Countering Spoof: Towards Detecting Deepfake with Multidimensional Biological Signals

Jin, Xinlei; Ye, Dengpan; Chen, Chuanxi

doi:10.1155/2021/6626974

Cited by 9 publications

(2 citation statements)

References 21 publications

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“…Intuitively, computer experts have designed algorithms that can measure biological signals using features from the video data such as changes in color, motion, subtle head movements, etc. [82].…”

Section: Deepfake Video Detection Using Biological Signalsmentioning

confidence: 99%

A Review of Image Processing Techniques for Deepfakes

Shahzad

Rustam

Flores

et al. 2022

Sensors

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Deep learning is used to address a wide range of challenging issues including large data analysis, image processing, object detection, and autonomous control. In the same way, deep learning techniques are also used to develop software and techniques that pose a danger to privacy, democracy, and national security. Fake content in the form of images and videos using digital manipulation with artificial intelligence (AI) approaches has become widespread during the past few years. Deepfakes, in the form of audio, images, and videos, have become a major concern during the past few years. Complemented by artificial intelligence, deepfakes swap the face of one person with the other and generate hyper-realistic videos. Accompanying the speed of social media, deepfakes can immediately reach millions of people and can be very dangerous to make fake news, hoaxes, and fraud. Besides the well-known movie stars, politicians have been victims of deepfakes in the past, especially US presidents Barak Obama and Donald Trump, however, the public at large can be the target of deepfakes. To overcome the challenge of deepfake identification and mitigate its impact, large efforts have been carried out to devise novel methods to detect face manipulation. This study also discusses how to counter the threats from deepfake technology and alleviate its impact. The outcomes recommend that despite a serious threat to society, business, and political institutions, they can be combated through appropriate policies, regulation, individual actions, training, and education. In addition, the evolution of technology is desired for deepfake identification, content authentication, and deepfake prevention. Different studies have performed deepfake detection using machine learning and deep learning techniques such as support vector machine, random forest, multilayer perceptron, k-nearest neighbors, convolutional neural networks with and without long short-term memory, and other similar models. This study aims to highlight the recent research in deepfake images and video detection, such as deepfake creation, various detection algorithms on self-made datasets, and existing benchmark datasets.

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Section: Deepfake Video Detection Using Biological Signalsmentioning

confidence: 99%

A Review of Image Processing Techniques for Deepfakes

Shahzad

Rustam

Flores

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…Although detection methods based on physiological signal characteristics can effectively make use of the defects of deepfake techniques, these methods gradually become invalid with the continuous improvement of generation methods, such as the addition of physiological characteristics (e.g., blink frequency). Besides, methods based on hard-to-find biological signals, such as heart rate, would be far less accurate due to video compression and other processing [39].…”

Section: Related Workmentioning

confidence: 99%

Multisemantic Path Neural Network for Deepfake Detection

Jin

Jiang

et al. 2022

Security and Communication Networks

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With the continuous development of deep learning techniques, it is now easy for anyone to swap faces in videos. Researchers find that the abuse of these techniques threatens cyberspace security; thus, face forgery detection is a popular research topic. However, current detection methods do not fully use the semantic features of deepfake videos. Most previous work has only divided the semantic features, the importance of which may be unequal, by experimental experience. To solve this problem, we propose a new framework, which is the multisemantic pathway network (MSPNN) for fake face detection. This method comprehensively captures forged information from the dimensions of microscopic, mesoscopic, and macroscopic features. These three kinds of semantic information are given learnable weights. The artifacts of deepfake images are more difficult to observe in a compressed video. Therefore, preprocessing is proposed to detect low-quality deepfake videos, including multiscale detail enhancement and channel information screening based on the compression principle. Center loss and cross-entropy loss are combined to further reduce intraclass spacing. Experimental results show that MSPNN is superior to contrast methods, especially low-quality deepfake video detection.

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Deepfakes: evolution and trends

et al. 2023

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This study conducts research on deepfakes technology evolution and trends based on a bibliometric analysis of the articles published on this topic along with six research questions: What are the main research areas of the articles in deepfakes? What are the main current topics in deepfakes research and how are they related? Which are the trends in deepfakes research? How do topics in deepfakes research change over time? Who is researching deepfakes? Who is funding deepfakes research? We have found a total of 331 research articles about deepfakes in an analysis carried out on the Web of Science and Scopus databases. This data serves to provide a complete overview of deepfakes. Main insights include: different areas in which deepfakes research is being performed; which areas are the emerging ones, those that are considered basic, and those that currently have the most potential for development; most studied topics on deepfakes research, including the different artificial intelligence methods applied; emerging and niche topics; relationships among the most prominent researchers; the countries where deepfakes research is performed; main funding institutions. This paper identifies the current trends and opportunities in deepfakes research for practitioners and researchers who want to get into this topic.

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Countering Spoof: Towards Detecting Deepfake with Multidimensional Biological Signals

Cited by 9 publications

References 21 publications

A Review of Image Processing Techniques for Deepfakes

A Review of Image Processing Techniques for Deepfakes

Multisemantic Path Neural Network for Deepfake Detection

Deepfakes: evolution and trends

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