaging residual encoding image reconstruction deep learning

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Auto-Learning of Parameters for High Resolution Sparse Group Lasso SAR imagery

Auto-Learning of Parameters for High Resolution Sparse Group...

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IEEE international conference on Network Infrastructure and Digital Content (IC-NIDC)

作者： Wei Liu Hanwen Xu Cheng Fang Lei Yang Weidong Jiao Tianjin Key Laboratory for Advanced Signal Processing Civil Aviation University of China Tianjin China

ISBN: (纸本)9781665405836

Aiming at the problem of adjusting the penalty term coefficient of feature enhancement in high-resolution synthetic aperture radar (SAR) imaging, a marginal estimation Bayes (MEB) algorithm is proposed, so that the prior features of the target can be fitted properly to improve the accuracy of image feature extraction. Firstly, the alternating direction method of multipliers (ADMM) convex optimization framework is modeled based on the echoed data, and least absolute shrinkage and selection operator (Lasso) model and sparse group Lasso (SG-Lasso) model are introduced, then the maximum marginal likelihood distribution of the regularization parameters is derived. Moreover, the Moreau Yoshida unadjusted Langevin algorithm (MYULA) is used to realize target posteriori sampling solution. Because the posterior distribution is difficult to solve, the gradient projection method is introduced to estimate the regularization parameters. Finally, auto-learning parameters are used to optimize the imaging. The proposed algorithm can not only estimate the parameters of a single regularization term, but also estimate the parameters of multiple regularization terms. Aiming at non-differentiable part in the prior, MYULA is adopted to calculate the subgradient of the non-differentiable posterior distribution. Therefore, the proposed algorithm is capable of auto-leaning parameters even regularization function is non-differentiable. In the experimental part, compared with the optimal value of manual debugging, the error between the proposed method and the optimal value is within 15%, and the effectiveness of the algorithm are verified by phase transition diagram (PTD).

关键词： image resolution Estimation Imaging Manuals Debugging Feature extraction Data models

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Needle Detection in Ultrasound images using Ultrasound Beamformed RF signals

Needle Detection in Ultrasound Images using Ultrasound Beamf...

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international Conf on Electrical and Electronic Engineering (ICEEE)

作者： Mohammad I. Daoud Ayah Abuhani Adnan R. Zayadeen Rami Alazrai German Jordanian University Amman Jordan Ultrasound Section Jordanian Royal Medical Services Amman Jordan

ISBN: (纸本)9781665440714;9781665411615

Medical ultrasound imaging is used in many image-guided needle interventions to visualize the patient anatomy and the advancing needle. Despite the improved capability of ultrasound to visualize the patient anatomy, detecting the needle in ultrasound images is often a difficult task. Many methods were introduced to detect the needle in ultrasound images. However, most of these methods employed ultrasound B-mode image analysis to detect the needle. Hence, the accuracy of these methods might be degraded by the limited visibility of the needle in ultrasound B-mode images, particularly at steep insertion angles. This study introduces an effective method to accurately detect the needle in ultrasound images based on the beamformed radio frequency (RF) signals that are recorded during conventional ultrasound imaging procedures. In particular, the beamformed RF signals are processed to identify the needle echoes using two signature-based features. The identified n e edle e c hoes are analyzed using the Robust Model Fitting Random Sample Consensus (RANSAC) algorithm to detect the needle trajectory. Moreover, the needle tip is detected using a moving window procedure that quantifies t h e v a riations o f t h e b e amformed R F signals around the needle trajectory. The performance of the proposed method is evaluated by detecting the trajectories and tips of biopsy needles inserted in bovine liver tissue specimens at different insertion angles. The results show that the proposed method was able to detect the needles with needle angular deviations between 0.2° and 0.6° and needle tip deviations between 0.3 to 0.5 mm. Furthermore, the results indicate that the proposed method was able to outperform a recently introduced needle detection method. These results suggest the feasibility of employing the proposed method to achieve accurate needle detection during ultrasound-guided needle interventions.

关键词： Visualization Ultrasonic imaging image analysis Bovine RF signals Liver signal processing algorithms

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