MesoNet: a Compact Facial Video Forgery Detection Network

Afchar, Darius; Nozick, Vincent; Yamagishi, Junichi; Echizen, Isao

doi:10.1109/wifs.2018.8630761

Cited by 909 publications

(653 citation statements)

References 30 publications

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“…We evaluated various variants of our approach by using different state-of-the-art classification methods. We are considering learning-based methods used in the forensic community for generic manipulation detection [10,17], computer-generated vs natural image detection [51] and face tampering detection [5]. In addition, we show that the classification based on XceptionNet [14] outperforms all other variants in detecting fakes.…”

Section: Automatic Forgery Detection Methodsmentioning

confidence: 98%

“…Several other works explicitly refer to detecting manipulations related to faces, such as distinguishing computer generated faces from natural ones [22,15,51], morphed faces [50], face splicing [24,23], face swapping [66,38] and DeepFakes [5,44,33]. For face manipulation detection, some approaches exploit specific artifacts arising from the synthesis process, such as eye blinking [44], or color, texture and shape cues [24,23].…”

Section: Related Workmentioning

confidence: 99%

“…For face manipulation detection, some approaches exploit specific artifacts arising from the synthesis process, such as eye blinking [44], or color, texture and shape cues [24,23]. Other works are more general and propose a deep network trained to capture the subtle inconsistencies arising from low-level and/or high level features [50,66,38,5,33]. These approaches show impressive results, however robustness issues often remain unaddressed, although they are of paramount importance for practical applications.…”

Section: Related Workmentioning

confidence: 99%

“…(4) MesoInception-4 [5] is a CNN-based network inspired by InceptionNet [56] to detect face tampering in videos. The network has two inception modules and two classic convolution layers interlaced with max-pooling layers.…”

Section: Detection Based On Learned Featuresmentioning

confidence: 99%

See 3 more Smart Citations

FaceForensics++: Learning to Detect Manipulated Facial Images

Rössler

Cozzolino

Verdoliva

et al. 2019

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

1,447

1,573

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Figure 1: FaceForensics++ is a dataset of facial forgeries that enables researchers to train deep-learning-based approaches in a supervised fashion. The dataset contains manipulations created with four state-of-the-art methods, namely, Face2Face, FaceSwap, DeepFakes, and NeuralTextures. AbstractThe rapid progress in synthetic image generation and manipulation has now come to a point where it raises significant concerns for the implications towards society. At best, this leads to a loss of trust in digital content, but could potentially cause further harm by spreading false information or fake news. This paper examines the realism of state-ofthe-art image manipulations, and how difficult it is to detect them, either automatically or by humans.To standardize the evaluation of detection methods, we propose an automated benchmark for facial manipulation detection 1 . In particular, the benchmark is based on Deep-Fakes [1], Face2Face [59], FaceSwap [2] and NeuralTextures [57] as prominent representatives for facial manipulations at random compression level and size. The benchmark is publicly available 2 and contains a hidden test set as well as a database of over 1.8 million manipulated images. This dataset is over an order of magnitude larger than comparable, publicly available, forgery datasets. Based on this data, we performed a thorough analysis of data-driven forgery detectors. We show that the use of additional domainspecific knowledge improves forgery detection to unprecedented accuracy, even in the presence of strong compression, and clearly outperforms human observers.

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Section: Automatic Forgery Detection Methodsmentioning

confidence: 98%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Detection Based On Learned Featuresmentioning

confidence: 99%

See 2 more Smart Citations

FaceForensics++: Learning to Detect Manipulated Facial Images

Rössler

Cozzolino

Verdoliva

et al. 2019

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

1,447

1,573

View full text Add to dashboard Cite

show abstract

“…There are some related work proposed to detect AI generated fake images or videos using deep networks. To detect DeepFake video, different detection methods have been proposed [4,5,6,7,8]. In addition, some works focus on the detection of GAN generated images [9,10,11,12].…”

Section: Related Workmentioning

confidence: 99%

On the Generalization of GAN Image Forensics

Xuan

Peng

Wang

et al. 2019

Biometric Recognition

131

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Recently GAN generated face images are more and more realistic with high-quality, even hard for human eyes to detect. On the other hand, the forensics community keeps on developing methods to detect these generated fake images and try to ensure the credibility of visual contents. Although researchers have developed some methods to detect generated images, few of them explore the important problem of generalization ability of forensics model. As new types of GANs are emerging fast, the generalization ability of forensics models to detect new types of GAN images is absolutely an essential research topic, which is also very challenging. In this paper, we explore this problem and propose to use preprocessed images to train a forensic CNN model. By applying similar image level preprocessing to both real and fake images, unstable low level noise cues are destroyed, and the forensics model is forced to learn more intrinsic features to classify the generated and real face images. Our experimental results also prove the effectiveness of the proposed method.

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A lightweight 3D convolutional neural network for deepfake detection

Liu

Zhu

et al. 2021

Int J of Intelligent Sys

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The rapid development of DeepFake technologies has brought great challenges to the authenticity of video contents. It is of vital importance to develop DeepFake detection methods, among which three‐dimensional (3D) convolution neural networks (CNN) have attracted wide interest and achieved satisfying performances. However, there are few 3D CNNs designed for DeepFake detection and the parameters of them are large, which cause heavy memory and storage consumption. In this paper, a lightweight 3D CNN is proposed for DeepFake detection. Channel transformation module is designed to extract features with much fewer parameters in higher level. Serving as spatial‐temporal module, 3D CNNs are adopted to fuse the spatial features in time dimension. To suppress frame content and highlight frame texture, spatial rich model features are extracted from the input frames, which helps the spatial‐temporal module achieve better performance. Experimental results show that the number of parameters of the proposed network is much less than those of other networks and the proposed network outperforms other state‐of‐the‐art DeepFake detection methods on mainstream DeepFake data sets.

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MesoNet: a Compact Facial Video Forgery Detection Network

Cited by 909 publications

References 30 publications

FaceForensics++: Learning to Detect Manipulated Facial Images

FaceForensics++: Learning to Detect Manipulated Facial Images

On the Generalization of GAN Image Forensics

A lightweight 3D convolutional neural network for deepfake detection

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