FDFtNet: Facing Off Fake Images Using Fake Detection Fine-Tuning Network

Jeon, Hyeonseong; Bang, Youngoh; Woo, Simon S.

doi:10.1007/978-3-030-58201-2_28

Cited by 43 publications

(24 citation statements)

References 20 publications

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“…Finally, according to the experimental results, landmark-based face alignment with bidirectional recurrent DenseNet performs the best for detecting face manipulation in videos. Jeon et al [73] introduced an FDFtNet method to improve the capability of existing CNN models, such as SqueezeNet, ShallowNetV3, ResNetV2, and Xception. In this method, the fine-tuning method is used to extract the features using MBblockV3, and the method can be called fine-tuning transformation.…”

Section: ) Dnn-based Techniques For Deepfakesmentioning

confidence: 99%

DeepFake Detection for Human Face Images and Videos: A Survey

et al. 2022

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Techniques for creating and manipulating multimedia information have progressed to the point where they can now ensure a high degree of realism. DeepFake is a generative deep learning algorithm that creates or modifies face features in a superrealistic form, in which it is difficult to distinguish between real and fake features. This technology has greatly advanced and promotes a wide range of applications in TV channels, video game industries, and cinema, such as improving visual effects in movies, as well as a variety of criminal activities, such as misinformation generation by mimicking famous people. To identify and classify DeepFakes, research in DeepFake detection using deep neural networks (DNNs) has attracted increased interest. Basically, DeepFake is the regenerated media that is obtained by injecting or replacing some information within the DNN model. In this survey, we will summarize the DeepFake detection methods in face images and videos on the basis of their results, performance, methodology used and detection type. We will review the existing types of DeepFake creation techniques and sort them into five major categories. Generally, DeepFake models are trained on DeepFake datasets and tested with experiments. Moreover, we will summarize the available DeepFake dataset trends, focusing on their improvements. Additionally, the issue of how DeepFake detection aims to generate a generalized DeepFake detection model will be analyzed. Finally, the challenges related to DeepFake creation and detection will be discussed. We hope that the knowledge encompassed in this survey will accelerate the use of deep learning in face image and video DeepFake detection methods.

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Section: ) Dnn-based Techniques For Deepfakesmentioning

confidence: 99%

DeepFake Detection for Human Face Images and Videos: A Survey

et al. 2022

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“…For this purpose, a form of context aggregation network is put forward. Jeon et al [18] have prioritized the computational cost of deepfake detection over its accuracy by proposing a lightweight neural network architecture that can utilize pre-existing and pretrained classifier models. Zhang et al [19] has introduced a unique approach to tackling the deepfake problem by analyzing the difference in image compression ratios of the multiple video frames or images that are blend together through error level analysis.…”

Section: -2-existing Deepfake Detection Algorithmsmentioning

confidence: 99%

DeepFake Detection using 3D-Xception Net with Discrete Fourier Transformation

Biswas¹,

Bhattacharya²,

Kakelli³

2021

JIST

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The videos are more popular for sharing content on social media to capture the audience's attention. The artificial manipulation of videos is growing rapidly to make the videos flashy and interesting but they can easily misuse to spread false information on social media platforms. Deep Fake is a problematic method for the manipulation of videos in which artificial components are added to the video using emerging deep learning techniques. Due to the increase in the accuracy of deep fake generation methods, artificially created videos are no longer detectable and pose a major threat to social media users. To address this growing problem, we have proposed a new method for detecting deep fake videos using 3D Inflated Xception Net with Discrete Fourier Transformation. Xception Net was originally designed for application on 2D images only. The proposed method is the first attempt to use a 3D Xception Net for categorizing video-based data. The advantage of the proposed method is, it works on the whole video rather than the subset of frames while categorizing. Our proposed model was tested on the popular dataset Celeb-DF and achieved better accuracy.

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“…文献 [75] 提出一种伪造人脸图像检测算法 Fakespotter, 通过提取神经网络模型中每一层神经元的激活状态作为伪造检测的特征. Jeon 等 [90] 在现有预训练模型基础上, 设计了一种参数微调网络, 可以很好地与现有图像分类网络模型进行结合. Nguyen 等 [76] 将胶囊网络结构引入到伪造检测任务中.…”

Section: 人脸编辑unclassified

Deep visual identity forgery and detection

彭春蕾¹,

高新波²,

王楠楠³

et al. 2021

Sci. Sin.-Inf.

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With the wide application of deep learning techniques in video and image generation, the quality of visual identity forgery, especially face forgery, is becoming increasingly high. The detection of visual identity forgery has been a hot issue because of its important influence on both national security and social stability. In this paper, we introduce recent researches on deep visual identity forgery from target-specific face forgery and target-generic face forgery. We further summarize the techniques of detecting visual identity forgery from multiple categories, including the spatial clue based methods, the temporal clue based method, the techniques for generalizable forgery detection and spoofing forgery detection models. We later present the public datasets and performance of representative approaches on these datasets. Finally, the issues and challenges in existing research are discussed.

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FDFtNet: Facing Off Fake Images Using Fake Detection Fine-Tuning Network

Cited by 43 publications

References 20 publications

DeepFake Detection for Human Face Images and Videos: A Survey

DeepFake Detection for Human Face Images and Videos: A Survey

DeepFake Detection using 3D-Xception Net with Discrete Fourier Transformation

Deep visual identity forgery and detection

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