# SupCon-MPL-DP: Supervised Contrastive Learning with Meta Pseudo Labels for Deepfake Image Detection
## 1 Introduction  
Recently, with the advancement of generative artificial intelligence models [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15], deepfakes have become increasingly similar to real images/videos, making them difficult to distinguish. Deepfakes can be broadly categorized into three generations based on the evolution of generative models. First-generation deepfake generative models [1; 2; 3; 4; 5] typically attempt to generate simple and low-resolution images/videos based on probability distributions or synthesize multiple images/videos by exploiting features in tasks such as Face2 Face and FaceSwap. In particular, generative adversarial network (GAN)-based models such as CGAN [2], WGAN [4], and WGAN-GP [5], as well as autoencoder-based models like VAE [6] and conditional VAE [7] have enabled the generation of various deepfake images/videos. However, first-generation deepfakes often exhibit noticeable artifacts that can be discerned by the human eye. With the transition to second-generation deepfake generative models [6; 7; 8], there has been progress in generating high-resolution deepfakeimages that are more difficult to distinguish compared with first-generation ones, along with performance improvements in various tasks. In particular, second-generation deepfake generative models like StyleGAN, proposed by [PERSON] et al. [8], produce deepfake images that are difficult for the human eye to distinguish, excluding some flaws such as artifacts in hair. Second-generation deepfakes can be generated using deepfake generation tools such as DeepfaceLab [10], DeepSwap [11], Synthesisa [12], and others. Finally, third-generation deepfake generative models [9; 13; 14; 15] produce images/videos that are even more flexible and difficult to distinguish than those generated by second-generation models, across various tasks. In particular, Stable Diffusion, proposed by [PERSON] et al. [13], is currently being used for the generation of various human and artwork images, raising concerns related to copyright and human rights issues. Furthermore, the consistency model proposed by [PERSON] et al. [15] has enabled state-of-the-art deepfake generative model training at a lower cost by reducing the extensive iteration process required by previous diffusion models for restoring original images from noise. As deepfakes increasingly become difficult to distinguish from real images/videos, they are being utilized in various criminal activities. To address issues caused by deepfakes, methods have been proposed to identify flaws in landmarks that occur when deepfake generative models create images, aiding in the detection of deepfakes [16; 17; 18; 19]. Meanwhile, recent advancements in deepfake detection for single models have shown improvement in detecting deepfake videos and images. [PERSON] et al. [20] enhanced the detection performance of deepfake videos and images by combining EfficientNet [21] and Vision Transformer [22] when training a single model. In other words, existing deepfake detection models [16; 17; 18; 19; 20; 21; 22; 23] verify flaws in facial landmarks during the preprocessing stage and construct large models for flexible predictions. Previous studies have primarily focused on the detection performance of labeled known domain (known domain) tasks in deepfake detection. However, deepfake generation models are rapidly evolving, and similar generations of deepfake generation models are also being developed diversely. Therefore, detecting deepfake images in unknown domains (unknown domain) is also crucial. A few studies have proposed generalized deepfake detection models using techniques such as contrastive learning, meta learning, and others [24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36]. In this paper, we propose SupCon-MPL, a combination of the Meta Pseudo Labels (MPL) [37] with supervised contrastive learning (SupCon) [38] to further train the model with unlabeled images/videos, simultaneously enhancing the model's generalization ability to distinguish deepfakes in unknown domains.