Self-supervised Learning of Adversarial Example: Towards Good Generalizations for Deepfake Detection

By Yong Zhang, Yibing Song, Lingqiao Liu, Jue Wang. Computer Vision and Pattern Recognition, 2022

325 citations recorded by the source corpus.

Recent studies in deepfake detection have yielded promising results when the training and testing face forgeries are from the same dataset. However, the problem remains challenging when one tries to generalize the detector to forgeries created by unseen methods in the training dataset. This work addresses the generalizable deepfake detection from a simple principle: a generalizable representation should be sensitive to diverse types of forgeries. Following this principle, we propose to enrich the “diversity” of forgeries by synthesizing augmented forgeries with a pool of forgery configurations and strengthen the “sensitivity” to the forgeries by enforcing the model to predict the forgery configurations. To effectively explore the large forgery augmentation space, we further propose to use the adversarial training strategy to dynamically synthesize the most challenging forgeries to the current model. Through extensive experiments, we show that the proposed strategies are surprisingly effective (see Figure 1), and they could achieve superior performance than the current state-of-the-art methods. Code is available at https://github.com/liangchen527/SLADD.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/750a5127db2375e6e5a1d022e66f25fb2982fcff

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Yanbo Fan
2. Yong Zhang
3. Baoyuan Wu
4. Zhiyuan Yan
5. Chuangchuang Tan
6. Guanghua Gu
7. Shikui Wei

In Computer Vision and Pattern Recognition

1. Baoyuan Wu
2. Zhiyuan Yan
3. Chuangchuang Tan
4. Yanbo Fan
5. Yong Zhang
6. Guanghua Gu
7. Shikui Wei

Citations in the corpus

• UCF: Uncovering Common Features for Generalizable Deepfake Detection (2023, methodology): “Following previous works [23, 7], the HQ version of FF++ is adopted by default.”
• Frequency-Aware Deepfake Detection: Improving Generalizability through Frequency Space Domain Learning (2024, methodology): “Furthermore, various methods (Wang et al. 2020, 2021; Chen et al. 2022; Cao et al. 2022; He et al. 2021; Shiohara et al. 2022)”