## 1 Introduction  
With the development of deep learning, especially computer vision technology, digital image tampering and forgery technology have been extensively promoted and widely used in various fields, such as making pornographic videos, spreading fake news, and manipulating political events. Compared with traditional digital image forgery methods, existing technologies are mainly based on automatic encoders and generative adversarial networks (GAN) [1]. This technology using deep learning methods to generate high-quality forged images is called \"deepfake\" [2]. People can generate realistic fake face images using deepfake that are difficult to detect with traditional techniques. If deepfake is misused, it can potentially bring substantial risks to an individual's reputation, public opinion control, national security, and other aspects, resulting in serious social problems and political threats [3]. Therefore, exploring deepfake detection technology has high practical value and is a hot research direction in academic and industrial circles. Deepfake detection differs from the general image binary classification task in that deepfake datasets are continuously updated with the development of forgery methods. New forgery methods will solve the previously existing forgery problems, such as noticeable visual artifacts in the generated forged images, making the generated images or videos more realistic. Current forgery generation algorithms are divided into two categories. One replaces the face identity in generating the forged image, as shown in Figure 0(b), and the other replaces the person's expression, which does not change the person's identity information, as shown in Figure 0(c). Generating forged images and videos is mainly achieved using generative adversarial networks, explicitly using generators to generate fake data, while the discriminators classify the data and generate enough samples to be identified as fake through adversarial learning. In addition to generative adversarial networks, there are methods based on autoencoders or some face graphics. Regarding identifying forged images, different researchers have exploited forgery traces using the features of forged images, such as pixel features in the spatial domain, and have extracted frequency domain features or noise features using filters. Furthermore, convolutional neural networks have been fully utilized to achieve forgery detection by modifying the network structure. With the development of deepfake detection techniques, the detection accuracy on some current mainstream datasets has peaked. Therefore, more extensive, diverse, and challenging datasets must be built to better simulate the data in real-world application scenarios. In real-world scenarios, forged images may be compressed, have some noise added during transmission, or be cropped and transformed, which does not change the identity or expression information of the person and therefore does not affect the image's authenticity. However, these operations often have a certain degree of negative impact on the classification learning of detection models. The distribution of deepfake datasets in the practical setting is usually complex and unpredictable because some forged images may have been compressed and smoothed in the post-processing process or damaged during transmission. Moreover, some attackers add elaborate interference to the image to induce detection models to make a false judgment [4]. As a result, detection models tend to lose their performance and generally need better robustness when detecting naturally corrupted or artificially perturbed images. In deepfake detection, researchers need to pay more attention to the impact of hardness and diversity in the training data on the robustness of detection models. Inspired by the fact that data augmentation has been widely used in many fields to increase the diversity of training sets and improve model robustness [5; 6], this paper first examines the data augmentation dimension.