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WildDeepfake: A Challenging Real-World Dataset for Deepfake Detection [article]

Bojia Zi, Minghao Chang, Jingjing Chen, Xingjun Ma, Yu-Gang Jiang
2021 arXiv   pre-print
We also propose two (eg. 2D and 3D) Attention-based Deepfake Detection Networks (ADDNets) to leverage the attention masks on real/fake faces for improved detection.  ...  While this has greatly advanced deepfake detection, most of the real videos in these datasets are filmed with a few volunteer actors in limited scenes, and the fake videos are crafted by researchers using  ...  We also propose two (eg. 2D and 3D) Attention-based Deepfake Detection Networks (ADDNets) to leverage the attention masks on real/fake faces for improved detection.  ... 
arXiv:2101.01456v1 fatcat:j4tmugxjvfbbbi6mbxsa2gxeky

3D CNN Architectures and Attention Mechanisms for Deepfake Detection [chapter]

Ritaban Roy, Indu Joshi, Abhijit Das, Antitza Dantcheva
2022 Advances in Computer Vision and Pattern Recognition  
AbstractManipulated images and videos have become increasingly realistic due to the tremendous progress of deep convolutional neural networks (CNNs).  ...  Toward this in this work, we study the ability of state-of-the-art video CNNs including 3D ResNet, 3D ResNeXt, and I3D in detecting manipulated videos.  ...  It replaces 2D convolutional layers of the original Inception model by 3D convolutions for spatio-temporal modeling and inflates pre-trained weights of the Inception model on ImageNet as its initial weight  ... 
doi:10.1007/978-3-030-87664-7_10 fatcat:vkmuim2vujaipaggyhwjguuy54

ADD: Attention-Based DeepFake Detection Approach

Aminollah Khormali, Jiann-Shiun Yuan
2021 Big Data and Cognitive Computing  
This paper presents an attention-based DeepFake detection (ADD) method that exploits the fine-grained and spatial locality attributes of artificially synthesized videos for enhanced detection.  ...  ADD framework is composed of two main components including face close-up and face shut-off data augmentation methods and is applicable to any classifier based on convolutional neural network architecture  ...  The Residual Networks (ResNets) [65] are a type of deep convolutional neural network where blocks of convolutional layers are skipped using shortcut connections.  ... 
doi:10.3390/bdcc5040049 fatcat:qihcgeydx5ccblg5ngjdm5ptnq

DA-FDFtNet: Dual Attention Fake Detection Fine-tuning Network to Detect Various AI-Generated Fake Images [article]

Young Oh Bang, Simon S. Woo
2021 arXiv   pre-print
In this work, we propose Dual Attention Fake Detection Fine-tuning Network (DA-FDFtNet) to detect the manipulated fake face images from the real face data.  ...  More recent research has introduced few-shot learning, which uses a small amount of training data to produce fake images and videos more effectively.  ...  Adam, Mobilenets: Efficient convolutional neural net- deep convolutional neural networks, in: Advances in neural information works for mobile vision applications, arXiv preprint arXiv:  ... 
arXiv:2112.12001v1 fatcat:b7wmsotrqndqhd5e7j4cigiray

A New Deep Learning-Based Methodology for Video Deepfake Detection Using XGBoost

Aya Ismail, Marwa Elpeltagy, Mervat S. Zaki, Kamal Eldahshan
2021 Sensors  
This paper presents a new deepfake detection method: you only look once–convolutional neural network–extreme gradient boosting (YOLO-CNN-XGBoost).  ...  Due to their devastating impacts on the world, distinguishing between real and deepfake videos has become a fundamental issue.  ...  Then, the 3D convolutional neural network is applied to capture the spatial-temporal features and detect deepfake videos.  ... 
doi:10.3390/s21165413 pmid:34450855 pmcid:PMC8398984 fatcat:pnifjwgcu5ehjfjemcj4isqtla

Deep Learning for Multimedia Forensics

Irene Amerini, Aris Anagnostopoulos, Luca Maiano, Lorenzo Ricciardi Celsi
2021 Foundations and Trends in Computer Graphics and Vision  
Usually, these networks are used after a convolutional neural network or a recurrent neural network that work as feature extractors, that is, they learn how to extract relevant features that are useful  ...  A.1.2 Convolutional Neural Networks Convolutional neural networks (CNNs) are a specific kind of neural network for processing data that has a known grid-like structure.  ... 
doi:10.1561/0600000096 fatcat:eb22cg3okzdx5knaarrwo646za

A Review of Deep Learning-based Approaches for Deepfake Content Detection [article]

Leandro A. Passos, Danilo Jodas, Kelton A. P. da Costa, Luis A. Souza Júnior, Danilo Colombo, João Paulo Papa
2022 arXiv   pre-print
Detection of counterfeit content has raised attention in the last few years for the advances in deepfake generation.  ...  This paper presents a comprehensive review of recent studies for deepfake content detection using deep learning-based approaches.  ...  [42] proposed an attention-based convolutional neural network, namely ADDNets, for deepfake detection.  ... 
arXiv:2202.06095v1 fatcat:27ogp2kj4jayvmoe5xbhhi33li

DeepRhythm: Exposing DeepFakes with Attentional Visual Heartbeat Rhythms [article]

Hua Qi and Qing Guo and Felix Juefei-Xu and Xiaofei Xie and Lei Ma and Wei Feng and Yang Liu and Jianjun Zhao
2020 arXiv   pre-print
will be disrupted or even entirely broken in a DeepFake video, making it a potentially powerful indicator for DeepFake detection.  ...  In this work, we propose DeepRhythm, a DeepFake detection technique that exposes DeepFakes by monitoring the heartbeat rhythms.  ...  Besides only adopting convolutional neural networks (CNNs), some researchers use a combined recurrent neural network (RNN) and CNN to extract image and temporal features to distinguish real and fake videos  ... 
arXiv:2006.07634v2 fatcat:gkyxcqnhv5alfp3ysscuxvytkm

A Survey on Deepfake Video Detection

Peipeng Yu, Zhihua Xia, Jianwei Fei, Yujiang Lu
2021 IET Biometrics  
Recently, deepfake videos, generated by deep learning algorithms, have attracted widespread attention. Deepfake technology can be used to perform face manipulation with high realism.  ...  This review aims to demonstrate the current research status of deepfake video detection, especially, generation process, several detection methods and existing benchmarks.  ...  In the field of deepfake detection, neural networks are widely used to distinguish forgery videos.  ... 
doi:10.1049/bme2.12031 fatcat:cdwx3jw6ajczplw7le6jsl5wy4

Automated Deepfake Detection [article]

Ping Liu, Yuewei Lin, Yang He, Yunchao Wei, Liangli Zhen, Joey Tianyi Zhou, Rick Siow Mong Goh, Jingen Liu
2021 arXiv   pre-print
Unlike previous works manually design neural networks, our method can relieve us from the high labor cost in network construction.  ...  In this paper, we propose to utilize Automated Machine Learning to adaptively search a neural architecture for deepfake detection.  ...  We use our method to search neural cells, stack them hierarchically to arXiv:2106.10705v3 [cs.CV] 12 Aug 2021 build a deep neural network for deepfake detection.  ... 
arXiv:2106.10705v3 fatcat:ivhabnk3vbby5o2eltfrch2hga

DFDT: An End-to-End DeepFake Detection Framework Using Vision Transformer

Aminollah Khormali, Jiann-Shiun Yuan
2022 Applied Sciences  
A common theme of existing detection methods is using Convolutional Neural Networks (CNNs) as a backbone.  ...  DFDT is specifically designed for deepfake detection tasks consisting of four main components: patch extraction & embedding, multi-stream transformer block, attention-based patch selection followed by  ...  Typical components of most state-of-the-art deepfake detection approaches are convolutional neural networks, and facial regions cropped out of an entire image [14] [15] [16] .  ... 
doi:10.3390/app12062953 fatcat:6vdlyruobjdpfnriivzj3xzzy4

DeepFake MNIST+: A DeepFake Facial Animation Dataset [article]

Jiajun Huang, Xueyu Wang, Bo Du, Pei Du, Chang Xu
2021 arXiv   pre-print
Various DeepFake detection methods and datasets are proposed for detecting such data, especially for face-swapping.  ...  However, our experiments show that the existed datasets are not sufficient to develop reliable detection methods. While the current liveness detector cannot defend such videos as the attack.  ...  Same with MesoInception, we also used the 256x256 resolution of frames as the input. Resnet: The Residual Neural network (Resnet) [20] is one of the most popular neural networks.  ... 
arXiv:2108.07949v1 fatcat:khp2aiqq3fh3tlvplevh4he6fy

An Exploratory Analysis on Visual Counterfeits using Conv-LSTM Hybrid Architecture

Mohammad Farukh Hashmi, B Kiran Kumar Ashish, Avinash G. Keskar, Neeraj Dhanraj Bokde, Jin Hee Yoon, Zong Woo Geem
2020 IEEE Access  
INDEX TERMS DeepFakes, generative adversarial network (GANs), facial landmarks, convolutional neural networks (CNN), recurrent neural network (RNN), visual counterfeits.  ...  This temporal-detection pipeline compares very minute visual traces on the faces of real and fake frames using Convolutional Neural Network (CNN) and stores the abnormal features for training.  ...  Video classification methods have evolved from hand craft features until 2D and recent 3D Conv nets [25] - [27] . Recurrent neural networks have also been utilized to model video sequences.  ... 
doi:10.1109/access.2020.2998330 fatcat:dnmk264igjfrvbfwex3awk55sa

ID-Reveal: Identity-aware DeepFake Video Detection [article]

Davide Cozzolino and Andreas Rössler and Justus Thies and Matthias Nießner and Luisa Verdoliva
2021 arXiv   pre-print
A major challenge in DeepFake forgery detection is that state-of-the-art algorithms are mostly trained to detect a specific fake method.  ...  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.  ...  of the first CNN methods proposed for DeepFake detection which uses dilated convolutions with inception modules.  ... 
arXiv:2012.02512v3 fatcat:j7do67ktenbstoye66xucybfsy

The eyes know it: FakeET – An Eye-tracking Database to Understand Deepfake Perception [article]

Parul Gupta, Komal Chugh, Abhinav Dhall, Ramanathan Subramanian
2020 arXiv   pre-print
Given that the principal purpose of deepfakes is to deceive human observers, FakeET is designed to understand and evaluate the ease with which viewers can detect synthetic video artifacts.  ...  We present FakeET– an eye-tracking database to understand human visual perception of deepfake videos.  ...  [1] proposed MesoInc-4, an inception inspired [21] convolutional neural network with a small number of layers.  ... 
arXiv:2006.06961v2 fatcat:5ppqezyczjaojhanmn25il3lyy
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