MesoNet: a Compact Facial Video Forgery Detection Network

  title={MesoNet: a Compact Facial Video Forgery Detection Network},
  author={Darius Afchar and Vincent Nozick and Junichi Yamagishi and Isao Echizen},
  journal={2018 IEEE International Workshop on Information Forensics and Security (WIFS)},
This paper presents a method to automatically and efficiently detect face tampering in videos, and particularly focuses on two recent techniques used to generate hyper-realistic forged videos: Deepfake and Face2Face. [] Key Result The tests demonstrate a very successful detection rate with more than 98% for Deepfake and 95% for Face2Face.

Face Forgery Detection Based on the Improved Siamese Network

This work proposes a face forgery detection method that consists of preprocessing, an improved Siamese network-based feature extractor (including a feature alignment module), and postprocessing (a voting principle).

Videoforensicshq: Detecting High-Quality Manipulated Face Videos

This paper examines how the performance of forgery detectors depends on the presence of artefacts that the human eye can see and introduces a new family of detectors that examine combinations of spatial and temporal features and outperform existing approaches both in terms of detection accuracy and generalization.

A Detect method for deepfake video based on full face recognition

  • Kai FengJan WuMin Tian
  • Computer Science
    2020 IEEE International Conference on Information Technology,Big Data and Artificial Intelligence (ICIBA)
  • 2020
A new method for tamper video detection based on the full faces is proposed and Facenet algorithm is used here to compare the similarity between real and fake video faces.

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.

Multi-Feature Fusion Based Deepfake Face Forgery Video Detection

A multi-feature fusion detection method is proposed to improve the generalization ability of the detector and effectively reduces the average error rate of span detection while ensuring good detection effect in the library.

Exposing DeepFake Videos Using Attention Based Convolutional LSTM Network

A novel attention-based deep fake video detection method, which captures the sharp changes in terms of the facial features caused by the composite video, and utilizes the convolutional long short-term memory to extract both spatial and temporal information of DeeFake videos.

Detecting Manipulated Facial Videos: A Time Series Solution

We propose a new method to expose fake videos based on a time series solution. The method is based on bidirectional long short-term memory (Bi-LSTM) backbone architecture with two different types of

Detecting DeepFake, FaceSwap and Face2Face facial forgeries using frequency CNN

A novel method that extracts faces from a target video and converts them into frequency domain using two dimensional global discrete Cosine transform (2D- GDCT) and employs a 3 layered frequency convolutional neural network to detect facial forgeries.

Preliminary Forensics Analysis of DeepFake Images

A preliminary idea on how to fight Deepfake images of faces will be presented by analysing anomalies in the frequency domain by using standard methods to identify fakeness in images.

Video Transformer for Deepfake Detection with Incremental Learning

The comprehensive experiments demonstrate that the proposed video transformer model with incremental learning achieves state-of-the-art performance in the deepfake video detection task with enhanced feature learning from the sequenced data.



FaceForensics: A Large-scale Video Dataset for Forgery Detection in Human Faces

A novel face manipulation dataset of about half a million edited images (from over 1000 videos) is introduced, which exceeds all existing video manipulation datasets by at least an order of magnitude and introduces benchmarks for classical image forensic tasks, including classification and segmentation.

A deep learning approach to detection of splicing and copy-move forgeries in images

  • Y. RaoJ. Ni
  • Computer Science
    2016 IEEE International Workshop on Information Forensics and Security (WIFS)
  • 2016
A new image forgery detection method based on deep learning technique, which utilizes a convolutional neural network to automatically learn hierarchical representations from the input RGB color images to outperforms some state-of-the-art methods.

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.

A Deep Learning Approach to Universal Image Manipulation Detection Using a New Convolutional Layer

A universal forensic approach to performing manipulation detection using deep learning that can automatically learn how to detect multiple image manipulations without relying on pre-selected features or any preprocessing is proposed.

An overview on video forensics

The paper aims at providing an overview of the existing video processing techniques, considering all the possible alterations that can be operated on a single signal and also the possibility of identifying the traces that could reveal important information about its origin and use.

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.

Automatic Face Reenactment

We propose an image-based, facial reenactment system that replaces the face of an actor in an existing target video with the face of a user from a source video, while preserving the original target

Exposing digital forgeries in video by detecting double MPEG compression

It is shown how a doublycompressed MPEG video sequence introduces specific static and temporal statistical perturbations whose presence can be used as evidence of tampering.

Screenshot identification using combing artifact from interlaced video

A screenshot identification scheme using unique characteristic of interlaced video, combing artifact that performs well under the widely used image formats as JPEG, BMP, TIFF and video formats as MPEG-2, MPEG-4, H.264.

A Survey of Image Forgery Detection

The state of the art in this new and exciting field of image forgery detection is reviewed, and several representative forensic tools within each of these categories are selected.