Deep Detection for Face Manipulation

  title={Deep Detection for Face Manipulation},
  author={Di Feng and Xuequan Lu and Xufeng Lin},
It has become increasingly challenging to distinguish real faces from their visually realistic fake counterparts, due to the great advances of deep learning based face manipulation techniques in recent years. In this paper, we introduce a deep learning method to detect face manipulation. It consists of two stages: feature extraction and binary classification. To better distinguish fake faces from real faces, we resort to the triplet loss function in the first stage. We then design a simple… Expand
4 Citations

Figures and Tables from this paper

Optical Flow based CNN for detection of unlearnt deepfake manipulations
A new forensic technique able to detect fake and original video sequences is proposed; it is based on the use of CNNs trained to distinguish possible motion dissimilarities in the temporal structure of a video sequence by exploiting optical flow fields. Expand
DeepFakes: Detecting Forged and Synthetic Media Content Using Machine Learning
The rapid advancement in deep learning makes the differentiation of authentic and manipulated facial images and video clips unprecedentedly harder. The underlying technology of manipulating facialExpand
DeepfakeUCL: Deepfake Detection via Unsupervised Contrastive Learning
Extensive experiments show that the novel deepfake detection method via unsupervised contrastive learning enables comparable detection performance to state-of-the-art supervised techniques, in both the intraand inter-dataset settings. Expand
Countering Malicious DeepFakes: Survey, Battleground, and Horizon
A comprehensive overview and detailed analysis of the research work on the topic of DeepFake generation, DeepFake detection as well as evasion of Deepfake detection, with more than 191 research papers carefully surveyed is provided. Expand


Exposing Deep Fakes Using Inconsistent Head Poses
  • X. Yang, Yuezun Li, Siwei Lyu
  • Computer Science
  • ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
  • 2019
This paper proposes a new method to expose AI-generated fake face images or videos based on the observations that Deep Fakes are created by splicing synthesized face region into the original image, and in doing so, introducing errors that can be revealed when 3D head poses are estimated from the face images. Expand
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. Expand
Exposing DeepFake Videos By Detecting Face Warping Artifacts
A new deep learning based method that can effectively distinguish AI-generated fake videos from real videos is described, which saves a plenty of time and resources in training data collection and is more robust compared to others. Expand
Exploiting Visual Artifacts to Expose Deepfakes and Face Manipulations
It is shown that relatively simple visual artifacts can be already quite effective in exposing such manipulations, including Deepfakes and Face2Face. Expand
Inverting face embeddings with convolutional neural networks
This work uses neural networks to effectively invert low-dimensional face embeddings while producing realistically looking consistent images and demonstrates that a gradient ascent style approaches can be used to reproduce consistent images, with a help of a guiding image. Expand
MesoNet: a Compact Facial Video Forgery Detection Network
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. Expand
Two-Stream Neural Networks for Tampered Face Detection
This work trains GoogLeNet to detect tampering artifacts in a face classification stream, and train a patch based triplet network to leverage features capturing local noise residuals and camera characteristics as a second stream for face tampering detection. Expand
Supplemental Material for ” Face 2 Face : Real-time Face Capture and Reenactment of RGB Videos ”
In this document, we provide supplementary information to the method by Thies et al. [4]. More specifically, we include additional detail about our optimization framework (see Section 1 and 2), andExpand
In Ictu Oculi: Exposing AI Created Fake Videos by Detecting Eye Blinking
This work describes a new method to expose fake face videos generated with deep neural network models based on detection of eye blinking in the videos, which is a physiological signal that is not well presented in the synthesized fake videos. Expand
Going deeper with convolutions
We propose a deep convolutional neural network architecture codenamed Inception that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual RecognitionExpand