Humans are easily fooled by digital images

Schetinger, Victor; Oliveira, Manuel M.; Silva, Roberto da; Carvalho, Tiago

doi:10.1016/j.cag.2017.08.010

Cited by 48 publications

(40 citation statements)

References 21 publications

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“…Indeed, microscopic analyses based on image noise cannot be applied in a compressed video context where the image noise is strongly degraded. Similarly, at a higher semantic level, human eye struggles to distinguish forged images [21], especially when the image depicts a human face [1,7]. That is why we propose to adopt an intermediate approach using a deep neural network with a small number of layers.…”

Section: Proposed Methodsmentioning

confidence: 99%

MesoNet: a Compact Facial Video Forgery Detection Network

Afchar

Nozick

Yamagishi

et al. 2018

2018 IEEE International Workshop on Information Forensics and Security (WIFS)

1,019

665

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This paper presents a method to automatically and efficiently detect face tampering in videos, and particularly focuses on two recent techniques used to generate hyperrealistic forged videos: Deepfake and Face2Face. Traditional image forensics techniques are usually not well suited to videos due to the compression that strongly degrades the data. Thus, this paper follows a deep learning approach and presents two networks, both with a low number of layers to focus on the mesoscopic properties of images. We evaluate those fast networks on both an existing dataset and a dataset we have constituted from online videos. The tests demonstrate a very successful detection rate with more than 98% for Deepfake and 95% for Face2Face.

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Section: Proposed Methodsmentioning

confidence: 99%

MesoNet: a Compact Facial Video Forgery Detection Network

Afchar

Nozick

Yamagishi

et al. 2018

2018 IEEE International Workshop on Information Forensics and Security (WIFS)

1,019

665

View full text Add to dashboard Cite

show abstract

“…Some recent advances in image processing, like the realtime facial reenactment face2face [2], show the importance of having some tools to distinguish computer graphics (CG) from natural photographic images (PG). Although the distinction between CG and PG depends not only on image properties, but also on cognitive characteristics of viewers [3], people show inaccurate skills at differentiating between altered and non-altered images [4].…”

Section: A Computer Graphics Vs Natural Photographsmentioning

confidence: 99%

Distinguishing computer graphics from natural images using convolution neural networks

Rahmouni

Nozick²,

Yamagishi

et al. 2017

2017 IEEE Workshop on Information Forensics and Security (WIFS)

253

188

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This paper presents a deep-learning method for distinguishing computer generated graphics from real photographic images. The proposed method uses a Convolutional Neural Network (CNN) with a custom pooling layer to optimize current best-performing algorithms feature extraction scheme. Local estimates of class probabilities are computed and aggregated to predict the label of the whole picture. We evaluate our work on recent photo-realistic computer graphics and show that it outperforms state of the art methods for both local and full image classification.

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“…Prior to this technology, it was generally believed that videos were reliable, even working as video evidence in multimedia forensics. However, the prevalence of DeepFake has made people realize that, in addition to short message service (SMS) and telephone fraud, people can even be defrauded by videos [4]. DeepFake technology has posed a challenge to the public trust level in the information age.…”

Section: Introductionmentioning

confidence: 99%

A Novel Counterfeit Feature Extraction Technique for Exposing Face-Swap Images Based on Deep Learning and Error Level Analysis

Zhang

Zhao

2020

Entropy

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The quality and efficiency of generating face-swap images have been markedly strengthened by deep learning. For instance, the face-swap manipulations by DeepFake are so real that it is tricky to distinguish authenticity through automatic or manual detection. To augment the efficiency of distinguishing face-swap images generated by DeepFake from real facial ones, a novel counterfeit feature extraction technique was developed based on deep learning and error level analysis (ELA). It is related to entropy and information theory such as cross-entropy loss function in the final softmax layer. The DeepFake algorithm is only able to generate limited resolutions. Therefore, this algorithm results in two different image compression ratios between the fake face area as the foreground and the original area as the background, which would leave distinctive counterfeit traces. Through the ELA method, we can detect whether there are different image compression ratios. Convolution neural network (CNN), one of the representative technologies of deep learning, can extract the counterfeit feature and detect whether images are fake. Experiments show that the training efficiency of the CNN model can be significantly improved by the ELA method. In addition, the proposed technique can accurately extract the counterfeit feature, and therefore achieves outperformance in simplicity and efficiency compared with direct detection methods. Specifically, without loss of accuracy, the amount of computation can be significantly reduced (where the required floating-point computing power is reduced by more than 90%).

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Humans are easily fooled by digital images

Cited by 48 publications

References 21 publications

MesoNet: a Compact Facial Video Forgery Detection Network

MesoNet: a Compact Facial Video Forgery Detection Network

Distinguishing computer graphics from natural images using convolution neural networks

A Novel Counterfeit Feature Extraction Technique for Exposing Face-Swap Images Based on Deep Learning and Error Level Analysis

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