deepfake detection ppt

Wang, X., Thome, N., and Cord, M. (2017).

deep learning-powered applications recently emerged is "deepfake". Densely connected convolutional networks. and creates an improved composite classifier. https://www. In this paper, we propose a new method for the problem of digital camera identification from its images based on the sensor's pattern noise. The videos are turned into a series of frames as PNGs with the use of the software ’FFmpeg’ [FFmpeg De-. On the generalization of GAN image forensics. We trained an EfficientNet model to detect deepfake images. Task-oriented GAN for PolSAR image classification and clustering. StackGAN++: Realistic image synthesis with stacked generative adversarial networks. A voice deepfake was used to scam a CEO out of $243,000. Sometimes deepfakes do not need to be spread to massive audience to cause detrimental effects. Deepfake Video Forensics based on Transfer Learning, Using Deep Learning to Solve Computer Security Challenges: A Survey, Deep Learning Advances on Different 3D Data Representations: A Survey, The Creation and Detection of Deepfakes: A Survey, AMP: Authentication of Media via Provenance, Use of a Capsule Network to Detect Fake Images and Videos,,,,,,,,,,, Learn more. Deepfake video detection using recurrent neural networks. Deepfakes have begun to erode trust of people in media contents as seeing them is no longer commensurate with believing in them. Deeplearning has been used to solve complex problems in various domains.... GitHub is home to over 50 million developers working together to host and review code, manage projects, and build software together.

© 2008-2020 ResearchGate GmbH. Deepfakes’ quality has been increasing and the performance of detection methods needs to be improved accordingly.

Unsupervised speech representation learning using wavenet autoencoders.

Check out this repo to know more about deepfake …

The videos are converted into frames, which are cropped to the questioned facial region. ∙ Two networks use the same encoder but different decoders for training process (top). H64, H128, LIAEF128, SAE, - Support multiple face extraction modes, e.g. a) The average variation in correlation scores per authentic and per Deepfake video. (2019, September 3). If nothing happens, download the GitHub extension for Visual Studio and try again. These features are then distributed into either a deep or shallow classifier to differentiate between fake and authentic videos. The process of creating those manipulated images and videos is also much simpler today as it needs as little as an identity photo or a short video of a target individual.

In, Zhou, P., Han, X., Morariu, V. I., and Davis, L. S. (2017, July). Aside from innovative The DeepfakeTIMIT data set [42] includes a set of low quality videos of 64 x 64 size and another set of high quality videos of 128 x 128 with totally 10537 pristine images and 34,023 fabricated images extracted from 320 videos for each quality set. The blinking rate is calculated based on the prediction results where a blink is defined as a peak above the threshold of 0.5 with duration less than 7 frames. Media Forensics (MediFor). Thies, J., Zollhofer, M., Stamminger, M., Theobalt, C., and Nießner, M. (2016). The first data set, namely UADFV, consists of 49 deep fake videos and their respective real videos. port was assumed to be of infinite size. On the other hand, the use of a physiological signal, eye blinking, to detect deepfakes was proposed in [78] based on the observation that a person in deepfakes has a lot less frequent blinking than that in untampered videos. The proposed method yields the best performance compared to its competing methods in all of these data sets. A fast forgery detection algorithm based on exponential-Fourier moments for video region duplication. However, the number of malicious uses of deepfakes largely dominates that of the positive ones.

However, it is also one of the techniques that cyber attackers employ to penetrate identification or authentication systems to gain illegitimate access. Segnet: A deep convolutional encoder-decoder architecture for image segmentation. The recent development of capsule network based on dynamic routing algorithm [87] demonstrates its ability to describe the hierarchical pose relationships between object parts. A new video manipulation technique known as Deepfake has established itself online o, second actor, provided that enough images (se, videos are known as ’Deepfakes’. The Deepfake algorithm allows a user to switch the face of one actor in a video with the face of a different actor in a photorealistic manner. On the other hand, Agarwal and Varshney [54] cast the GAN-based deepfake detection as a hypothesis testing problem where a statistical framework was introduced using the information-theoretic study of authentication [55]. (2014, October). Video manipulation is carried out on a frame-by-frame basis so that low level artifacts produced by face manipulations are believed to further manifest themselves as temporal artifacts with inconsistencies across frames. Yang et al. For example, a voice deepfake was used to scam a CEO out of $243,000 [15]. who distributed it and what they said about it [102]. This aspect needs to take into account in courtrooms nowadays when images and videos are used as evidences to convict perpetrators because of the existence of a wide range of digital manipulation methods [103].

The primary goal of this survey is to study and analyze the existing passive video forgery detection techniques. Retrieved from. ∙ Deep learning has been successfully applied to solve various complex problems Deep learning advances however have also been employed to create software that and (iii) Which are the mechanisms that are currently being used to fight against disinformation?. (2018). Arjovsky, M., Chintala, S., and Bottou, L. (2017, July). is used to reconstruct face.

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