WildDeepfake: A Challenging Real-World Dataset for Deepfake Detection

By Bojia Zi, Minghao Chang, Jingjing Chen, Xingjun Ma, Yu-Gang Jiang. ACM Multimedia, 2020

577 citations recorded by the source corpus.

In recent years, the abuse of a face swap technique called deepfake has raised enormous public concerns. So far, a large number of deepfake videos (known as "deepfakes") have been crafted and uploaded to the internet, calling for effective countermeasures. One promising countermeasure against deepfakes is deepfake detection. Several deepfake datasets have been released to support the training and testing of deepfake detectors, such as DeepfakeDetection [1] and FaceForensics++ [23]. While this has greatly advanced deepfake detection, most of the real videos in these datasets are filmed with a few volunteer actors in limited scenes, and the fake videos are crafted by researchers using a few popular deepfake softwares. Detectors developed on these datasets may become less effective against real-world deepfakes on the internet. To better support detection against real-world deepfakes, in this paper, we introduce a new dataset WildDeepfake, which consists of 7,314 face sequences extracted from 707 deepfake videos collected completely from the internet. WildDeepfake is a small dataset that can be used, in addition to existing datasets, to develop and test the effectiveness of deepfake detectors against real-world deepfakes. We conduct a systematic evaluation of a set of baseline detection networks on both existing and our WildDeepfake datasets, and show that WildDeepfake is indeed a more challenging dataset, where the detection performance can decrease drastically. We also propose two (eg. 2D and 3D) Attention-based Deepfake Detection Networks (ADDNets) to leverage the attention masks on real/fake faces for improved detection. We empirically verify the effectiveness of ADDNets on both existing datasets and WildDeepfake. The dataset is available at: https://github.com/deepfakeinthewild/deepfake-in-the-wild.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/2060fa23185747294541f428c39640177450b8fb

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In Computer Vision and Pattern Recognition

Citations in the corpus

Deepfake detection using deep learning methods: A systematic and comprehensive review (2023, methodology): “Also, some approaches, such as the Hidden Markov Models (HMM) based speech synthesis system, are proposed to improve the range of voice characteristics or talking patterns (Zi et al., 2020).”
End-to-End Reconstruction-Classification Learning for Face Forgery Detection (2022, methodology): “Cross-dataset evaluation in terms of AUC (%) and EER (%) by training on WildDeepfake [17].”
A Continual Deepfake Detection Benchmark: Dataset, Methods, and Essentials (2022, methodology): “EASY: {GauGAN, BigGAN, CycleGAN, IMLE, FaceForensic++, CRN, WildDeepfake} 2.”
Learning Self-Consistency for Deepfake Detection (2020, unknown): “To support the research on detecting the other types of face manipulations, several deepfake datasets are released [57, 19, 41, 10, 28, 8, 15, 61] and countermeasures have been introduced.”
DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature (2023, background): “The problem of machine-generated text detection echoes earlier work on detecting deepfakes, artificial images or videos generated by deep nets, which has spawned substantial efforts in detection of fake visual content (Dolhansky et al., 2020; Zi et al., 2020).”
CoReD: Generalizing Fake Media Detection with Continual Representation using Distillation (2021, background): “Recently, several more advanced deepfake datasets (DFDC [17], CelebDF [45], CelebFOM [64], and WildDeepfake [80]) as well as GAN image datasets (CelebBlend [64], GANsformer [25], and StytleGAN2 [34]) are released.”