Deepfake: An Overview

  title={Deepfake: An Overview},
  author={Anupama Chadha and Vaibhav Kumar and Sonu Rani Kashyap and Mayank Gupta},
Recent advancements in digital technologies have significantly increased the quality and capability to produce realistic images and videos using highly advanced computer graphics and AI algorithms due to which it becomes difficult to distinguish between the real media and fake media. These computer-generated images or videos have useful applications in real life; however, these can also lead to various threats related to privacy and security. Deepfake is one of the ways which can lead to these… 
2 Citations

A Review of Modern Audio Deepfake Detection Methods: Challenges and Future Directions

A review of existing AD detection methods was conducted, along with a comparative description of the available faked audio datasets, in what is believed to be the first review targeting imitated and synthetically generated audio detection methods.



FaceForensics++: Learning to Detect Manipulated Facial Images

This paper proposes an automated benchmark for facial manipulation detection, and shows that the use of additional domain-specific knowledge improves forgery detection to unprecedented accuracy, even in the presence of strong compression, and clearly outperforms human observers.

A Counter-Forensic Method for CNN-Based Camera Model Identification

A counter-forensic method capable of subtly altering images to change their estimated camera model when they are analyzed by any CNN-based camera model detector, which shows that even advanced deep learning architectures trained to analyze images and obtain camera model information are still vulnerable to the proposed method.

Modular Convolutional Neural Network for Discriminating between Computer-Generated Images and Photographic Images

A modular CGI--PI discriminator with a customized VGG-19 network as the feature extractor, statistical convolutional neural networks as thefeature transformers, and a discriminator is built that outperformed a state-of-the-art method and achieved accuracy up to 100%.

Capsule-forensics: Using Capsule Networks to Detect Forged Images and Videos

  • H. NguyenJ. YamagishiI. Echizen
  • Computer Science
    ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
  • 2019
The method introduced in this paper uses a capsule network to detect various kinds of spoofs, from replay attacks using printed images or recorded videos to computer-generated videos using deep convolutional neural networks.

Face2Face: Real-Time Face Capture and Reenactment of RGB Videos

A novel approach for real-time facial reenactment of a monocular target video sequence (e.g., Youtube video) that addresses the under-constrained problem of facial identity recovery from monocular video by non-rigid model-based bundling and re-render the manipulated output video in a photo-realistic fashion.

Deepfake Video Detection Using Recurrent Neural Networks

  • David GueraE. Delp
  • Computer Science
    2018 15th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)
  • 2018
A temporal-aware pipeline to automatically detect deepfake videos is proposed that uses a convolutional neural network to extract frame-level features and a recurrent neural network that learns to classify if a video has been subject to manipulation or not.

Bringing portraits to life

A technique to automatically animate a still portrait, making it possible for the subject in the photo to come to life and express various emotions, and gives rise to reactive profiles, where people in still images can automatically interact with their viewers.

Digital image forensics: a booklet for beginners

This survey is designed for scholars and IT professionals approaching this field, reviewing existing tools and providing a view on the past, the present and the future of digital image forensics.

Conditional CycleGAN for Attribute Guided Face Image Generation

This work extends the cycleGAN to Conditional cycleGAN such that the mapping from X to Y is subjected to attribute condition Z, and uses face feature vector extracted from face verification network as Z to demonstrate the efficacy of this approach on identity preserving face image super-resolution.