A Collaborative Alignment Framework of Transferable Knowledge Extraction for Unsupervised Domain Adaptation

Xie, Binhui; Li, Shuang; Lv, Fangrui; Liu, Chi Harold; Wang, Guoren; Wu, Dapeng

doi:10.1109/tkde.2022.3185233

Cited by 36 publications

(7 citation statements)

References 44 publications

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“…Lastly, the facial images are input into the CNN to obtain the 2048‐dimensional feature vectors. The collected feature vectors are stored to easily load them into the BP‐DANN for the training of unsupervised domain adaptive (B. Xie et al, 2023). Furthermore, the features extraction CNN pre‐trained on a big deepfake dataset might well be utilized to extract additional transferrable feature vectors, reducing the gap between the source and target throughout unsupervised domain adaptive training.…”

Section: Deep Fake Detection Mechanismsmentioning

confidence: 99%

Deepfake detection using deep learning methods: A systematic and comprehensive review

Heidari,

Jafari Navimipour,

Dag

et al. 2023

WIREs Data Min & Knowl

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Deep Learning (DL) has been effectively utilized in various complicated challenges in healthcare, industry, and academia for various purposes, including thyroid diagnosis, lung nodule recognition, computer vision, large data analytics, and human‐level control. Nevertheless, developments in digital technology have been used to produce software that poses a threat to democracy, national security, and confidentiality. Deepfake is one of those DL‐powered apps that has lately surfaced. So, deepfake systems can create fake images primarily by replacement of scenes or images, movies, and sounds that humans cannot tell apart from real ones. Various technologies have brought the capacity to change a synthetic speech, image, or video to our fingers. Furthermore, video and image frauds are now so convincing that it is hard to distinguish between false and authentic content with the naked eye. It might result in various issues and ranging from deceiving public opinion to using doctored evidence in a court. For such considerations, it is critical to have technologies that can assist us in discerning reality. This study gives a complete assessment of the literature on deepfake detection strategies using DL‐based algorithms. We categorize deepfake detection methods in this work based on their applications, which include video detection, image detection, audio detection, and hybrid multimedia detection. The objective of this paper is to give the reader a better knowledge of (1) how deepfakes are generated and identified, (2) the latest developments and breakthroughs in this realm, (3) weaknesses of existing security methods, and (4) areas requiring more investigation and consideration. The results suggest that the Conventional Neural Networks (CNN) methodology is the most often employed DL method in publications. According to research, the majority of the articles are on the subject of video deepfake detection. The majority of the articles focused on enhancing only one parameter, with the accuracy parameter receiving the most attention.This article is categorized under: Technologies > Machine Learning Algorithmic Development > Multimedia Application Areas > Science and Technology

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Section: Deep Fake Detection Mechanismsmentioning

confidence: 99%

Deepfake detection using deep learning methods: A systematic and comprehensive review

Heidari,

Jafari Navimipour,

Dag

et al. 2023

WIREs Data Min & Knowl

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“…Deep learning algorithms like improved pulse-coupled CNN, and transfer learning are used in challenging image processing applications ( Liu et al, 2022a ; Zhao et al, 2022 ). Unsupervised deep learning models in health, business, and e-commerce provides various decision process based on image processing datasets ( Xie et al, 2022 ; Zhang et al, 2021 ). Computer vision is not limited, today every digital application requires vision technologies for biometrics and other identification applications ( Zeng et al, 2020 ).…”

Section: Related Workmentioning

confidence: 99%

An efficient transfer learning based cross model classification (TLBCM) technique for the prediction of breast cancer

Jakkaladiki¹,

Malý²

2023

PeerJ Computer Science

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Breast cancer has been the most life-threatening disease in women in the last few decades. The high mortality rate among women is due to breast cancer because of less awareness and a minimum number of medical facilities to detect the disease in the early stages. In the recent era, the situation has changed with the help of many technological advancements and medical equipment to observe breast cancer development. The machine learning technique supports vector machines (SVM), logistic regression, and random forests have been used to analyze the images of cancer cells on different data sets. Although the particular technique has performed better on the smaller data set, accuracy still needs to catch up in most of the data, which needs to be fairer to apply in the real-time medical environment. In the proposed research, state-of-the-art deep learning techniques, such as transfer learning, based cross model classification (TLBCM), convolution neural network (CNN) and transfer learning, residual network (ResNet), and Densenet proposed for efficient prediction of breast cancer with the minimized error rating. The convolution neural network and transfer learning are the most prominent techniques for predicting the main features in the data set. The sensitive data is protected using a cyber-physical system (CPS) while using the images virtually over the network. CPS act as a virtual connection between human and networks. While the data is transferred in the network, it must monitor using CPS. The ResNet changes the data on many layers without compromising the minimum error rate. The DenseNet conciliates the problem of vanishing gradient issues. The experiment is carried out on the data sets Breast Cancer Wisconsin (Diagnostic) and Breast Cancer Histopathological Dataset (BreakHis). The convolution neural network and the transfer learning have achieved a validation accuracy of 98.3%. The results of these proposed methods show the highest classification rate between the benign and the malignant data. The proposed method improves the efficiency and speed of classification, which is more convenient for discovering breast cancer in earlier stages than the previously proposed methodologies.

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“…The computer vision challenges are addressed using capsule technology in forensic problems. Dynamic routing technique with capsule network ( Sabour, Frosst & Hinton, 2017 ; Li, Du & Wei, 2021 ; Xie et al, 2022 ) is being used to represent relationship hierarchal with demonstrating object pieces. Three capsules are used in routing the images and classifying the real and fake models ( Ma et al, 2021 ; Wang et al, 2017 ).…”

Section: Literature Reviewmentioning

confidence: 99%

VIOLA jones algorithm with capsule graph network for deepfake detection

Venkatachalam

Trojovský

Hubálovský

2023

PeerJ Computer Science

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DeepFake is a forged image or video created using deep learning techniques. The present fake content of the detection technique can detect trivial images such as barefaced fake faces. Moreover, the capability of current methods to detect fake faces is minimal. Many recent types of research have made the fake detection algorithm from rule-based to machine-learning models. However, the emergence of deep learning technology with intelligent improvement motivates this specified research to use deep learning techniques. Thus, it is proposed to have VIOLA Jones’s (VJ) algorithm for selecting the best features with Capsule Graph Neural Network (CN). The graph neural network is improved by capsule-based node feature extraction to improve the results of the graph neural network. The experiment is evaluated with CelebDF-FaceForencics++ (c23) datasets, which combines FaceForencies++ (c23) and Celeb-DF. In the end, it is proved that the accuracy of the proposed model has achieved 94.

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A Collaborative Alignment Framework of Transferable Knowledge Extraction for Unsupervised Domain Adaptation

Cited by 36 publications

References 44 publications

Deepfake detection using deep learning methods: A systematic and comprehensive review

Deepfake detection using deep learning methods: A systematic and comprehensive review

An efficient transfer learning based cross model classification (TLBCM) technique for the prediction of breast cancer

VIOLA jones algorithm with capsule graph network for deepfake detection

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