On the Detection of Digital Face Manipulation

Dang, Hao; Liu, Feng; Stehouwer, Joel; Liu, Xiaoming; Jain, Anil K.

doi:10.1109/cvpr42600.2020.00582

Cited by 439 publications

(258 citation statements)

References 36 publications

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“…Recently, another dataset called Diverse Fake Face Dataset (DFFD) was introduced by Dang et al [51]. DFFD contains 100,000 and 200,000 fake images generated by adopting respective state-of-the-art methods (ProGAN and StyleGAN models).…”

Section: Fake Face Dataset (Dffd)mentioning

confidence: 99%

Deepfakes Detection Techniques Using Deep Learning: A Survey

Almars¹

2021

JCC

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Deep learning is an effective and useful technique that has been widely applied in a variety of fields, including computer vision, machine vision, and natural language processing. Deepfakes uses deep learning technology to manipulate images and videos of a person that humans cannot differentiate them from the real one. In recent years, many studies have been conducted to understand how deepfakes work and many approaches based on deep learning have been introduced to detect deepfakes videos or images. In this paper, we conduct a comprehensive review of deepfakes creation and detection technologies using deep learning approaches. In addition, we give a thorough analysis of various technologies and their application in deepfakes detection. Our study will be beneficial for researchers in this field as it will cover the recent state-of-art methods that discover deepfakes videos or images in social contents. In addition, it will help comparison with the existing works because of the detailed description of the latest methods and dataset used in this domain.

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Section: Fake Face Dataset (Dffd)mentioning

confidence: 99%

Deepfakes Detection Techniques Using Deep Learning: A Survey

Almars¹

2021

JCC

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“…Moreover, Dang et al [35] evaluated the inconsistent 3D head pose on Celeb-DF (a new deepfake dataset that was created by the improved deepfake algorithm [36]) and only obtained an AUROC of 0.548. Almost all of the existing methods are general for various deepfake videos, but Agarwal et al [24] created an SVM classifier that was specialized for world leaders, which uses biometric patterns, including facial action units (AU) and head movements.…”

Section: Literature Reviewmentioning

confidence: 99%

Fighting Deepfakes Using Body Language Analysis

2021

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Recent improvements in deepfake creation have made deepfake videos more realistic. Moreover, open-source software has made deepfake creation more accessible, which reduces the barrier to entry for deepfake creation. This could pose a threat to the people’s privacy. There is a potential danger if the deepfake creation techniques are used by people with an ulterior motive to produce deepfake videos of world leaders to disrupt the order of countries and the world. Therefore, research into the automatic detection of deepfaked media is essential for public security. In this work, we propose a deepfake detection method using upper body language analysis. Specifically, a many-to-one LSTM network was designed and trained as a classification model for deepfake detection. Different models were trained by varying the hyperparameters to build a final model with benchmark accuracy. We achieved 94.39% accuracy on the deepfake test set. The experimental results showed that upper body language can effectively detect deepfakes.

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“…They are not well equipped to detect nowaday diverse types of fake images. Scholars try to tackle diverse types of fake face images with multifarious ideas in recent studies [21,22,23,24,25,26,27]. For instance, [21] proposes an auto-encoder-based model to detect manipulated face images.…”

Section: Introductionmentioning

confidence: 99%

“…For instance, [21] proposes an auto-encoder-based model to detect manipulated face images. [23] puts forward an attention-based CNN to locate manipulation regions in fake images. [24,25] use dynamic routing algorithm to choose features extracted by several Capsule Networks.…”

Section: Introductionmentioning

confidence: 99%

Attentive Semantic Exploring for Manipulated Face Detection

Chen

Yang

2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Face manipulation methods develop rapidly in recent years, whose potential risk to society accounts for the emerging of researches on detection methods. However, due to the diversity of manipulation methods and the high quality of fake images, detection methods suffer from a lack of generalization ability. To solve the problem, we find that segmenting images into semantic fragments could be effective, as discriminative defects and distortions are closely related to such fragments. Besides, to highlight discriminative regions in fragments and to measure contribution to the final prediction of each fragment is efficient for the improvement of generalization ability. Therefore, we propose a novel manipulated face detection method based on Multilevel Facial Semantic Segmentation and Cascade Attention Mechanism. To evaluate our method, we reconstruct two datasets: GGFI and FFMI, and also collect two open-source datasets. Experiments on four datasets verify the advantages of our approach against other state-of-the-arts, especially its generalization ability.

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On the Detection of Digital Face Manipulation

Cited by 439 publications

References 36 publications

Deepfakes Detection Techniques Using Deep Learning: A Survey

Deepfakes Detection Techniques Using Deep Learning: A Survey

Fighting Deepfakes Using Body Language Analysis

Attentive Semantic Exploring for Manipulated Face Detection

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