Visible to the public OC-FakeDect: Classifying Deepfakes Using One-class Variational Autoencoder

TitleOC-FakeDect: Classifying Deepfakes Using One-class Variational Autoencoder
Publication TypeConference Paper
Year of Publication2020
AuthorsKhalid, H., Woo, S. S.
Conference Name2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Keywordsanomaly detection, Benchmark testing, binary-classification based detection, binary-classification based methods, class-based approach, computer-generated image, data privacy, data scarcity limitations, DeepFake, deepfakes data, deepfakes detection, deepfakes generation methods, detecting Deepfakes, detection performance, detects nonreal images, Face, face recognition, fake face images, feature extraction, Forensics, Human Behavior, human factors, image classification, image forgery method, Image reconstruction, learning (artificial intelligence), Metrics, object detection, OC-FakeDect, one-class anomaly detection problem, one-class variational autoencoder, pattern clustering, person, privacy issues, pubcrawl, resilience, Resiliency, Scalability, Streaming media, sufficient fake images data, Support vector machines, Training
AbstractAn image forgery method called Deepfakes can cause security and privacy issues by changing the identity of a person in a photo through the replacement of his/her face with a computer-generated image or another person's face. Therefore, a new challenge of detecting Deepfakes arises to protect individuals from potential misuses. Many researchers have proposed various binary-classification based detection approaches to detect deepfakes. However, binary-classification based methods generally require a large amount of both real and fake face images for training, and it is challenging to collect sufficient fake images data in advance. Besides, when new deepfakes generation methods are introduced, little deepfakes data will be available, and the detection performance may be mediocre. To overcome these data scarcity limitations, we formulate deepfakes detection as a one-class anomaly detection problem. We propose OC-FakeDect, which uses a one-class Variational Autoencoder (VAE) to train only on real face images and detects non-real images such as deepfakes by treating them as anomalies. Our preliminary result shows that our one class-based approach can be promising when detecting Deepfakes, achieving a 97.5% accuracy on the NeuralTextures data of the well-known FaceForensics++ benchmark dataset without using any fake images for the training process.
Citation Keykhalid_oc-fakedect_2020