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A Coarse-to-fine Deep Convolutional Neural Network Framework for Frame Duplication Detection and Localization in Forged Videos [article]

Chengjiang Long, Arslan Basharat, Anthony Hoogs
2019 arXiv   pre-print
In this paper, we propose a novel coarse-to-fine framework based on deep Convolutional Neural Networks to automatically detect and localize such frame duplication.  ...  Videos can be manipulated by duplicating a sequence of consecutive frames with the goal of concealing or imitating a specific content in the same video.  ...  In this paper, we propose a novel coarse-to-fine deep learning framework, denoted as C2F-DCNN, for frame duplication detection and localization in forged videos.  ... 
arXiv:1811.10762v2 fatcat:hkbijwcgwbchnil7ws55rmxxdy

Duplicate Frame Video Forgery Detection Using Siamese-based RNN

Maryam Munawar, Iram Noreen
2021 Intelligent Automation and Soft Computing  
The first step in the proposed framework is to extract the features and convert videos into frames. I3D network receives an original and a forged video to detect frame-to-frame duplication.  ...  A novel deep learning framework consisting of Inflated 3D (I3D) and Siamese-based Recurrent Neural Network (RNN) is proposed to resolve the aforementioned issues.  ...  The proposed framework is a deep learning approach based on Inflated 3D (I3D) network and Siamese-based Recurrent Neural Network (SRNN) for frame duplication.  ... 
doi:10.32604/iasc.2021.018854 fatcat:urz2ao4xfvdplh72ah5m3p5cmi

Efficient Approach towards Detection and Identification of Copy Move and Image Splicing Forgeries Using Mask R-CNN with MobileNet V1

Kalyani Dhananjay Kadam, Swati Ahirrao, Ketan Kotecha, Suneet Kumar Gupta
2022 Computational Intelligence and Neuroscience  
This research work also provides a forged percentage score for a region in an image.  ...  However, the deep learning networks for handling these forgeries are expensive in terms of the high number of parameters, storage, and computational cost.  ...  . e research study in [56] uses color illumination, deep convolution neural networks, and semantic segmentation to detect and localize image splicing forgery.  ... 
doi:10.1155/2022/6845326 pmid:35035463 pmcid:PMC8754624 fatcat:4ff622eef5bfjac5w5i4nogjf4

Detecting and Locating Passive Video Forgery Based on Low Computational Complexity Third-Order Tensor Representation

Yasmin M. Alsakar, Nagham E. Mekky, Noha A. Hikal
2021 Journal of Imaging  
Based on the used computer's configurations, an average time of 35 s. is needed to detect and locate 40 forged frames out of 300 frames.  ...  Experimental results and comparisons show the superiority of the proposed scheme with a precision value of up to 99% in detecting and locating both types of attacks for static as well as dynamic videos  ...  Acknowledgments: The authors thank Department of Information Technology, Faculty of Computers and Information Science, Mansoura University.  ... 
doi:10.3390/jimaging7030047 pmid:34460703 fatcat:djbnutbzvngmpfn63roaeoj2zq

Media Forensics and DeepFakes: an overview [article]

Luisa Verdoliva
2020 arXiv   pre-print
This review paper aims to present an analysis of the methods for visual media integrity verification, that is, the detection of manipulated images and videos.  ...  On the one hand, this opens the door to a series of exciting applications in different fields such as creative arts, advertising, film production, video games.  ...  Double compression detection has been also extended to H.264 video analysis in [125] , where a two-stream neural network is proposed to analyze separately intra-coded frames and predictive frames.  ... 
arXiv:2001.06564v1 fatcat:b3izh3hcmrae5frbd2bv3tmlgu

Learning Neural Textual Representations for Citation Recommendation

Binh Thanh Kieu, Inigo Jauregi Unanue, Son Bao Pham, Hieu Xuan Phan, Massimo Piccardi
2021 2020 25th International Conference on Pattern Recognition (ICPR)  
; Skocaj, Danijel 1532 End-To-End Training of a Two-Stage Neural Network for Defect Detection GAP: Quantifying the Generative Adversarial Set and Class Feature Applicability of Deep Neural Networks Deep  ...  Feature Engineering and Stacked Echo State Networks for Musical Onset Detection DAY 2 -Jan 13, 2021 Hou, Zejiang; Kung, SY 2636 A Discriminant Information Approach to Deep Neural Network Pruning  ... 
doi:10.1109/icpr48806.2021.9412725 fatcat:3vge2tpd2zf7jcv5btcixnaikm

Artificial Intelligence for Text-Based Vehicle Search, Recognition, and Continuous Localization in Traffic Videos

Karen Panetta, Landry Kezebou, Victor Oludare, James Intriligator, Sos Agaian
2021 AI  
In this paper, we address these challenges by proposing V-Localize, a novel artificial intelligence framework for vehicle search and continuous localization captured from live traffic videos based on input  ...  Additionally, to the best of our knowledge, no metrics currently exist for evaluating the robustness and performance efficiency of a vehicle recognition model on live videos and even less so for vehicle  ...  [34] proposed using coarse-to-fine convolutional neural network architecture for achieving fine-grained vehicle model and make recognition.  ... 
doi:10.3390/ai2040041 fatcat:y5uegij4fvh7tl5cpplugsrxci

Face Flashing: a Secure Liveness Detection Protocol based on Light Reflections [article]

Di Tang, Zhe Zhou, Yinqian Zhang, Kehuan Zhang
2018 arXiv   pre-print
The overall accuracy of our liveness detection system is 98.8\%, and its robustness was evaluated in different scenarios. In the worst case, our system's accuracy decreased to a still-high 97.3\%.  ...  To overcome these limitations, we propose a new liveness detection protocol called Face Flashing that significantly increases the bar for launching successful attacks on face authentication systems.  ...  ACKNOWLEDGMENT We thank our shepherd Muhammad Naveed for his patient guidance on improving this paper, and anonymous reviewers for their insightful comments. We also want to thank Tao  ... 
arXiv:1801.01949v2 fatcat:vug5rlhlzbckbbdxb6lpjyz5f4

Comparative Analysis of Different Machine Learning Classifiers for the Prediction of Chronic Diseases [chapter]

Rajesh Singh, Anita Gehlot, Dharam Buddhi
2022 Comparative Analysis of Different Machine Learning Classifiers for the Prediction of Chronic Diseases  
A comparative study of different machine learning classifiers for chronic disease prediction viz Heart Disease & Diabetes Disease is done in this paper.  ...  Precise diagnosis of these diseases on time is very significant for maintaining a healthy life.  ...  Acknowledgement The authors of this manuscript thanks Uttaranchal University for encouragement and motivation in using advance technology in Horticulture. 7. many research studies are being done to improve  ... 
doi:10.13052/rp-9788770227667 fatcat:da47mjbbyzfwnbpde7rgbrlppe

Ocular biometrics: A survey of modalities and fusion approaches

Ishan Nigam, Mayank Vatsa, Richa Singh
2015 Information Fusion  
machine learning algorithms for better representation and classification, (iv) developing algorithms for ocular recognition at a distance, (v) using multimodal ocular biometrics for recognition, and (  ...  Biometrics, an integral component of Identity Science, is widely used in several large-scale-county-wide projects to provide a meaningful way of recognizing individuals.  ...  The authors would like to thank the Editorin-Chief, Associate Editor, and reviewers for their insightful feedback on the paper. The authors also acknowledge Dr. Maria De Marsico and Dr.  ... 
doi:10.1016/j.inffus.2015.03.005 fatcat:ph2katoyuzdylamlesnt7vzbay

30th Annual Computational Neuroscience Meeting: CNS*2021–Meeting Abstracts

2021 Journal of Computational Neuroscience  
Currently, most functional models of neural activity are based on firing rates, while the most relevant signals for inter-neuron communication are spikes.  ...  One of the goals of neuroscience is to understand the computational principles that describe the formation of behaviorally relevant signals in the brain, as well as how these computations are realized  ...  O14 On the role of arkypallidal and prototypical neurons for neural synchronization in the basal ganglia Acknowledgements This work was supported by NIH-NIBIB (R01-EB027584).  ... 
doi:10.1007/s10827-021-00801-9 pmid:34931275 pmcid:PMC8687879 fatcat:evpmmfpaivgpxdqpive5xdgmwu

Dynamical neural networks: Modeling low-level vision at short latencies

L. Perrinet
2007 The European Physical Journal Special Topics  
We study how the substrate of this system, that is local biochemical neural processes, could combine to give rise to an efficient and global perception.  ...  Our goal is to understand the dynamics of neural computations in low-level vision.  ...  This hypothesis thus provides a conceptual framework which may enable us to further understand the principles behind our neural Acknowledgments The author thanks the team at the Redwood Neuroscience  ... 
doi:10.1140/epjst/e2007-00061-7 fatcat:ljct5ea6bnekrm2zolkx5rx3ky

Artificial Intelligence [article]

John Paul Mueller Luca Massaron
2018 Zenodo  
A major part of the problem is that movies, television shows, and books have all conspired to give false hopes as to what AI will accomplish.  ...  In addition, the human tendency to anthropomorphize (give human characteristics to) technology makes it seem as if AI must do more than it can hope to accomplish.  ...  Detecting Edges and Shapes from Images Convolutional Neural Networks (also known as ConvNet or CNN) have fuelled the recent deep learning renaissance.  ... 
doi:10.5281/zenodo.5599660 fatcat:rn6nyx5xinehzpwfy3knurtzgy

Towards multi-modal face recognition in the wild

Chao Xiong, Tae-Kyun Kim, Imperial College London
To begin with, we aims to learn a part-based facial representation with deep neural networks to address face verification in the wild.  ...  In particular, the proposed framework consists of two deliberate components: a Deep Mixture Model (DMM) to find accurate patch correspondence and a C [...]  ...  Convolutional Neural Network Fully-connected layer is one of the most common layers used in deep neural network.  ... 
doi:10.25560/34935 fatcat:bgn7ennck5amzcaywailvt3ddu

Machine Learning-Based Multimedia Analytics

Daniel Mas Montserrat
Furthermore, we present multiple neural network architectures and systems for commercial logo detection, 3D pose estimation and tracking, deepfakes detection, and manipulation detection in satellite images  ...  We present several image synthesis and rendering techniques to generate new images for training neural networks.  ...  use a temporal-aware pipeline composed by a Convolutional Neural Network (CNN) and a Recurrent Neural Network (RNN) to detect DeepFake videos.  ... 
doi:10.25394/pgs.12616292.v1 fatcat:2fgi4u3oozcxblntqm32imwamu
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